# Knowledge Graph Technologies: the Next Frontier of the Food, Agriculture, and Water Domains

Marie-Angélique Laporte, Catherine Rousseau, Christophe Guéret

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Marie-Angélique Laporte, Catherine Rousseau, Christophe Guéret. Knowledge Graph Technologies: the Next Frontier of the Food, Agriculture, and Water Domains. Frontiers in Artificial Intelligence, 2023, Frontiers Research Topics, 10.3389/978-2-8325-4182-1 . hal-04612011

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# Knowledge graph technologies: The next frontier of the food, agriculture, and water domains

**Edited by**

Marie-Angélique Laporte, Catherine Rousseau and Christophe Guéret

**Published in**

Frontiers in Artificial Intelligence

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# Knowledge graph technologies: The next Frontier of the food, agriculture, and water domains

**Topic editors**

Marie-Angélique Laporte — Bioversity International, France

Catherine Roussey — INRAE Occitanie Montpellier, France

Christophe Guéret — Accenture, Ireland

**Citation**

Laporte, M.-A., Roussey, C., Guéret, C., eds. (2023). *Knowledge graph technologies: The next Frontier of the food, agriculture, and water domains*.

Lausanne: Frontiers Media SA. doi: 10.3389/978-2-8325-4182-1
Table of
contents

04 **Editorial: Knowledge graph technologies: the next Frontier of the food, agriculture, and water domains**
Catherine Roussey, Christophe Guéret and Marie-Angélique Laporte

07 **Integrating collective know-how for multicriteria decision support in agrifood chains—application to cheesemaking**
Patrice Buche, Julien Couteaux, Julien Cufi, Sébastien Destercke and Alrick Oudot

22 **Using knowledge graphs to infer gene expression in plants**
Anne E. Thessen, Laurel Cooper, Tyson L. Swetnam, Harshad Hegde, Justin Reese, Justin Elser and Pankaj Jaiswal

33 **EPPO ontology: a semantic-driven approach for plant and pest codes representation**
Aarón Ayllón-Benitez, José Antonio Bernabé-Diaz, Paola Espinoza-Arias, Iker Esnaola-Gonzalez, Delphine S. A. Beeckman, Bonnie McCaig, Kristin Hanzlik, Toon Cools, Carlos Castro Iragorri and Nicolás Palacios

46 **Ontological how and why: action and objective of planned processes in the food domain**
Damion Dooley and Tarini Naravane

56 **Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes**
Baptiste Imbert, Jonathan Kreplak, Raphaël-Gauthier Flores, Grégoire Aubert, Judith Burstin and Nadim Tayeh

74 **Combining different points of view on plant descriptions: mapping agricultural plant roles and biological taxa**
Florence Amardeilh, Sophie Aubin, Stephan Bernard, Sonia Bravo, Robert Bossy, Catherine Faron, Franck Michel, Juliette Raphel and Catherine Roussey

91 **CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring**
Arjun Pakrashi, Duncan Wallace, Brian Mac Namee, Derek Greene and Christophe Guéret

104 **C3PO: a crop planning and production process ontology and knowledge graph**
Baptiste Darnala, Florence Amardeilh, Catherine Roussey, Konstantin Todorov and Clément Jonquet
<figure><img src="image_4.png" /></figure>

Check for updates

OPEN ACCESS

EDITED AND REVIEWED BY
Matthew McCabe,
King Abdullah University of Science and
Technology, Saudi Arabia

*CORRESPONDENCE

Catherine Roussey

catherine.roussey@inrae.fr

Christophe Guéret

christophe.gueret@accenture.com

Marie-Angélique Laporte

m.a.laporte@cgiar.org

RECEIVED 11 October 2023

ACCEPTED 24 October 2023

PUBLISHED 28 November 2023

CITATION

Roussey C, Guéret C and Laporte M-A (2023)
Editorial: Knowledge graph technologies: the
next Frontier of the food, agriculture, and water
domains. *Front. Artif. Intell.* 6:1319844.
doi: 10.3389/frai.2023.1319844

COPYRIGHT

© 2023 Roussey, Guéret and Laporte. This is an
open-access article distributed under the terms
of the *Creative Commons Attribution License*
(CC BY). The use, distribution or reproduction
in other forums is permitted, provided the
original author(s) and the copyright owner(s)
are credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted which
does not comply with these terms.

# Editorial: Knowledge graph technologies: the next Frontier of the food, agriculture, and water domains

Catherine Roussey¹*, Christophe Guéret²* and
Marie-Angélique Laporte³*

¹MISTEA, INRAE & Institut Agro, University of Montpellier, Montpellier, France, ²Accenture, Technology Innovation Labs, The Dock, Dublin, Ireland, ³Digital Inclusion, Bioversity International, Montpellier, France

KEYWORDS

agriculture, food, knowledge graph, ontology, graph database, Semantic Web, property graph, inference

## Editorial on the Research Topic

Knowledge graph technologies: the next Frontier of the food, agriculture, and water domains

A Knowledge Graph (KG) is based on a graph model to encode the description of entities. As defined by Hogan and his collaborators in 2022, a knowledge graph is “a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent relations between these entities.” For Knowledge Graph using Semantic Web technologies, entities (people, events, concepts, etc.) are identified by a Uniform Resource Identifier (URI). This URI is the source of a graph description, the edge specifies the nature of the link (person name or brotherhood relationship) and the destination of the edge could be a simple literal (the person name) or a URI that identifies another entity (the URI of the brother). The main advantage of these technologies is to link entities that are described differently in several knowledge graphs provided by various organizations. Thus, computer scientists may analyze all those graph descriptions to derive new information (detect incoherencies, complete data, etc.).

During the last decade, considerable progress has been made in the construction and enrichment of KGs, including ontology matching, data integration, fact prediction, and validation. This happened largely thanks to the use of techniques developed in the fields of knowledge representation, reasoning, and machine learning. With these advances, more and more applications are now able to produce and process KGs in domains such as life sciences, Galleries/Libraries/Archives/Museums (GLAMs), and health care. The subjects of interest within the Food, Agriculture, and Water domains are often complex phenomena where entities evolve through time and space. Those phenomena may be transformed by different processes and influenced by both human and natural systems. The scientific disciplines that study these phenomena are diverse and do not necessarily share the same vocabularies, the same techniques of observation, the same analyses, and so on. Indeed, each discipline often has its own point of view to describe the complexity of the studied phenomena. KG technologies provide one possible approach to express this diversity of representations and align or combine them.

This Research Topic has received 13 abstracts, from which 8 articles were accepted.

Three articles present a method, 4 articles are original research, and 1 is a conceptual analysis. Overall they cover three broad Research Topics often discussed in the KG research communities: ontologies design, data architectures, reasoning.
Ontologies are the back-bone of KG modeling as they define
what is in the data and how the information is connected. The
Research Topic covers this important topic with three publications:

* "C3PO: a crop planning and production process ontology and knowledge graph" by Darnala et al. presents the design method to build and update a modular ontology and associated knowledge graph about vegetable production and planification activities. Some new design patterns are defined dedicated to agriculture. For example, the set of planned tasks that compose a technical itinerary of a crop type are presented. The final C3PO knowledge graph was used by the Elzeard enterprise to build three decision information systems.

* "EPPO ontology: a semantic-driven approach for plant and pest codes representation" by Ayllón-Benitez et al. presents the translation of European and Mediterranean Plant Protection Organization (EPPO) database into an OWL ontology. Each entity identified by an EPPO code becomes an OWL class. The ontology will be used as lingua franca to search data into different information systems used in BASF.

* "Ontological how and why: action and objective of planned processes in the food domain" by Dooley and Naravane present an extension of the FoodOn ontology about food processes. They propose two new types of process representations: processes by objectives, processes by mechanisms. Their goals are to improve search capability and identification.

An ontology on its own is not much use without data to
instantiate it. The past decades of research into KG saw several
approaches being presented to combine and align different data
into a KG. Not all of those apply straight away to the agricultural
domain and this Research Topic features 4 articles proposing
specialized innovative approaches:

* "CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring" by Pakrashi et al. presents an approach to integrate data in the dairy industry by leveraging a combination of data mesh and data fabric design pattern. The approach is presented from a general point of view along with two specific use-case examples for the dairy industry.

* "Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case study on grain legumes" by Imbert et al. presents the design method of a Neo4J graph database that integrates the trait and gene information extracted from several sources. The graph model reuses existing ontologies like the Gene Ontology (GO), the Plant Ontology (PO) and the Plant Experimental Condition Ontology (PECO). The method was applied on the database design related to five legume species.

* "Combining different points of view on plant descriptions: mapping agricultural plant roles and biological taxa" by Amardeilh et al. presents some guidelines to publish a mapping dataset between two knowledge graphs: The French Crop Usage thesaurus defined crop usage expressed in French. TAXREF is the nomenclatural and taxonomic repository of living organisms that appear in French territories. A

new specialized RDF vocabulary of mapping is defined
and presented.

* "Integrating collective know-how for multicriteria decision support in agrifood chains—application to cheesemaking" by Buche et al. presents a multi-criteria decision support system (MDCSS) based on the capture and modelization of collective know-how in a Knowledge Graph. The ontology for expressing this information is introduced together with an example application for the process of cheese making.

Lastly, to illustrate the “Knowledge” part of a KG and reasoning
over this knowledge, we have in this issue one paper covering using
a KG to infer new information:

* "Using knowledge graphs to infer gene expression in plants" by Thessen et al. illustrates how a knowledge graph connecting partial information available about different plants can lead to new insights. Leveraging homologous genes as an inference back-end it is possible, as shown, to infer some of the unknown phenotypic impacts of plants gene regulatory networks.

We would like to thank the authors who submitted articles,
the reviewers who evaluated them and the external editors who
managed the reviews. All these people helped build a quality
program for this Research Topic.

Author contributions

CR: Writing—original draft, Writing—review & editing.
CG: Writing—original draft, Writing—review & editing. M-AL:
Writing—original draft, Writing—review & editing.

Funding

The author(s) declare that financial support was received for
the research, authorship, and/or publication of this article. CR
benefits from support of the Data to Knowledge in Agronomy
and Biodiversity (D2KAB-www.d2kab.org) project that received
funding from the French National Research Agency (ANR-18-
CE23-0017) and the project Partages de Connaissances (PACON)
of the transverse program MetaBio funded by INRAE.

Acknowledgments

CR was part of the French association for Artificial Intelligence (AFIA https://afia.asso.fr/) and would like to thank the knowledge engineering college of AFIA for their help in the reviewing process.

Conflict of interest

The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated

organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
<figure><img src="image_5.png" /></figure>

Check for updates

OPEN ACCESS

EDITED BY

Christophe Guéret,
Accenture, Ireland

REVIEWED BY

Donagh Berry,
Teagasc Food Research Centre, Ireland
Stephanie Bisgaard,
Accenture, United States
Jan Top,
Wageningen University and
Research, Netherlands

*CORRESPONDENCE

Patrice Buche

<figure><img src="image_6.png" /></figure>

patrice.buche@inrae.fr

¹These authors have contributed equally to this work

RECEIVED 15 January 2023

ACCEPTED 03 April 2023

PUBLISHED 28 April 2023

CITATION

Buche P, Couteaux J, Cufi J, Destercke S and
Oudot A (2023) Integrating collective
know-how for multicriteria decision support in
agrifood chains—application to cheesemaking.
*Front. Artif. Intell.* 6:1145007.
doi: 10.3389/frai.2023.1145007

COPYRIGHT

© 2023 Buche, Couteaux, Cufi, Destercke and Oudot. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Integrating collective know-how
for multicriteria decision support
in agrifood chains—application to
cheesemaking

Patrice Buche¹*†, Julien Couteaux²†, Julien Cufi¹†,
Sébastien Destercke³† and Alrick Oudot¹†

¹IATE, INRAE, Univ. Montpellier, Institut Agro, Montpellier, France, ²I2M, INRAE, Univ. Bordeaux, Bordeaux, France, ³HEUDIASYC, CNRS, Univ. Compiègne, Compiègne, France

Agrifood chain processes are based on a multitude of knowledge, know-how and experiences forged over time. This collective expertise must be shared to improve food quality. Here we test the hypothesis that it is possible to design and implement a comprehensive methodology to create a knowledge base integrating collective expertise, while also using it to recommend technical actions required to improve food quality. The method used to test this hypothesis consists firstly in listing the functional specifications that were defined in collaboration with several partners (technical centers, vocational training schools, producers) over the course of several projects carried out in recent years. Secondly, we propose an innovative core ontology that utilizes the international languages of the Semantic Web to effectively represent knowledge in the form of decision trees. These decision trees will depict potential causal relationships between situations of interest and provide recommendations for managing them through technological actions, as well as a collective assessment of the efficiency of those actions. We show how mind map files created using mind-mapping tools are automatically translated into an RDF knowledge base using the core ontological model. Thirdly, a model to aggregate individual assessments provided by technicians and associated with technical action recommendations is proposed and evaluated. Finally, a multicriteria decision-support system (MCDSS) using the knowledge base is presented. It consists of an explanatory view allowing navigation in a decision tree and an action view for multicriteria filtering and possible side effect identification. The different types of MCDSS-delivered answers to a query expressed in the action view are explained. The MCDSS graphical user interface is presented through a real-use case. Experimental assessments have been performed and confirm that tested hypothesis is relevant.

KEYWORDS

ontology, decision support systems, Semantic Web, knowledge representation, expertise integration, cheesemaking

1. Introduction

Agrifood chain processes are based on a multitude of knowledge, know-how and experiences forged over time. Agrifood companies that manage food product processing rely on their know-how to tailor their practices to the prevailing raw material variations, consumer expectations and regulations. The practice of acquiring knowledge through hands-on experience is a common one in the transformer industry, resulting in a vast accumulation of expertise among workers. This knowledge is typically passed on through on-the-job training and learning by doing. However, recent economic and health crises,
along with internal changes within companies such as increased
turnover and difficulty recruiting in certain sectors, have
made it increasingly challenging to preserve and transmit this
valuable know-how.

The aim of this paper, building upon the work of Buche et al. (2019), is to develop a new method for gathering and organizing knowledge, integrated in a software tool that can aid in preserving, accessing, and regularly updating the collective knowledge of the food industry for use in technology-related decision making. By implementing this methodology, we hope to overcome the challenges faced in preserving and transmitting the wealth of expertise within the industry and support the continued development of the food sector. The possibility of sustainably safeguarding and promoting practitioners' experience, as well as the technical expertise and scientific knowledge gained within a given food processing chain will be demonstrated based on a long-term collaboration with French cheesemaking companies with a "geographical indication" label, such as the protected designation of origin [appellation d'origine protégée (AOP)] and protected geographical indication [indication géographique protégée (IGP)].

The emergence of methods based on knowledge engineering
in the field of food and bio-based product processing facilitates
the development of decision-support tools that model complex
reasoning based on processing operators' expertise (Buche et al.,
2019; Baudrit et al., 2022; Belaud et al., 2022; Munch et al.,
2022). Here we present a new multicriteria decision-support
system (MCDSS) based on collective know-how which enables the
formulation of recommendations on technological actions that may
help maintain product quality or correct a product quality defect at
the scale of a given food processing operation.

The MCDSS workflow process presented in Figure 1 consists
of five main steps. The first one is a collaborative mind
mapping activity involving almost all technicians of a given
food chain and coordinated by a technical expert serving as an
adviser in each chain. He/she is responsible for structuring the
knowledge expressed in decision trees using a mind mapping
software tool that respects some simple syntactic conventions
(keyword labels in nodes). One decision tree is associated with
a situation of interest (a product quality or defect) while being
input in a given mind-mapping file. A decision tree represents
potential causal relations between the situation of interest and
explanatory situations associated with recommendations in terms
of technological actions to manage the situation of interest. The
second step involves individually and then collectively determining
the efficiency of actions based on technician feedback. This
information is input in the same mind-mapping file. In the
third step, the mind-mapping file is automatically translated and
stored in the knowledge base implemented as an RDF knowledge
graph. End-users (technicians, food chain operators, students,
etc.) mine, in the fourth step, the knowledge base using two
views available in the MCDSS to deliver recommendations. For
a given situation of interest, the explanatory view displays all
possible explanatory situations, associated analytical parameter
values and technical actions to correct/reach the situation of
interest. The Action View feature enables users to efficiently
filter actions based on multiple criteria within a decision tree,
in order to correct or reach a desired situation. Additionally,

it allows users to identify any potential side effects associated
with a given recommendation. Users can easily switch back and
forth between the two views, facilitating the process of selecting
the best recommendation for a specific situation. The MCDSS
workflow process is iterative (see fifth step in Figure 1), i.e.,
each decision tree including action efficiency indicators may be
easily updated in the mind mapping tool to account for new
experiences which are then automatically translated in the MCDSS
knowledge base.

The Materials and methods section focuses on the
following topics:

* Specifications and architecture of the decision-support system.

* A proposed model to aggregate individual action efficiency assessments.

* An ontological model to structure MCDSS knowledge base content.

* Two views of the multicriteria decision-support system.

The Results and Discussion section presents MCDSS
functionality assessments and a comparison with the current state
of the art.

2. Materials and methods

## 2.1. Specifications and architecture of the decision-support system

The detailed MCDSS specifications were determined in
collaboration with several technical centers associated with French
cheesemaking, namely Comté, Reblochon, Emmental de Savoie,
Cantal, and Salers in the framework of two research projects
funded by the French government from 2017 to 2023 (CASDAR
Docamex, France Relance Docamex). Hereafter is a list of
target functionalities:

1. For a given situation of interest (targeted food quality or defect), the MCDSS must provide all known possible explanations organized in a decision tree starting from the most general explanatory situations, which must be refined by more specific explanations until it is precise enough to propose an action lever and an associated recommended technological action. It must represent interactions between explanatory situations. Two kinds of interaction should be considered: (i) conjunctive interactions of situations S<sub>1</sub> and S<sub>2</sub> to explain S<sub>3</sub>, which means that situation S<sub>3</sub> may emerge only if S<sub>1</sub> and S<sub>2</sub> appear; (ii) strengthening (resp. weakening) interactions of situation S<sub>1</sub> by situation S<sub>2</sub> to explain S<sub>3</sub>, which means that the effect of S<sub>1</sub> on S<sub>3</sub> is strengthened (resp. weakened) if S<sub>2</sub> appears. The decision tree will enable users to consider all possible known explanations of a given situation of interest. This functionality, which is mostly geared toward junior technicians, is very important in cheesemaking chains as they have to deal with growing turnover rates.

2. It should be possible to associate a situation ("of interest" or explanatory) with the value of a relevant analytical parameter that allows verification that the situation is actually happening.
<figure><img src="image_7.png" /><figcaption>**FIGURE 1**

Workflow process associated with the MCDSS (M, manual task; A, automatic task; SA, semi-automatic task). The three stars indicate the action's efficiency is "very effective".</figcaption></figure>

This is of great interest for technicians who have to deal with
several cheese production processes (e.g., Comté and Bleu de
Bresse for the CTFC technical center) without being fully aware
of all of the analytical parameter values associated with the
encountered situations.

3. The MCDSS must be able to determine the possible side effects of an action: a corrective action for one situation of interest should not lead another problem.

4. Feedback on technicians' individual experiences in terms of technological action efficiency to deal with a given situation of interest must be registered and aggregated. Indeed, action ranking is of great importance to help users choose the "best" action to cope with a given situation of interest. Moreover, registration of contextual criteria relevant for decision-support and associated with those assessments is required to facilitate decision support. For instance, a given action like "Review herd rationing practices" may be considered very efficient in the long term (LT), yet not at all efficient in the short term (ST). The MCDSS must be able to rank actions using a multicriteria filtering system.

5. It must represent the expert knowledge expressed in decision trees using international World Wide Web Consortium (W3C) standards in order to facilitate interoperability between industry and academic institutes in an Open Science setting. More particularly, two standard languages are recommended: (1) Resource Description Framework (RDF) for graph data description and exchange. RDF provides a variety of syntax notations and data serialization formats; (2) Web Ontology Language (OWL), a family of knowledge representation languages for authoring RDF-based ontologies.

## 2.2. From mind mapping to formal knowledge representation

Buche et al. (2019) proposed a method that enables collective mind mapping dedicated to this MCDSS. Interested readers may refer to this paper for further details on step 1 implementation (see Figure 1). In this section, we focus on two new contributions of the paper. The first concerns a numerical model that aggregates individual assessments associated with action efficiency expressed by technicians into a single indicator. This functionality is required in Specification 4 (see Section 2.1). During step 2, as presented in Figure 1, the aggregated indicator is discussed and validated collectively by the team of technicians to determine the final action efficiency value, which is input in the knowledge base for decision-making support. The second contribution is an extended version of the ontology presented in Buche et al. (2019) to structure the information in the MCDSS knowledge base for navigation and querying purposes. The extension includes the efficiency indicators and associated criteria. This extended version is expressed using the W3C standards to fulfill Specification 5 (see Section 2.1), which is also a novel contribution of this paper as the ontology presented in Buche et al. (2019) was based on the Conceptual Graph model (Sowa, 1984; Chein and Mugnier, 2009).

### 2.2.1. A model to aggregate individual action efficiency assessments

Each technician, denoted $T_i$ hereafter, provides two types of information:
* His/her experience in terms of number of action implementations, called $F_i$, reflecting the reliability of his/her statements, which takes its value in the set $\{(N)\}$ever, (R)arely: $1 < 3$, (S)ometimes: $3 < 10$, (O)ften: $>10\}$, as summarized by $R = \{N, R, S, O\}$.

* The efficiency of the action, denoted $E_i$, which takes its value in $\{\text{Very effective (A)}, \text{Moderately effective (B)}, \text{Not very effective (C)}, \text{No effect (D)}\}$, as summarized by $E = \{A, B, C, D\}$.

The technician can also select “don’t know” for the second value.

With the experience of the technician corresponding to the number of times (roughly) where he/she encountered the situation of interest, it seems quite natural to interpret his/her answer as a number of “virtual” observations. We will therefore associate with each value in R an equivalent number, i.e., $N \to 0.5$, $R \to 2$, $S \to 5$, $O \to 10$. In practice, each of these values is chosen to be within the corresponding interval. For instance, Rarely corresponds to the interval [1,3], for which we picked the central value 2. We still assigned a positive value to Never, so as to reflect the fact that the reported experience may come from sources other than direct observation. Those choices were made in accordance with the end user and can in practice be changed according to the application, as they remain subjective (but not arbitrary) to some extent. The corresponding intervals could in principle also be kept, yet processing such information would increase the cognitive load for users, hence our choice to keep precise numbers representing the numbers of experiments.

Let $n_i$ be the number corresponding to the experience of technician $T_i$. For example, if technician $T_i$ answers $F_i = R$, therefore rarely, then $n_i = 2$. If k technicians provide an answer, then total $N = \sum_{i=1}^k n_i$ will denote the total number of virtual observations.

The aim is then—based on these virtual observations—to construct a histogram on E, and associate a probability with each of its elements. Let $n^A$, $n^B$, $n^C$, $n^D$ denote the total number of observations given to A, B, C, D, respectively.

**Definition 1:** $n^A$ the total number of observations given to A is defined by

$$n^A = \sum_{T_i: E_i=A} n_i$$

The probability (subjective and a priori) of A then becomes

$$p(A) = \frac{n^A}{Nb}$$

and the same for B, C, D.

**Example 1:** Suppose three technicians provide their opinions as follows:

* $F_1 = R \Rightarrow n_1 = 2$; $E_1 = A$ (very effective)

* $F_2 = S \Rightarrow n_2 = 5$; $E_2 = B$ (moderately effective)

* $F_3 = R \Rightarrow n_3 = 2$; $E_3 = C$ (not very effective)

which gives $N = 9$ and $p(A) = 2/9$; $p(B) = 5/9$; $p(C) = 2/9$.

The information given by the previous distribution is probably too complex to be readily understood by a technician and requires a

simple summary. This can easily be done through various statistics and then supplied to the user in graphical and easily interpretable form. In contrast with number of times a situation has been encountered, in our case efficiency is not associated with an actual numerical measure. Moreover, such measures would probably vary across situations and not be comparable. We therefore chose to not replace ordered categories A, B, C by numbers, and instead provided both a central value and its dispersion based on the quantile notion. More precisely, we will use the median (quantile at 50%) and two quantiles around the latter (therefore 50% - $\alpha$ and 50% + $\alpha$) as a statistical summary.

**Definition 2:** the quantile of level $\in [0, 1]$, denoted $i_\beta$, relative to the distribution $p$ defined on E is the value

$$i_\beta = \left\{ j \in E: \left( \sum_{l \le j-1} p(l) < \beta \right) \wedge \left( \sum_{l \le j} p(l) \ge \beta \right) \right\}$$

where $<$ corresponds to the alphabetical order and with the convention $\sum_{l \le 0} p(l) = 0$.

Let us get back to our previous example, where we will conventionally denote $P(\{A, B\}) = p(A) + p(B)$, etc.

**Example 2-1:**

$$P(\{A\}) = p(A) = 2/9 = 0.2222.$$

$$P(\{A,B\}) = p(A) + p(B) = 2/9 + 5/9 = 0.7777.$$

$$P(\{A,B,C\}) = P(\{A,B,C,D\}) = 1.$$

We will therefore have the following quantile $i_{0.1} = A$ (first decile) because $\sum_{l \le 0} p(l) = 0$ and $\sum_{l \le A} p(l) = 0.2222$ therefore $\{(\sum_{l \le 0} p(l) < 0.1) \wedge (\sum_{l \le A} p(l) \ge 0.1)\}$ is true.

In the same way, $i_{0.25} = B$ (first quartile); $i_{0.5} = B$ (median); $i_{0.75} = B$ (third quartile); $i_{0.9} = C$ (ninth decile).

It is clear that if the technicians all provide the same evaluation, then all the quantiles will have the value of this evaluation. Conversely, if the technicians are somewhat divided and of equivalent experience, the difference between the quantiles will show this uncertainty. We hence propose to match A, B, C, D to a number of “stars” (3,2,1,0) and to provide the average of the values observed in set $[i_{0.1}, i_{0.9}]$ as a reference value. In our example, this is the set [A, B, C], with the reference value 2. It would also be useful to show that there is no consensus on this reference value by highlighting all the intervals [1,3].

**Figure 7** presents examples of graphical representations in terms of stars. The following example illustrates the case where the reference value is not one of the initial values.

**Example 2-2:**

Suppose that two technicians provide their opinions as follows:

* $F_1 = R \Rightarrow n_1 = 2$; $E_1 = A$ (very effective)

* $F_2 = P \Rightarrow n_2 = 5$; $E_2 = B$ (moderately effective)

In this case, $p(A) = 2/7$, $p(B) = 5/7$ with $i_{0.5} = B$ (median) and $[i_{0.1}, i_{0.9}] = [A, B]$ with 2.5 being the obtained average (stars).

### 2.2.2. A new ontological model to structure the MCDSS knowledge base content

Decision trees edited in mind-map files in step 1 and enriched with action efficiency assessments in step 2 must be stored in the
<figure><img src="image_8.png" /><figcaption>FIGURE 2
OWL ontological model used to structure a decision tree in the MCDSS knowledge base.</figcaption></figure>

MCDSS knowledge base. As indicated in Specification 5, Semantic Web language standards created by W3C must be used for knowledge base implementation. The OWL ontology—an original contribution of this paper—designed to structure and instantiate a decision tree in the RDF knowledge base is presented in this section.

The OWL definition of classes and properties presented in Figure 2 is available in Buche et al. (2022). Hereafter we explain how this ontological model takes the specifications expressed in Section 2.1 into account.

As expressed in Specification 1, a situation S₁, an instance of the Situation OWL class, is explained by a situation S₂ through an instance of the CausalityNode class linked to S₁ (resp. S₂) by the OWL `hasForCause` (resp. `hasForConsequence`) object property. Note that S₁ may be an instance of the SituationOfInterest class that is a kind of Situation. A situation S may be associated with an action A via the `hasForAction` object property. An action A is associated with its lever through the `hasForLever` object property. A conjunctive interaction CI₁, an instance of the SituationConjunction class, is linked to conjunctive causal situations S₁ and S₂ (and other situations if required) by the `isComposedOf` property. CI₁ is linked to an instance of the CausalityNode class by the `hasForCause` property. This CausalityNode class instance is linked to the consequence situation S₃ by the `hasForConsequence` property. The strengthening (resp. weakening) interaction of situation S₁ by situation S₂ to explain S₃ is also represented using a conjunctive interaction CI₁, an instance of the SituationConjunction class. The asymmetric role of situations is achieved in the following way: the altered situation S₁ is linked to altering situation S₂ via the *SpecificationOfWeakening* (*resp.* *SpecificationOfReinforcement*) object property if the alteration type is weakening (resp. strengthening).

The `isDetectedBy` datatype property associated with an instance of the Situation class implements Specification 2. An instance of Action is associated with an instance of the Efficiency class to implement Specification 4. The `hasForKeyCriterion` datatype property permits determination of the list of criteria values associated with a single *Efficiency* instance. The `hasForScore`, `hasForObservations`, and `hasForTechniciansAgreement` datatype

properties are associated with an *Efficiency* class instance which is linked to an *Action* instance. The `hasForConsequenceCriterion` object property links an *Efficiency* instance with a set of pairs (name, value) that are used for decision support. The *refersToDefect* object property links an *Efficiency* instance with the situation of interest to which it refers.

Figure 3 is an excerpt of a mind-mapping file representing the decision tree associated with the situation of interest *Excessive salting* achieved by the blue node at the bottom left part of the figure. The entire mind-mapping file is available in Buche et al. (2022). This situation of interest may be explained by the *Significant salt intake* situation. Then four explanations are possible. Hereafter we will consider the one whose node is white, i.e., *Conditions favoring salt uptake in brine* and its associated branch, whose nodes are also white, until reaching the two nodes *Put the brine tank in the dryer* and EFFICIENCY: ST. Figure 4 shows a zoom on the table associated with the node EFFICIENCY: ST. This table includes the aggregated efficiency indicator with the number of observations (see Section 2.2.1) and contextual criteria associated with them. In Figure 5, we present a part of the MCDSS RDF knowledge base corresponding to the translation of the branch whose nodes are white in the decision tree presented in Figure 3. The entire RDF graph corresponding to the mind-mapping file is available in Buche et al. (2022).

In Figure 5, to facilitate the understanding of the translation of Figures 3, 4 into RDF, instances of OWL classes are represented by rectangles, with the class name in the header complemented by a pseudo-label representing its URI (as the real one is too long) or the associated value of the rdfs:label property. Values associated with datatype properties are framed in black.

## 2.3. Multicriteria decision-support system

The decision-support system (see step 4 in Figure 1) consists of two complementary access modes to the knowledge base content, i.e., the explanatory and action views. The explanatory view
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

displays the decision tree associated with a given situation of interest, including all possible explanatory situations, associated analytical parameter values and technical actions to correct/reach the situation of interest. The action view displays the list of actions related to a given decision tree to correct/reach the associated situation of interest. It enables multicriteria filtering, action ranking and side effect identification.

Both views may be used independently and jointly depending on the usage case. For instance:

* A systematic review of all possible explanatory situations is carried out using the explanatory view.

* Solving a contextualized problem is carried out using the action view through the multicriteria
<figure><img src="image_11.png" /><figcaption>FIGURE 5
An excerpt of the MCDSS knowledge base corresponding to the selected branch in Figure 3.</figcaption></figure>

filtering mode, sometimes complemented with the explanatory view.

Hereafter we define a multicriteria (MCDSS) query executed in the action view and the associated answers. Then we present the MCDSS graphical user interface (GUI) using an illustrative example based on a real case from a French protected designation of origin (AOP) chain.

### 2.3.1. MCDSS query definition

We define in this section the notion of MCDSS query Q executed on the KB knowledge base. Then the answer to Q, called AN, and the two complementary AN-inter and AN-intra answers are defined for side effect identification.

**Definition 3:** The MCDSS knowledge base (KB) is defined as the 9-tuple $(S, V_k, C_c, V_c, A, L, E, Ag, O)$, with:

* $S$ = the set of instances of the SituationOfInterest class;

* $V_k$ = the set of key criteria labels associated with the hasForKeyCriterion datatype property;

* $C_c$ = the set of consequence criteria names associated with the hasForName datatype property;

* $V_c$ = the set of consequence criteria values associated with the hasForValue datatype property;

* $A$ = the set of Action class instances implemented using Lever class instances;

* $L$ = the set of Lever class instances;

* $E$ = the set of action efficiency labels associated with the hasForScore datatype property;

* $Ag$ = the set of action efficiency consensus labels associated with the hasForTechnicianAgreement datatype property;

* $O$ = the set of action efficiency labels associated with the hasForObservations datatype property.

**Definition 4:** Given KB defined in Def. 3, the set of input conjunctive filtering parameters associated with an MCDSS query Q executed in KB is defined by the 6-tuple:

$$ (s \in S, \{v_1, \dots, v_m\} \in V_k, \{(c_1, v_1), \dots, (c_n, v_n)\} \in (C_c, V_c)^n, \\ \{e_1, \dots, e_o\} \in E, \{ag_1, \dots, ag_p\} \in Ag, \{o_1, \dots, o_q\} \in O) $$

Note: multivalued parameters are considered to be aggregated disjunctively in the querying.

**Example 3:** $Q1 = (\text{excessive salting}, \{\emptyset\}, \{\text{very effective}\}, \{\text{good, average}\}, \{\emptyset\})$ represents the querying of the excessive salting situation of interest with the action efficiency being very effective and the action efficiency consensus being good or average. The SPARQL query generated by the MCDSS and corresponding to is available in Buche et al. (2022).

**Definition 5:** The answer AN associated with an MCDSS query Q executed in KB is defined by a set of 2-tuples: $\{(a_1, l_1), \dots, (a_n, l_n) \in (A, L)^n\}$, with $(a_i, l_i)$ related to the decision tree associated with the situation of interest s.

**Example 4:** $AN1 = \{(\text{dilute the brine, Brine salt concentration}), (\text{acidify the brine to pH 5.4, Brine acidity}), (\text{practice brining on a rack, Brining equipment}), (\text{reduce brining time, Brine duration})\}$ is the answer that includes the four recommended actions associated with the query of Example 1. The triplets results of the SPARQL query corresponding to is available in Buche et al. (2022).

Two complementary answers with AN are provided by the MCDSS when the Q query is executed. The objective, corresponding to Specification 3 (see Section 2.1), is to identify two
types of potential side effects that could occur if a recommended
action related to AN is implemented:

*   **AN-inter**: potential side effects with other situations of interest related to KB. Situations where the associated decision tree recommends the use of a lever associated with a given AN action are selected.

*   **AN-intra**: potential side effects with other actions related to the decision tree associated with the situation of interest expressed in Q.

**Definition 6:** Given the AN answer to a query Q, the AN-inter answer associated with a recommended *a*ᵢ *action* implemented using a given *l*ᵢ lever with (*a*ᵢ, *l*ᵢ) ∈ AN is defined by a set of 2-tuples:

(*s*' ∈ S, {(*a*₁, *l*₁), ..., (*a*ₙ, *l*ₙ) ∈ (*A*, *L*)ⁿ}) with *s*' ≠ *s*, with *s* being the situation of interest associated with the Q query and (*a*ⱼ, *l*ⱼ), *j*=1, ..., *n* being related to the decision tree associated with the *s*' situation of interest.

**Example 5:** AN-inter1 associated with the recommendation (*reduce brining time*, *Brine duration*)) related to AN1 is ({*unpleasant taste* or *odor*, {(*extend the brining time* to 2h maximum, *Brine duration*)}}, (*brown paste*, {(*extend the brining time* to 2h maximum, *Brine duration*)}), (*excessive proteolysis*, {(*extend the brining time* to 2h maximum, *Brine duration*)}), (*insufficient salting*, {(*extend the brining time* to 2h maximum, *Brine duration*)}}).

AN-inter1 means that implementing the recommendation *reduce brining time* to solve the *excessive salting* situation may create a side effect with four other situations of interest likely to occur: *unpleasant taste* or *odor*, *brown paste*, *excessive proteolysis*, *insufficient salting*. Indeed, the same *Brine duration* lever is recommended to solve these situations but it is used in an opposite way (*extend the brining time* to 2h maximum), which could potentially trigger those situations of interest if the recommendation is applied. MCDSS users may query the decision trees associated with those situations of interest to find a good trade-off to avoid triggering unwanted side effects.

AN-inter1 means that implementing the recommendation *reduce brining time* to solve the *excessive salting* situation may create a side effect with four other situations of interest likely to occur: *unpleasant taste* or *odor*, *brown paste*, *excessive proteolysis*, *insufficient salting*. Indeed, the same *Brine duration* lever is recommended to solve these situations but it is used in an opposite way (*extend the brining time* to 2h maximum), which could potentially trigger those situations of interest if the recommendation is applied. MCDSS users may query the decision trees associated with those situations of interest to find a good trade-off to avoid triggering unwanted side effects.

**Definition 7:** The AN-intra answer associated with an *a* recommended *action* implemented using a given *l* lever to solve the *s* ∈ S situation of interest is defined by a 4-tuple:

( { (a₁₁, l₁) , · · · , (a₁ₙ, lₙ) } ∈ (A, L)ⁿ , { (a₂₁, l₁) , · · · , (a₂ₙ, lₙ) } ∈ (A, L)ⁿ , { (a₃₁, l₁) , · · · , (a₃ₙ, lₙ) } ∈ (A, L)ⁿ , { (a₄₁, l₁) , · · · , (a₄ₙ, lₙ) } ∈ (A, L)ⁿ ) with a₁ᵢ actions (resp. a₂ᵢ actions) corresponding to potential weakening actions of the recommended *a* action (resp. potential actions weakened by the recommended *a* action) and a₃ᵢ actions (resp. a₄ᵢ actions) corresponding to potential reinforcement actions of the recommended *a* action (resp. potential actions reinforced by recommended *a* action).

**Example 6:** AN-intra1 associated with the recommendation (*reduce brining time*, *Brine duration*) related to AN1 is ({(practice desalting on a rack, Brining equipment))},{Ø},{Ø},{Ø}).

AN-intra1 means that implementing the *reduce brining time* recommendation to solve the *excessive salting* situation may be weakened by the *practice desalting on a rack* action. Complementary information about this possible interaction may be found using the explanatory view.

2.3.2. MCDSS graphical user interface

Using an illustrative example, we show how the MCDSS graphical user interface has been implemented to propose both complementary access modes to the knowledge base content, i.e., the explanatory and action views.

The explanatory view proposes navigation in a decision tree associated with a given situation of interest to query all possible explanatory situations. Figure 6 shows an excerpt of the explanatory view for the *Excessive salting* situation of interest. Analytical values associated with situations are shown in red. For example, *NaCl rate* > XXX ¹g/100g is the value associated with the *Excessive salting* situation. The first high-level explanatory situation is *Significant salt intake by the cheese during its production*, while several others specify this high-level explanation. For instance, it could be explained by the *Conditions favoring salt uptake in brine situation*. By following this branch of the decision tree, we reach a more detailed explanation, i.e., *Too much salt added in brine*. This latter explanation is associated with the *Dilute the brine* action. Its associated analytical value is *Density to reach XXX-YYY°B*.

The action view enables knowledge base querying and filtering to solve a contextualized problem. Figure 7 shows a query presented in example 3 concerning the *Excessive salting* situation of interest. Filtering criteria used regarding the action efficiency indicator and agreement level enable filtering of three actions out of a total of 15 present in the decision tree.

Complementary answers identifying possible side effects may be obtained using buttons (see the two buttons at the bottom of Figure 7 corresponding to AN-intra and AN-inter answers for the Reduce brining time action). Figure 8 shows a list of four situations of interest presented in Example 5 above: unpleasant taste or odor, brown paste, excessive proteolysis, and insufficient salting. The Brine duration lever is recommended to solve these situations, while using it in an opposite way (extend the brining time to 2h maximum) compared to that recommended for the Excessive salting situation of interest. Figure 9 shows the action presented in Example 5, which may weaken the recommended Reduce brining time action.

3. Results

In this section, we present the assessment results of Specifications 1, 2, and 4, which were performed with end-users.

3.1. Reviewing all possible technological
actions associated with a situation of
interest (Specifications 1 and 2)

In a technological reasoning task, for a given situation of interest (targeted quality or defect), a technician must be able to check all possible explanatory situations and corresponding analytical parameters to check that this situation will happen. Moreover, he/she must be aware of the associated recommended technological action. The protocol presented in Figure 10 was

<footnote n="1">1 The actual numerical values have been anonymized to avoid recognition of the cheese chain.</footnote>
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

designed to assess the impact of MCDSS use in this reasoning task. It was tested with technicians from three different food chains. The protocol includes the following steps:

*   Fifteen people related to three different chains passed this test. Figure 11 shows that 39% of the technological actions were noted without the MCDSS and 66.5% after its use, which represents 27% enhancement. Only two chains (10 people) undertook the analytical value tests as chain 1 corresponds

to generic knowledge associated with a situation of interest learned in technical schools. As analytical values highly depend on a given cheesemaking process, it was not possible to conduct this test on this generic knowledge. Figure 12 shows that 18.33% of the correct answers were obtained without the MCDSS. The score increased to 76.25% after its use, which represents 60% enhancement. Both tests showed a good (even very good for the second one) enhancement with regard to the answers provided via use of the MCDSS.
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

* Unfortunately, the MCDSS prototype was not finished when the assessment campaign was carried out during the project. Consequently, it was not possible to assess the implementation corresponding to Specification 3 (identification of side effects associated with a recommended action). This will be of course done as soon as possible in the future. Nevertheless, the assessment results presented above suggest that these results will be also good. Indeed, finding all side effects between situations of interest (see Section 2.3, ANS-inter) may be a huge manual task as more than a 100 decision trees may be defined for a given cheese chain.

## 3.2. Technological efficiency aggregation assessment (Specification 4)

This assessment was conducted with a group of five technicians, all of whom were experts of a real cheesemaking process. It was focused on a set of three decision trees corresponding to three situations of interest (*Excessive dripping*, *Excessive acidification*, and *Excessive salting*). The five technicians provided individual assessments for 44 actions related to the three decision trees. Each action was assessed twice (88 assessments), with each assessment corresponding to two different production approaches: production
<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /><figcaption>**FIGURE 11**
Test results (blue without MCDSS, brown with MCDSS) for recommended technological action findings: the y-axis represents the number of correct answers (mean value) in the three chains and on average.</figcaption></figure>

approach 1 and production approach 2. Aggregated values were computed using the model presented in Section 2.2.1. In parallel, this group of experts collectively determined an assessment for

each action without using the model. Computed assessments were compared to collective assessments. The associated data are available in Buche et al. (2022). Two assessments were considered to be in disagreement when there was a difference of at least two modalities (e.g., <no effect, moderately effective>, <very effective, not very effective>, etc.). The results presented in Table 1 show an error rate of around 5.7% (Total number of disagreements/Total number of actions), which is rather low. In practice, the method was considered relevant enough to compute an aggregated efficiency indicator associated with an action in a given decision tree. This aggregated indicator was discussed and validated collectively by the group of technicians during monthly meetings before being input in decision trees stored in the MCDSS knowledge base.

As already said above, the MCDSS prototype was not finished when the assessment campaign was carried out. Consequently, it was not possible to assess the implementation corresponding to Specification 4 about multicriteria filtering (identification of side effects associated with a recommended action).

# 4. Discussion

We discuss in this section the original contributions of the paper compared to the current state of the art, summarize and provide complementary information about key contributions and present some future directions of the research.

## 4.1. Comparison to the current state of the art

A lot of progress has been achieved in knowledge integration and multicriteria analysis methods and tools in the food science and technology field, yet they remain fragmented and incomplete (Aceves Lara et al., 2018; Thomopoulos et al., 2019). Different methods have been developed to gather scientific and technological knowledge and data for different purposes, but this information has only been general and focused solely on elementary processing operations, which do not take into account the entire processing operation. For example, Kansou et al. (2014) proposed a qualitative model of a unitary mixing operation using an expert system to predict the quality of wheat flour dough. Baudrit et al. (2015) modeled preharvest grape berry maturity—a critical characteristic for the wine industry—using expert knowledge and data and probabilistic graphical approaches. Belna et al. (2022) optimized microfiltration unit operation to integrate conflicting stakeholder objectives, such as maximizing product output quality while minimizing cost inputs and addressing environmental impacts. Baudrit et al. (2022) used data from scientific articles describing the entire milk microfiltration process including several unit operations in addition to the milk microfiltration step as skimming, heat treatment or storage. Those data are available in Buche et al. (2021). But the method presented in Baudrit et al. (2022) only proposes to learn a predictive model of the milk microfiltration unit operation in large-scale operational conditions including different membranes.
<table><caption>TABLE 1 Assessment of the efficiency aggregation method.</caption><thead><tr><td>Decision tree</td><td>Number of action assessments</td><td>Number of disagreements</td></tr></thead><tbody><tr><td>Excessive dripping</td><td>36</td><td>2</td></tr><tr><td>Excessive acidification</td><td>28</td><td>1</td></tr><tr><td>Excessive salting</td><td>24</td><td>2</td></tr><tr><td>Total</td><td>88</td><td>5</td></tr></tbody></table>

In a circular economy context, Belaud et al. (2022) proposed a decision-support system to rank alternative lignocellulosic waste transformation processes based on knowledge engineering tools to compile experimental data to assess potential environmental impacts. Munch et al. (2021) and Munch et al. (2022) combined ontology, probabilistic models and linked open data to generate, through a reverse engineering approach, agricultural wastes well-suited for processing biocomposites for food packaging. In both cases, ontological models facilitated analysis of the impact of the entire processing operation on the end-product quality or on indicators associated with the process, but the approaches required gathering of a substantial set of numerical experimental data to obtain good results. These approaches are unsuitable to achieve the objectives outlined in this paper because they are over-demanding in terms of obtaining sufficient numerical data to represent the entire range of collective knowledge at the level of a given food chain.

Fault tree analysis (FTA), which targets fault event risk assessment (Baig et al., 2013), may be compared to our approach. FTA enables computation of a level of risk represented by the occurrence probability of an undesired event. FTA also helps identify critical safety solutions to avoid the risk. For instance, in Pahasup-anan et al. (2021), the authors analyzed different situations that could trigger a dust explosion in an extruded food production facility. Kim et al. (2020) used FTA to assess the level of risk of four situations which could help determine the risk of microbial contamination of food by E. coli. A fault tree includes a root node representing the undesired event. The branches of the tree represent the explanatory scenarios, which may explain the fault event starting from basic events representing situations that would likely contribute to the overall fault defined in the roots. A whole fault tree could be considered as a set of scenarios associated with a probability of occurrence. Risk analysis is of course essential. However, FTA quantitative analysis requires collection of basic event occurrence measurements.

Our core ontology, which defines the decision tree structure, is comparable to the tree structure used in FTA as we also represent a decision tree linking explanatory situations to a given situation of interest. However, our objectives are quite different (see Section 2.1). Our original contribution compared to FTA consists of: (i) proposing a semantic decision tree representation using Semantic Web languages to enable easier open data linkage with other sources of information available on the Web; (ii) representing levers and associated technological actions to solve a situation of interest; (iii) representing the action efficiency based on individual experience; (iv) representing contextual criteria associated with

recommendations to help filter recommendations in a multicriteria way; and (v) identifying possible side effects associated with the implementation of a recommendation. Moreover, FTA aims to estimate the probabilistic risk of failure, which requires the availability and collection of a substantial amount of numerical data, whereas our approach is based on collecting and representing the collective technical know-how available for a given domain (company, food chain, etc.).

Our ontological model may be compared with the COOK ontology (Ghrab et al., 2017), the Core Ontology of Organization Know-How and Knowing-That. In COOK, Know-How is defined as the capacity/disposition to perform an action. The COOK Know-How concept is similar to our Situation concept. COOK proposes a rich taxonomy to categorize different kinds of know-how (individual, collective, internal, external, crucial, etc.). From this viewpoint, we could consider that in the MCDSS the Situation concept is a specialization of CollectiveKnow-How. Our ontology enables us to represent complex interactions between Situations (conjunction, reinforcement, weakening) which is not possible in COOK. In COOK, the Knowing-That concept—a kind of belief—represents the relation between a proposition and a thinker. It assigns a truth value to the proposition. It is harder to compare this part of the COOK ontology to ours. Indeed, in our ontology, we implicitly consider that we represent a collective belief state, i.e., a COOK concept. On the other hand, we propose a more elaborate representation of the COOK Proposition concept as we represent expert reasoning using the notion of causality between situations, the efficiency of an action and associated contextual criteria. In conclusion, there are several similarities between both ontologies with a richer description of kinds of know-how in COOK and an explicit representation of expert reasoning in our ontology, which is not present in COOK. The part of our ontology which more or less corresponds to the Knowing-That concept part of COOK is more detailed because we have proposed a complete and operational MCDSS based on our ontology. Our ontology, like that of COOK, may be applied to any application domain based on know-how.

Compared to Buche et al. (2019), i.e., preliminary research that gave rise to the study described in this paper, several new contributions are proposed and assessed: (i) a model to aggregate action efficiency based on individual experience; (ii) an extended version of the ontology expressed in OWL, including the representation of action efficiency and associated information (key and complementary criteria); and (iii) the definition and implementation of the action view.

## 4.2. Key contributions

Here we showcased a new multicriteria decision-support system based on collective know-how in food chains to enhance food quality during the production process. This MCDSS is currently being used in production conditions in 13 AOP cheesemaking chain organizations involving professional stakeholders (cheese producers, experts working in technical centers) and professors from technological schools (ENILs), thereby comprising more than 60 users. Note that the model used to represent the decision trees may be refined in a flexible
<figure><img src="image_18.png" /><figcaption>FIGURE 12</figcaption></figure>

Test results (blue without MCDSS, brown with MCDSS) for analytical value findings: the y-axis represents the percentage of correct answers (mean value) in two chains and on average.

way until the level of detail sought by the expert is reached. This flexibility has been successfully used in the project as professors from technological schools have created generic cheese-making decision-trees. Operators introducing new chains in the project have tailored these generic decision trees to their specific cheese-making process.

Most of the MCDSS functionalities implemented to fulfill the specifications have been assessed, with promising results overall. All of the ontological model concepts are required to implement the MCDSS specifications. The choice to represent the causality relation between situations may be discussed. Indeed, a *HasCausality* semantic relation between two *Situation* nodes would have also been possible and simpler. However, as presented below in the perspectives of this work, it would be useful for advanced users conducting statistical analyses to be able to qualify the causality relation between situations in a future version of the MCDSS. By example, we would like to distinguish between types of sources, which contain the statistical analysis (internal study, bibliographical study). This metadatum should be associated with Causality nodes.

This justifies the modeling choice to set the stage for this future development. The comparison with and without the MCDSS was important to convince technicians of the MCDSS relevance. There are currently many obstacles to innovation, especially when it comes to know-how of which experts are sometimes afraid of losing (loss of employment, of power, etc.). Moreover, the direct use of mind maps without MCDSS assistance could be questioned. From our viewpoint the relevance is limited since the use of a simple mind map does not allow for three kinds of computerized analysis:

* The first corresponds to numerical aggregation of individual action efficiency assessments (see Specification 4). It provides an aggregated indicator, which is discussed and validated collectively by the technician team to determine the final action efficiency value, especially in the event of major disagreement. This value enriches the decision tree

associated with a given situation of interest registered in the knowledge base.

* The second consists of the action multicriteria filtering mechanism generated by MCDSS queries, which reduces the list of candidate actions for a given situation of interest (see Specification 4).

* The third corresponds to the computing of complementary answers to enable assessment of the potential side effects with other actions and situations of interest (see Specification 3).

A typical use case of the MCDSS, which illustrates the relevance of those computerized analysis, is the following. A cheese maker has a problem of excessive salting. He/she queries the MCDSS on his/her phone using the *excessive salting* decision tree. First, using the Action view (see Figure 7), he/she selects the three corrective actions, which are very effective with a good/average agreement between experts of the chain. Clicking on the button identifying possible side effects (see Figure 8), he/she understands that using the *Brine duration* lever could be risky as four other defects may appear. Therefore, he/she navigates in the Explanatory view (see Figure 6) to compare the two remaining recommended actions (acidify or dilute the brine) will be able to verify if the analytical value associated with the situation *High brine density* corresponds to his/her actual situation to choose the action he/she will use.

Collected know-how consistency checking is consolidated and enriched throughout the workflow presented in Figure 1. First note that Buche et al. (2019) proposed a method that enables collective mind mapping dedicated to this MCDSS. Consequently, decision trees which are outcomes of this collective mind mapping activity are already validated as they contain the consolidated knowledge of the food chain experts resulting from collective discussion. Secondly, it is possible to verify that recommended actions are relevant by checking the technicians' feedbacks (Specification 4) after implementation of the recommendations. If the actions are good, then the recommendation remains valid. Conversely, further investigation may be required to understand why a recommended action failed. Thirdly, criteria associated with
action efficiency assessments may contain information to verify
action's relevance. For instance, an advanced chain has defined
two criteria using the MCDSS: (1) StatisticalResults (yes/no),
meaning that statistical results validating the recommendation
have been obtained in the food chain; (2) BibliographicalResults
(yes/no), meaning that results published in a scientific paper
have validated the recommendation. In both cases, a link to a
complementary website may be embedded in the decision tree
branch to provide more information. Fourthly, it is possible to
determine if certain suggestions delivered by the MCDSS were not
executed. Indeed, this kind of action may be identified if the *Never
modality is associated with the "number of action implementations"
information (hasForObservations property associated with the
*Efficiency* concept). Explanations may be provided by analyzing
values associated with the *ConsequenceCriteria* associated with
the *Efficiency* concept. For instance, a given action has never
been executed because it could generate a sanitary risk or be
costly to implement. Fifthly, the collective mind mapping activity
conducted to create and maintain decision trees may identify
knowledge gaps. This means that in a given situation the experts
may not know which action to recommend or may disagree on
the action to recommend. Sometimes, they may know the action
to recommend to solve a given situation of interest, without being
able to explain why. In all of those cases, new experiments may be
conducted to unlock knowledge gaps. Learning improvement in
novel knowledge gap cases is a natural outcome of the collective
mind mapping activity, which is the first step of the MCDSS
 workflow process.

In terms of upscaling, more advanced chains manage around
a 100 decision trees and they will certainly increase to several
hundreds. But big data are not involved and no scalability problems
in terms of volumes should arise because we only represent
expert knowledge. The problem would arise if we were to seek
to represent the numerical experimental data so as to be able to
create/assess this expert knowledge. But this is beyond the scope
of this work.

5. Conclusion and perspectives

The perspectives of this project are numerous. In AOP
cheesemaking chain organizations, the priority has been toward
recommendations to correct organoleptic defects. But in the future
the method will enable us to create decision trees to recommend
actions to solve food safety problems or to achieve a given
food quality. Moreover, we will extend know-how representation
to the upstream part of the chain, including milk production.
New methodological challenges will be tackled to take spatio-
temporal knowledge representation into account. Advanced users
who conduct statistical analyses may like to be able to qualify
the causality relation between situations in a future version of
the MCDSS. This extension will be easy to design thanks to the
choice made in the ontology model to represent causality relations.
Another prospect will be to take new MCDSS sustainability criteria
into account. We will focus specifically on the environmental
impact of cheese production. Using Semantic Web languages
to implement the knowledge base will facilitate interoperability
management with new sources of information that are also

managed with those languages (Pénicaud et al., 2019; Cortesi et al., 2022a,b).

This MCDSS is a generic tool, which could potentially be used
in different food and bio-product chains. Encouraging preliminary
tests, as reported in Buche et al. (2019), have been conducted
in the cereal (couscous) and dairy sectors (instant milk powder).
Consequently, new dissemination activities will be conducted in
the future.

Data availability statement

The datasets presented in this study can be found in
online repositories. The names of the repository/repositories and
accession number(s) can be found at: https://doi.org/10.57745/
SEJP1B.

Ethics statement

Ethical approval was not required for the study involving
human participants in accordance with the local legislation
and institutional requirements. Written informed consent
to participate in this study was not required from the
participants in accordance with the national legislation and
the institutional requirements.

Author contributions

PB, SD, JCu, AO, and JCo contributed to conception and
design of the study. JCu and AO organized the database. PB and
JCo performed the statistical analysis. PB wrote the first draft of
the manuscript. PB and SD wrote sections of the manuscript. All
authors contributed to manuscript revision, read, and approved the
submitted version.

Funding

This study has been partially financed by the French Ministry
of Agriculture through the CASDAR system in the framework
of the DOCaMEx project, the France Relance project Docamex
an innovative digital tool at the service of cheese knowledge
and know-how (12-363-DNUM-CAAF-003), and the French
National Research Agency (ANR) in the framework of the D2KAB
project (ANR-18-CE23-0017).

Acknowledgments

The authors thank experts from ENIL schools and technical
centers devoted to AOP cheesemaking chains in France, namely
Comté, Reblochon, Emmental de Savoie, Cantal, and Salers, for
their help in the design and assessment of the methods and tools
presented in this paper. We are especially grateful to Manon Recour (Syndicat Interprofessionnel du Reblochon) and Virginie Cuccheval (Center Technique des Fromages Comtois) for the very rich exchanges throughout the MCDSS design process. We also thank Jérôme Fortin and David Manley who carefully read and comment this paper.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships

that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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<figure><img src="image_19.png" /></figure>

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OPEN ACCESS

EDITED BY

Marie-Angélique Laporte,
Bioversity International, France

REVIEWED BY

Ashni Sedani,
Accenture, United Kingdom
Angela Kranz,
Helmholtz Association of German Research
Centres (HZ), Germany

*CORRESPONDENCE

Anne E. Thessen

<figure><img src="image_20.png" /></figure>

annethessen@gmail.com

RECEIVED 05 April 2023

ACCEPTED 23 May 2023

PUBLISHED 13 June 2023

CITATION

Thessen AE, Cooper L, Swetnam TL, Hegde H, Reese J, Elser J and Jaiswal P (2023) Using knowledge graphs to infer gene expression in plants. *Front. Artif. Intell.* 6:1201002.
doi: 10.3389/frai.2023.1201002

COPYRIGHT

© 2023 Thessen, Cooper, Swetnam, Hegde, Reese, Elser and Jaiswal. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Using knowledge graphs to infer gene expression in plants

Anne E. Thessen¹*, Laurel Cooper ID ², Tyson L. Swetnam ID ³, Harshad Hegde ID ⁴, Justin Reese ID ⁴, Justin Elser ID ² and Pankaj Jaiswal ID ²

¹Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States, ²Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States, ³BIOS Institute, University of Arizona, Tucson, AZ, United States, ⁴Environmental Genomics and Systems Biology Division, Berkeley Lab (DOE), Berkeley, CA, United States

**Introduction:** Climate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood genotype-environment-phenotype (G×E×P) dynamics in order to increase ecosystem and agriculture resilience. An important part of predicting phenotype is understanding the complex gene regulatory networks present in organisms. Previous work has demonstrated that knowledge about one species can be applied to another using ontologically-supported knowledge bases that exploit homologous structures and homologous genes. These types of structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed through *in silico* experimentation.

**Methods:** We developed one such structure, a knowledge graph (KG) using information from Planteome and the EMBL-EBI Expression Atlas that connects gene expression, molecular interactions, functions, and pathways to homology-based gene annotations. Our preliminary analysis uses data from gene expression studies in *Arabidopsis thaliana* and *Populus trichocarpa* plants exposed to drought conditions.

**Results:** A graph query identified 16 pairs of homologous genes in these two taxa, some of which show opposite patterns of gene expression in response to drought. As expected, analysis of the upstream cis-regulatory region of these genes revealed that homologs with similar expression behavior had conserved cis-regulatory regions and potential interaction with similar trans-elements, unlike homologs that changed their expression in opposite ways.

**Discussion:** This suggests that even though the homologous pairs share common ancestry and functional roles, predicting expression and phenotype through homology inference needs careful consideration of integrating cis and trans-regulatory components in the curated and inferred knowledge graph.

KEYWORDS

knowledge graph (KG), plant genome, gene expression, ontology, phenotype

## Introduction

Climate change is already affecting ecosystems around the world and forcing us to explore ways to adapt to meet societal needs. This is particularly true in crop science where researchers are working to identify and predict genes and their resulting phenotypes under different environmental conditions in order to secure food production under a new climate regime (Thudi et al., 2021; Tian et al., 2021). Understanding gene/phenotype/environment relationships requires a large data set which can be difficult to collect, so most researchers focus on a small number of heavily studied species. The speed with which climate change
is progressing necessitates a massive scaling up of the number of species with understood G/P/E dynamics. The research and the knowledge gained in this area will also help human exploration in space, where plants will play an important role (Barker et al., 2023). Previous study has demonstrated that knowledge about one species can be applied to another using ontologically supported knowledgebases that exploit homologous structures and orthologous genes (Naithani et al., 2020). These types of knowledge structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed.

An important part of predicting phenotype is understanding the complex gene regulatory networks present in plants. This study will focus on the promoter region, the 5' cis-regulatory regions of the homologs. This region is a portion of the DNA strand that is "upstream" from the 5' end of the gene's coding start site and provides selective binding sites for trans-acting factors such as transcription factors, repressors, and activators that regulate the expression of the gene (Liu et al., 1999). These regions are just one element of the gene expression process. Studying the expression of trans-acting factors is important for understanding the spatiotemporal dynamics of molecular interactions that help adapt or overcome stress. Resources such as the Gene Ontology (The GO Consortium, 2021), Planteome (Cooper et al., 2018), Plant Reactome (Naithani et al., 2020), and KnetMiner (Hassani-Pak et al., 2021) contain much of what we know about gene function, gene regulatory networks, and phenotypes in the form of Gene X regulates Gene Y and Gene Y impacts phenotype Z, but the contextual effect of environmental conditions under which these interactions happen is almost always not included in the annotations. Not all plants and their genes are characterized in detail, but if it is included, the environmental context is usually detailed only in the metadata. Investigations that use protein domain identification and gene homology-based methods to infer the functional role a gene carries out in a given species may be overlooking the spatial and temporal dynamics of mRNA expression that determines whether a gene product (protein) will be present at the desired time and place to serve a molecular function. The interactive nature of genes, environments, and phenotypes requires a data structure that can represent qualitative relationships (e.g., "has phenotype" or "regulates") and integrate heterogeneous data types in a single, queryable framework. One of these data structures is a knowledge graph (KG) (Sheth et al., 2019).

A graph is made up of objects (nodes) and the relationships (edges) between those objects and, in this context, represents what we know about how biological and environmental entities (objects) interact. Rather than store data in a table or database, a knowledge graph stores the synthesized knowledge we gain from the data, e.g., Gene X has phenotype Y. As more knowledge is added to the graph, more complex queries, network analyses, and inferences can be made. Important examples include the use of knowledge graphs in rare disease diagnosis in humans (Zemojtel et al., 2014), drug repurposing (Reese et al., 2021), improving cancer treatment (Gogleva et al., 2022), and meta-analyses (Tiddi et al., 2020). KGs used for translational science rarely contain environmental exposures even though we know environmental conditions are an important part of gene expression dynamics. The exact way to model exposures in a KG is still under development (Chan et al.,

2023). A KG containing information about plant genomics and phenomics under different environmental conditions can be used to generate hypotheses in silico for targeting, thereby reducing the number of in vivo experiments that need to be conducted, saving time and resources.

This study examines gene expression patterns in response to drought conditions in four plant species, such as *Arabidopsis thaliana*, *Zea mays*, *Sorghum bicolor*, and *Populus trichocarpa*. The central motivation of this study is to assess the feasibility of using homologs to make predictions about gene expression in multiple species.

# Materials and methods

## Data description

### Planteome

The Planteome (https://planteome.org/) is a centralized web portal with a suite of interrelated ontologies for plants and a database of plant genomics data, annotated to the ontology terms (Cooper et al., 2018). In the October 2020 release (version 4.0), the Planteome database included approximately 60,000 ontology terms and more than 3 million data objects, which are connected to ontology terms through approximately 20 million associations. The Planteome database has plant genomic information covering 125 plant taxa. The data available in the Planteome and annotated with ontology terms, include plant gene expression data, traits, phenotypes, genomes, and germplasm sources.

The ontologies developed in-house by the Planteome project include the Plant Ontology (PO; Cooper et al., 2018; Walls et al., 2019), which describes plant anatomical structures and developmental stages, the Plant Trait Ontology (TO) for traits and phenotypes, and the Plant Experimental Conditions Ontology (PECO), which describes experimental conditions and plant exposures. In addition to these, the Planteome hosts the collaborator reference ontologies—the Gene Ontology (GO; The GO Consortium, 2021), Phenotype and Trait Ontology (PATO; Gkoutos et al., 2018), and also a number of species-specific trait dictionaries developed by the Crop Ontology (CO; Shrestha et al., 2010; Arnaud et al., 2020). In the current release, the Planetome includes 11 of the CO trait dictionaries, mapped to the TO.

GO annotations were computationally generated for new species using InParanoid and InterProScan (Shulaev et al., 2011; Myburg et al., 2014). InParanoid was used to predict gene orthology based on the *Arabidopsis thaliana* associations generated by TAIR (Reiser et al., 2022). InterProScan was used to add GO annotations to genes via inference by analyzing protein families and domain mappings (Paysan-Lafosse et al., 2023).

## EMBL-EBI expression atlas

The EMBL-EBI Expression Atlas (GXA) can be accessed online and is part of the European Bioinformatics Institute (Papatheodorou et al., 2020). It contains manually curated and analyzed data from over 900 plant experiments that have been re-analyzed using the latest versions of the reference plant genome
assembly and annotations and by deploying a standardized analysis workflow. Every experiment is fully documented with metadata and provenance.

Gene expression data were downloaded as a table from GXA after searching for desired species and environmental conditions. Data were filtered to include only genes that had statistically different gene expressions (p<0.05) compared with a baseline that was <-1 or >1. Genes with positive differential expression were annotated as having increased expression. Genes with negative differential expression were annotated as having decreased expression. The tabulated data were annotated with additional ontology terms where appropriate and made available in GitHub for graph construction.

## Creating the graph

The graph was created by combining data from Planteome, the GXA, PO, TO, GO, and PECO using the tools available at KG-Hub (Caufield et al., 2023). First, the data and mapping files were downloaded from their respective data repositories. GO-Basic and NCBI Tax-Slim were downloaded from the OBO Foundry in javascript object notation (JSON) format. PO and TO were downloaded from the OBO Foundry in owl format and transformed to JSON using ROBOT (Jackson et al., 2019). Data files containing information about *Sorghum bicolor*, *Zea mays*, *Oryza sativa*, *Populus trichocarpa*, and *Arabidopsis thaliana* were downloaded from Planteome servers in GAF format. Data files containing differential gene expression data involving *Sorghum bicolor*, *Zea mays*, *Oryza sativa*, *Populus trichocarpa*, and *Arabidopsis thaliana* in drought and saline environments were downloaded from the GXA. Several mapping files were used to normalize gene and trait identifiers. Rice gene identifiers were mapped to *Oryza sativa* v7.0 using the ID converter file from the Rice Annotation Project Database (Ouyang et al., 2007; Sakai et al., 2013). Maize gene identifiers were mapped to Zm-B73-REFERENCE-NAM-5.0 assembly using a mapping file that includes all B73 assembly versions and includes the DAGchain analysis which was obtained from MaizeGDB (Portwood et al., 2019; EMBL-EBI). Poplar gene identifiers were mapped to the reference genome using a mapping file from Gramene (Tello-Ruiz et al., 2018). *Sorghum* gene names were normalized to *Sorghum bicolor* v3.1.1 (McCormick et al., 2018). Plant traits and phenotypes were annotated with TO terms using a look-up dictionary file. Second, each of the data files was transformed into standardized nodes and edges in a tsv file using custom scripts. These scripts normalized gene and trait identifiers using ontologies and the provided mapping files and annotated every entity with a Biolink semantic type (Table 1), and relationships between entities were described using Biolink predicates (Table 2). The graph was assembled according to the Biolink model, which provides standard semantic types and relationships for biological entities (Unni et al., 2022).

There was not enough overlapping expression data to include *O. sativa* or saline environments in this analysis, but they were included in the graph.

<table><caption>TABLE 1 Identifiers and Biolink semantic types assigned to elements of the graph.</caption><thead><tr><td>Biological element</td><td>Identifier</td><td>Biolink type</td></tr></thead><tbody><tr><td>Plant part</td><td>PO</td><td>Anatomical entity</td></tr><tr><td>Growth stage</td><td>PO</td><td>Life stage</td></tr><tr><td>Plant trait</td><td>TO</td><td>Phenotypic feature</td></tr><tr><td>Zea mays gene</td><td>Zm00001eb IDs</td><td>Genomic entity</td></tr><tr><td>Sorghum bicolor gene</td><td>Sobic IDs</td><td>Genomic entity</td></tr><tr><td>Oryza sativa gene</td><td>LOC_Os IDs</td><td>Genomic entity</td></tr><tr><td>Populus trichocarpa gene</td><td>POPTR IDs</td><td>Genomic entity</td></tr><tr><td>Experimental condition</td><td>PECO</td><td>Environmental exposure</td></tr><tr><td>QTL</td><td>Gramene IDs</td><td>Genomic entity</td></tr><tr><td>Cultivar</td><td>NCBITaxonomy</td><td>Organismal entity</td></tr><tr><td>Taxon</td><td>NCBITaxonomy</td><td>Organism taxon</td></tr><tr><td>Cellular component</td><td>GO</td><td>Cellular component</td></tr><tr><td>Molecular function</td><td>GO</td><td>Molecular function</td></tr><tr><td>Biological process</td><td>GO</td><td>Biological process</td></tr><tr><td>Germplasm</td><td>GRIN and IRIC IDs</td><td>Organismal entity</td></tr></tbody></table>

<table><caption>TABLE 2 Edges and their Biolink predicates.</caption><thead><tr><td>Subject entity</td><td>Predicate type</td><td>Object entity</td></tr></thead><tbody><tr><td>Genomic entity</td><td>In taxon</td><td>Organism taxon</td></tr><tr><td>Genomic entity</td><td>Active in</td><td>Cellular component</td></tr><tr><td>Genomic entity</td><td>Regulates</td><td>Biological process</td></tr><tr><td>Genomic entity</td><td>Enables</td><td>Molecular function</td></tr><tr><td>Genomic entity</td><td>Expressed in</td><td>Anatomical entity</td></tr><tr><td>Genomic entity</td><td>Expressed in</td><td>Life stage</td></tr><tr><td>Genomic entity</td><td>Has phenotype</td><td>Phenotypic feature</td></tr><tr><td>Genomic entity</td><td>Orthologous to</td><td>Genomic entity</td></tr><tr><td>Organism taxon</td><td>Has phenotype</td><td>Phenotypic feature</td></tr><tr><td>Organismal entity</td><td>In taxon</td><td>Organism taxon</td></tr><tr><td>Organismal entity</td><td>Has phenotype</td><td>Phenotypic feature</td></tr><tr><td>Environmental exposure</td><td>Increases expression of</td><td>Genomic entity</td></tr><tr><td>Environmental exposure</td><td>Decreases expression of</td><td>Genomic entity</td></tr></tbody></table>

The third and final step merged the transformed tsv files into a deduplicated list of nodes and edges in KGX format. The final graph consisted of over 400,000 nodes and over 5,000,000 edges and contained additional data from EOLTraitbank that was not used in this study (Figure 1). Specific information about quantitative and qualitative plant phenotypes was represented as an edge property (Figure 2).

## Querying the graph

The merged node file and edge file were uploaded into Neo4j for exploration and query. A Cipher query (Box 1) was used
<figure><img src="image_21.png" /><figcaption>FIGURE 1</figcaption></figure>

Structure of the knowledge graph. Data are transformed using ontologies and the Biolink model to form a graph. Nodes (gray boxes) are labeled with Biolink semantic type and edges (gray arrows) are labeled with Biolink predicate. Arrows indicate directionality.

<table><caption>TABLE 3 Gene expression in *A. thaliana* and *P. trichocarpa* homologous genes under drought conditions.</caption><thead><tr><td><i>A. thaliana</i> Gene*</td><td><i>P. trichocarpa</i> Gene*</td><td>Gene function (from Planteome)</td></tr></thead><tbody><tr><td>AT3G49960 ↓</td><td>POPTR_007G053400v3 ↑</td><td>Peroxidase activity, response to oxidative stress, heme binding</td></tr><tr><td>AT1G70710 ↓</td><td>POPTR_010G109200v3 ↓</td><td>Catalytic activity, hyrolase activity, carbohydrate metabolic process</td></tr><tr><td>AT1G10550 ↓</td><td>POPTR_014G115000v3 ↓</td><td>Xyloglucan metabolism, hyrolase activity, carbohydrate metabolic process, cell wall biogenesis</td></tr><tr><td>AT5G67400</td><td>POPTR_007G053400v3 ↑</td><td>Peroxidase activity, response to oxidative stress, heme binding, hydrogen peroxide catabolic process</td></tr><tr><td>AT5G23210 ↓</td><td>POPTR_005G091700v3 ↓</td><td>Proteolysis, serine-type carboxypeptidase activity</td></tr><tr><td>AT1G67750 ↓</td><td>POPTR_008G182200v3 ↓</td><td>Pectate lyase activity, metal ion binding</td></tr><tr><td>AT2G39530 ↓</td><td>POPTR_010G205300v3 ↑</td><td>iron/sulfur cluster binding</td></tr><tr><td>AT5G13140 ↓</td><td>POPTR_003G167100v3 ↓</td><td>Response to nematode, pectate lyase activity, metal ion binding</td></tr><tr><td>AT1G11580 ↓</td><td>POPTR_011G025400v3 ↑</td><td>Enzyme inhibitor activity, pectinesterase activity, cell wall modification, rRNA N-glycosylase activity, aspartyl esterase activity, toxin activity, defense response</td></tr><tr><td>AT3G27400 ↓</td><td>POPTR_001G339500v3 ↑</td><td>Response to nematode, pectate lyase activity, metal ion binding</td></tr><tr><td>AT4G02330 ↓</td><td>POPTR_014G127000v3 ↑</td><td>Enzyme inhibitor activity, pectinesterase activity, cell wall modification, response to stress, aspartyl esterase activity</td></tr><tr><td>AT5G20630 ↓</td><td>POPTR_006G142600v3 ↓</td><td>Manganese ion binding, nutrient reservoir activity</td></tr><tr><td>AT4G26260 ↑</td><td>POPTR_018G069700v3 ↓</td><td>Iron ion binding, inositol oxygenase activity, syncytium formation, L-ascorbic acid biosynthetic pathway</td></tr><tr><td>AT2G44990 ↑</td><td>POPTR_014G056800v3 ↓</td><td>Oxidoreductase activity, secondary shoot formation, carotene catabolic process, strigolactone biosynthetic process, xanthophyll catabolic process, metal ion binding</td></tr><tr><td>AT1G70710 ↓</td><td>POPTR_010G109200v3 ↓</td><td>Cellulase activity, cell wall modification, hydrolase activity</td></tr><tr><td>AT1G12940 ↑</td><td>POPTR_015G081500v3 ↓</td><td>Transmembrane transport</td></tr></tbody></table>

to find all of the homologous genes that had been documented to have differential gene expression in either a drought or a saline environment (Supplementary material 1, 2). The saline environment did not return overlapping data.

Genes returned from the query for the drought environment were compared based on GO annotations (Supplementary material 3), but this also did not give enough data to make conclusions using PANTHER (Supplementary material 4).

## Comparing promoter regions

We collected 5'-regulatory regions of the identified genes (700-900 bp) using BioMart in the Gramene database (Spooner et al., 2012) and searched for potential transcription factor-binding sites using PlantPAN (Chow et al., 2016). Using these data (Supplementary material 5), we created a matrix comparing the occurrence of each transcription factor in the binding site

<footnote>* ↓ Indicates decreased expression and ↑ indicates increased expression.</footnote>
of each gene pair and made note of which were or were not held in common. We used ClustVis (Metsalu and Vilo, 2015) to examine the similarity between the transcription factor-binding sites for each of the *Populus* and *Arabidopsis* gene pairs using PCA. A total of 12 transcription factor-binding sites (AT-Hook, bHLH, C2H2, Dehydrin, Dof, GATA, Homeodomain, Myb/SANT, NF-YB, TBP, Trihelix, and ZF-HD) were present in the promoter

regions of all the genes studied and thus were removed from clustering analysis. The same data were fed into Morpheus (Müller et al., 2008) for hierarchical clustering performed with default parameters using One minus Pearson's correlation and complete linkage methods on the TF-binding site annotations. Additional similarity matrices were created using Pearson's correlation metric to separately examine the TF-binding site annotations for genes with similar and contrasting expression profiles. The correlation heatmap colors were adjusted for visualization purposes.

<figure><img src="image_22.png" /></figure>

## Data availability

The merged KG data are hosted on the CyVerse DataCommons (https://datacommons.cyverse.org/browse/iplant/home/shared/genophenoenvo). The KG data are available for direct download

### BOX 1 Cipher query.

MATCH (e {id:'PECO:0007404l')-[r]->(g),(g)-[q:'biolink: orthologous_to']- (h), (e {id:'PECO:0007404'})-[s]-> (h) RETURN *

<figure><img src="image_23.png" /></figure>
<figure><img src="image_24.png" /><figcaption>FIGURE 4</figcaption></figure>

Similarity of the transcription factor-binding sites in the promoter region of *Populus* and *Arabidopsis* homologous gene pairs. Poplar (POPTR) and *Arabidopsis* (AT) genes were grouped into their homolog pairs and whether they had similar or contrasting gene expression when exposed to drought. This figure shows that the promoter regions of pairs with contrasting expressions were less similar (blue) and the promoter regions of pairs with similar expressions were more similar (red).

or remote visualization via CyVerse WebDav service (https://data.cyverse.org/dav-anon/iplant/commonst์/community_released/genophenoenvo/kg/) using visualization software such as Neo4J. The Python code used to create the graphs is publicly hosted on GitHub (https://github.com/genophenoenvo/knowledge-graph). The final merged KG includes two tab-separated value (tsv) files which include the edges and nodes.

## Results

The graph query returned 62 pairs of homologous genes from *Sorghum bicolor*, *Zea mays*, *Arabidopsis thaliana*, and *Populus trichocarpa* (Supplementary material 6), but only 16 pairs between *A. thaliana* and *P. trichocarpa* had documented similar (8) and differential (8) expressions in drought conditions (Table 3). All of
<figure><img src="image_25.png" /></figure>

the genes with similarly expressed pairs had decreased expression. Expression data for the 16 homologous pairs of *A. thaliana* and *P. trichocarpa* came from two studies in GXA (de Simone et al., 2017; Filichkin et al., 2018).

Based on the predicted transcription factor-binding sites in the promoter regions, the *Populus* genes in the differentially expressed homolog pairs cluster separately from the other *Populus* and *Arabidopsis* genes (Figure 3). This difference is driven by a group of 11 transcription factor-binding sites that are absent in the promoter regions of the subset of divergent *Populus* genes (RAV, MIKC, NAM, G2-like, CPP, ARR-B, tify, TALE, NF-YC, ERF, and NF-YA). The separation of these genes cannot be explained by the taxon or the study providing the data (which overlaps the taxon).

There were seven *Populus* genes that clustered away from the others. All but one (POPTR_014G056800v3 involved in strigolactone biosynthesis) were hypothetical proteins (According to Gramene). GO annotations for these genes clustered around transporter activity, catabolic activity, response to stress, binding, and catalytic activity. The 11 transcription factors absent in the binding sites of the *Populus* genes include proteins involved in plant stress response in *Arabidopsis* (According to UniProt).

A comparison of the promoter regions between homolog pairs showed that homologs that were expressed similarly had more similar promoter regions than pairs that were expressed differentially (Figure 4).

Separate comparisons of the promoter regions from gene pairs with contrasting expression profiles also show that gene pairs with similar expression had more similar promoter regions (Figure 5A) and gene pairs with contrasting expression had less similar promoter regions (Figure 5B).

Discussion

This study shows that one can use *in silico* experiments to predict gene expression in drought conditions using homologous gene families in some species pairs but not all. This study supports previous findings that in some cases, promoter regions evolve separately from the coding region of the genes they regulate (Tirosh et al., 2008). Thus, we can translate knowledge about gene expression in one species to another, but we need to include these dynamics in the data infrastructures we use to make this translation, in this case, KGs. Many data structures link a gene to a phenotype, trait, or disease without specific expression information. The current representation of differential gene expression links exposure to a chemical or a drug to the increased or decreased expression of a specific gene in the context of toxicology and drug development (Fecho et al., 2022; Unni et al., 2022). Gene regulatory networks are represented as mini-networks of genes that influence other genes (The GO Consortium, 2021), but many of these networks are still unknown in plants. In the short term, *in silico* KG experiments involving gene expression can be improved by including empirically validated gene expression patterns of homologs.

Gene regulatory networks in plants have been developed using a combination of experimental and computational approaches (Kulkarni and Vandepoele, 2020). Methods combining high-throughput DNA sequencing (ChIP-seq) and expression data have successfully revealed the detailed regulatory networks controlling flowering (Chen et al., 2018) but are difficult to scale. Methods such as ATAC-seq and DAP-seq are more scalable but only reveal a partial picture of the regulatory network (O'Malley et al., 2016;
Maher et al., 2018). KGs can be used to infer regulatory networks at scale, but the quality is highly dependent on the data used to build the KG. The advantage of applying a KG is the ability to integrate incredibly heterogeneous data in a single graph, thus modeling regulatory networks in their larger biological context. An example of this application is the relatively new field of “network medicine” that uses KGs to examine the progression of disease (Silverman et al., 2020). The main disadvantage of KGs in this application is that large amounts of computable data and domain-specific knowledge models are needed to create a graph of this type. Many disciplines do not have these resources available. While KGs can infer gene regulatory networks, these networks should always be confirmed using established experimental and computational approaches.

These opposing gene expression patterns are not a concern for researchers who are only interested in finding a list of genes that are potentially important in a specific context. It is not until one needs to generate hypotheses about the impact of the environment on the biological function that more complex graph representations become needed. If we are to incorporate the effect of the environment, we need to know more than that Gene X has phenotype Y. We need to know if the environmental effect increases or decreases the expression of the gene and the biological consequences of that change in expression. In some cases, we may only know that an environment is linked to a specific phenotype without knowing the underlying mechanism. This information can still add useful knowledge to the graph. In some cases, the graph itself can be used to generate hypotheses about the interplay between genes, biological processes, molecular functions, cellular components, and an observed phenotype.

Despite having the graph available to quickly explore the data and locate genes of interest, the workflow for comparing the promoter regions required substantial manual intervention. In this instance, we only had 16 gene pairs to explore, but scaling up these types of analyses will require the ability to traverse data annotated with gene identifiers and gene coordinates. Future studies should include extending the graph model to include these data types.

The semantic representation of the effect of environmental exposure on gene expression is more straightforward for the effects of a chemical or a substance, such as phenol or rubber cement. Data can be collected in the laboratory using model organisms, and the results added to the graph for analysis and translational research. Everyday environmental exposures are rarely this simple and frequently involve exposure to many types of substances in different contexts, such as climate or socioeconomic status. Future studies may need to develop ontologies and semantic representations for these more complex exposures.

Our observations support our hypothesis and justify the extension of our KG to include TF-binding site annotations and the actual TF genes, which are either known empirically or are supported by co-expression network analysis. In future, an investigation of conservation vs. non-conservation of cis- and trans-regulatory regions of genes may improve the

understanding of interspecies and intraspecies responses to stress and adaptation.

## Data availability statement

The merged KG data are hosted on the CyVerse DataCommons (https://datacommons.cyverse.org/browse/iplant/home/shared/genophenoenvo). The KG data are available for direct download or for remote visualization via CyVerse WebDav service (https://data.cyverse.org/dav-anon/iplant/commonsexplore/techor released/genophenoenvo/kg/) using visualization software such as Neo4J. The python code used to create the graphs are publicly hosted on GitHub (https://github.com/genophenoenvo/knowledge-graph). The final merged KG includes two tab-separated value (tsv) files which include the edges and nodes.

## Author contributions

AT developed and framed research question(s), analyzed data, contributed to data analysis, developed software, contributed to writing and revising the paper, and project administration and management. HH contributed to data analysis. TS contributed to data analysis and contributed to writing and revising the paper. JR developed software, validated results or software, developed and framed research question(s), and contributed to writing and revising the paper. LC contributed to data analysis, project administration and management, and contributed to writing and revising the paper. PJ developed and framed research question(s), analyzed data, contributed to data analysis, and contributed to writing and revising the paper. JE contributed to data analysis, developed software, validated results or software, and contributed to writing and revising the paper. All authors contributed to the article and approved the submitted version.

## Funding

This study was supported by the National Science Foundation grant awards #1940330, #1939945, #1940059, and #1940062. CyVerse is based upon work supported by the National Science Foundation grant awards #0735191, #1265383, and #1743442.

## Acknowledgments

The authors would like to thank Sierra Moxon and Harry Caufield for their technical assistance and helpful conversation. The preprint for this paper can be found at https://doi.org/10.5281/zenodo.7764762.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated

organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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# Appendix

All supplementary files can be accessed in GitHub under a CC-0 license (https://github.com/diatomsRcool/supplementary Region).

1. drought_expression.tsv

2. drought_genes.tsv

3. GO_annotations.tsv

4. panther_results folder

5. promoter_region_clustvis_data0.tsv

6. orthologous_genes.tsv

Clustvis analysis is at https://biit.cs.ut.ee/clustvis/?s=IWJNurmUtGWZoMt.
<figure><img src="image_26.png" /></figure>

Check for updates

OPEN ACCESS

EDITED BY
Catherine Roussey,
INRAE Occitanie Montpellier, France

REVIEWED BY
Claire Nédellec,
INRAE Centre Ile de France, France
Nadia Yacoubi Ayadi,
Research Centre Inria Sophia Antipolis
Méditerranée, France
Jean Charlet,
Assistance Publique Hopitaux De Paris, France

*CORRESPONDENCE
Aarón Ayllón-Benitez
✉ aaron.ayllon@basf.com

RECEIVED 25 December 2022

ACCEPTED 26 May 2023

PUBLISHED 19 June 2023

**CITATION**
Ayllón-Benitez A, Bernabé-Diaz JA, Espinoza-Arias P, Esnaola-Gonzalez I, Beeckman DSA, McCaig B, Hanzlik K, Cools T, Castro Iragorri C and Palacios N (2023) EPPO ontology: a semantic-driven approach for plant and pest codes representation.
*Front. Artif. Intell.* 6:1131667.
doi: 10.3389/frai.2023.1131667

**COPYRIGHT**
© 2023 Ayllón-Benitez, Bernabé-Diaz, Espinoza-Arias, Esnaola-Gonzalez, Beeckman, McCaig, Hanzlik, Cools, Castro Iragorri and Palacios. This is an open-access article distributed under the terms of the *Creative Commons Attribution License (CC BY)*. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# EPPO ontology: a semantic-driven approach for plant and pest codes representation

Aarón Ayllón-Benitez¹*, José Antonio Bernabé-Diaz¹,
Paola Espinoza-Arias¹, Iker Esnaola-Gonzalez¹,
Delphine S. A. Beeckman², Bonnie McCaig³, Kristin Hanzlik⁴,
Toon Cools⁵, Carlos Castro Iragorri⁶ and Nicolás Palacios⁶

¹BASF Digital Solutions, Madrid, Spain, ²BASF Belgium Coordination Center CommV, Innovation Center Gent, Ghent, Belgium, ³BASF Corporation, Raleigh, NC, United States, ⁴BASF SE Data Management and Data Governance, Global Research Services APR/HP, Limburgerhof, Germany. ⁵TalentBay, Brussels, Belgium, ⁶Linking Data SAS, Bogotá, Colombia

The agricultural industry and regulatory organizations define strategies and build tools and products for plant protection against pests. To identify different plants and their related pests and avoid inconsistencies between such organizations, an agreed and shared classification is necessary. In this regard, the European and Mediterranean Plant Protection Organization (EPPO) has been working on defining and maintaining a harmonized coding system (EPPO codes). EPPO codes are an easy way of referring to a specific organism by means of short 5 or 6 letter codes instead of long scientific names or ambiguous common names. EPPO codes are freely available in different formats through the EPPO Global Database platform and are implemented as a worldwide standard and used among scientists and experts in both industry and regulatory organizations. One of the large companies that adopted such codes is BASF, which uses them mainly in research and development to build their crop protection and seeds products. However, extracting the information is limited by fixed API calls or files that require additional processing steps. Facing these issues makes it difficult to use the available information flexibly, infer new data connections, or enrich it with external data sources. To overcome such limitations, BASF has developed an internal EPPO ontology to represent the list of codes provided by the EPPO Global Database as well as the regulatory categorization and relationship among them. This paper presents the development process of this ontology along with its enrichment process, which allows the reuse of relevant information available in an external knowledge source such as the NCBI Taxon. In addition, this paper describes the use and adoption of the EPPO ontology within the BASF's Agricultural Solutions division and the lessons learned during this work.

**KEYWORDS**

EPPO codes, ontologies, plants, seeds, diseases, pests, crop protection, chemical industry
# 1. Introduction

In agriculture, reducing crop losses caused by organisms such as pests and diseases is crucial. In 2021 it was estimated that up to 40 percent of global crop production is lost annually due to pests (IPPC Secretariat et al., 2021), leading to huge economic costs, low availability and quality of food and raw materials, and environmental pollution, among others negative effects. In the last decades several organizations and companies have been working to provide regulations, technologies and products to prevent and mitigate damage caused by pests outbreaks. Therefore, to have a common and consistent way of identifying plants and pests when providing their solutions, such organizations and companies use the EPPO coding system as the worldwide reference.

The EPPO coding system was created and maintained by Bayer in the 1970s and then transferred to the European and Mediterranean Plant Protection Organization (EPPO) in 1996. In 2014, this system was released as the EPPO Global Database,<fnref n="1" /> freely available under an open data license and in several formats (e.g., XML, SQLite, TXT). In this coding system, an EPPO code is a unique identifier for plants, pests, and pathogens which is built as combinations of 5 to 6 letters. EPPO codes mainly cover taxonomic codes but also non-taxonomic codes. On the one hand, taxonomic codes refer to those EPPO codes developed for biological organisms or groups of biological organisms based on their scientific naming and classification in groups known as “taxa”. On the other hand, non-taxonomic codes represent a smaller set of codes describing entities of interest to those working in the field of plant protection products (PPP). Developed with the aim to describe the use of a PPP, they facilitate communication among National Plant Protection Organizations and other stakeholders involved in the registration of plant protection products. Further details on the information available for taxonomic and non-taxonomic codes are given in Figure 1.

In addition, EPPO codes are hierarchically organized and, specifically within the taxonomic portion of the EPPO Global Database, each taxonomic level has a unique code which is mainly derived from the corresponding scientific name of that level. Whereas, in the case of non-taxonomic codes, they are built following more concrete rules described in the EPPO Standard PP1/248 (European and Mediterranean Plant Protection Organization, 2022). Currently, the EPPO database contains basic information of more than 90,000 species and detailed information for more than 1,700 pests and diseases. Even so, the coding system is dynamic and new codes constantly are added [on average more than 2,000 new codes per year (Roy, 2019)].

BASF is one of the large companies consuming EPPO codes as a standard for plant pest identification. BASF applies EPPO codes in the research and development of new agricultural products (such as insecticides, fungicides, herbicides, seeds, among others) and tools (e.g., a system for disease and pest recognition, and tailored recommendation of treatments based on in-field conditions<fnref n="2" />). Nevertheless, the availability of multiple format files to extract EPPO codes data requires additional processing steps to consume

them. To solve this, EPPO Global Database provides a fixed REST API to extract data; however, this limits the flexibility of data consumption. Therefore, consuming the information of EPPO codes requires accessing to these different files and API requests to get the complete information needed. To face these limitations and provide more capabilities to EPPO codes, we developed an ontology to represent them in a formal semantic language. Ontologies allow to homogeneously structure and harmonize data without ambiguities, infer new knowledge, and enrich data with external knowledge sources (Studer et al., 1998). The adoption of ontologies in large companies like BASF allows sharing and reusing common parts of knowledge across the organization, facilitating data reusability and interoperability.

In this manuscript, we detail the process followed to build the EPPO ontology and the lessons learned during this work. We begin by describing the related work (Section 2). Then, we explain the ontology development process along with its automatic creation pipeline and the enrichment step (Section 3). Next, we describe in detail the main ontology elements (Section 4) and illustrate how BASF is using the EPPO ontology (Section 5). Finally, we outline our conclusions and discuss future work (Section 6).

# 2. Related work

In the context of this work, some ontologies have been reported in the literature. From a general point of view, the most relevant ontology for our work is the NCBITaxon ontology (Bastian et al., 2013) which allows describing organism names and taxonomic lineages from the NCBI taxonomy database (Federhen, 2012). This ontology provides a comprehensive collection of organisms including the taxonomic levels (e.g., kingdom, order, family, etc.) that are also detailed in the EPPO codes. However, it does not include further information, provided by the EPPO Global Database representation such as code, EPPO code phytosanitary categorization, categorization status, code type, host-pests relationship, etc.

Focusing on plant pests and diseases, few ontologies have been reported to represent the crop domain including pests. The Pest Crop Ontology (PCO) (Damos et al., 2017) provides a high-level representation of crops, pests, treatments, and the relations among them. To provide further details than those provided by PCO, the Pests in Crops and their Treatments Ontology (PCT-O) (Lacasta et al., 2018) was developed to describe the conditions required by a pest to produce outbreaks and the restrictions on the treatments. In terms of describing crop management details, the Crop Planning and Production Process Ontology (C3PO) (Darnala et al., 2021) allows representing plot management and crop itineraries by means of several modules which encapsulate high-level information about plants, crop management, potential diseases and pests, treatments, among others. However, none of the aforementioned ontologies include further details on pests such as a consistent taxon, non-taxon, and commodity group classification, synonyms, preferred names, and granular details about them. Finally, the Plant Health Threat Ontology (Alomar et al., 2015, 2016) formally represents plant pest and disease names and the relations among them and to other concepts like hosts, symptoms, crops, etc. This ontology reuses the Plant Ontology (Cooper et al., 2013), and concepts

<footnote n="1">1 https://gd.eppo.int</footnote>

<footnote n="2">2 https://www.xarvio.com/global/en.html</footnote>
<figure><img src="image_27.png" /><figcaption>FIGURE 1
Taxonomic vs. non-taxonomic codes.</figcaption></figure>

coming from multilingual sources such as UniProt Taxon, EPPO Global Database and DBPedia. In terms of EPPO information, a recent report (European Food Safety Authority et al., 2021) details that 133 plant pests are included in the current ontology version. Unfortunately, this ontology is not publicly available; therefore, it is not possible to analyze it and, consequently, the plant pests that it represents cannot be reused.

# 3. Development of the EPPO ontology

The ontology was built following the development lifecycle proposed in the BASF Governance Operational Model for Ontologies (GOMO) (Iglesias-Molina et al., 2022). This lifecycle was derived from the Linked Open Terms (LOT) methodology (Poveda-Villalón et al., 2022), which is a methodology based on agile techniques and comprises several stages and activities for the ontology construction. The GOMO lifecycle includes four main stages which will be described in the following subsections.

## 3.1. Requirements and kick off

This stage intends to define and gather all the requirements and basic elements necessary for the ontology development. Therefore, the first activity we undertook was to define the purpose and scope of the EPPO ontology. To do so, we collected the feedback of several domain experts from our Agricultural Solutions division and agreed that the purpose covered by this ontology is the

representation of the information available in the EPPO Global database and the relationships between the concepts identified therein. Therefore, this ontology is not limited to be used by a specific application, but has been developed in the interest of having a single, harmonized, and flexible source of the EPPO code system information. As for the ontology scope, we agreed to include taxonomic and non-taxonomic codes along with their code types, parent-child relationship per code, phytosanitary categorization, and their taxonomy level (if applicable). Further details on the information available in taxonomic and non-taxonomic classifications is presented in Figure 1.

The second activity we performed was to define the requirements that the ontology must fulfil. To this end and based on the needs of the domain experts, we posed several competency questions (Grüninger and Fox, 1995) that guided us during the development process. Table 1 shows an excerpt of the competency questions. A complete list is provided in Section 1 of the Supplementary material.

The third activity we executed was to identify and analyze the structure of the relevant data sources in relation to the ontology purpose and scope. We identified several files in the EPPO Data Services; however, we focused particularly on three of them:

(a) the SQLite database file<fnref n="3" /> containing EPPO codes for taxonomic and non-taxonomic organisms, including data such as their preferred names, synonyms in several languages, creation and modification dates, among others; (b) the REST API service<fnref n="4" /> that provides direct access to information specific to EPPO codes,

<footnote n="3">3 https://data.eppo.int/files/sqlite_all.zip</footnote>

<footnote n="4">4 https://data.eppo.int/documentation/rest#collapse1</footnote>
<table><caption>TABLE 1 Excerpt of competency questions from the EPPO ontology.</caption><thead><tr><td>Identifier</td><td>Competency question</td><td>Expected answer</td></tr></thead><tbody><tr><td>CQ1</td><td>Which taxonomic code is associated with non-taxonomic code "TRZAW"?</td><td>https://ontology.basf.net/ontology/BASF/Bioscience/EPPO/TRZAX</td></tr><tr><td>CQ2</td><td>List the non-taxonomic EPPO codes + names associated with Species ("Brassica juncea") or EPPO Code ("BRSJU") (Is this species part of any crop group?)</td><td>non-taxonomic EPPO code: https://ontology.basf.net/ontology/BASF/Bioscience/EPPO/BRSJU, non-taxonomic EPPO name: leafy brassica crops; non-taxonomic EPPO code: https://ontology.basf.net/ontology/BASF/Bioscience/EPPO/3MUSC, non-taxonomic EPPO name: mustard crops</td></tr><tr><td>CQ3</td><td>Do "BRSJU" and "BRSRW" belong to a common crop group?—leafy brassica crops (3LFBC)</td><td>True</td></tr><tr><td>CQ4</td><td>List all EPPO Codes (+ names + description) that are part of non-taxonomic code group "treatment methods" (3TMETM)</td><td>EPPO code: https://ontology.basf.net/ontology/BASF/Bioscience/EPPO/3BRUSM, EPPO name: brushing, EPPO description: Application of a liquid product or powder with a brush, e.g., tree trunk application of fungicide in citrus or local treatment of single weeds in a crop stand; ...</td></tr></tbody></table>

e.g., to their taxonomy classification, categorization list, hosts, pests, among others; (c) the Replaced codes<fnref n="5" /> file, which contains information on the entire history of EPPO codes that were superseded by other EPPO codes. Finally, we also took into account several so-called "categorization" lists,<fnref n="6" /> available in the EPPO Global Database web page. These lists indicate what the regulatory status from a phytosanitary (i.e., plant health) perspective is for a given organism (EPPO code) as defined by a Regional Plant Protection Organization (RPPO), based on the local plant health legislation (e.g., A1 or A2 quarantine pest).

Lastly, in the fourth activity we identified a reusable terminology resource relevant to the ontology purpose and scope. More specifically, we chose the NCBITaxon ontology<fnref n="7" /> (explained in Section 2) as the most related resource to be reused during the ontology enrichment activity.

## 3.2. Implementation

This stage aims to generate the ontology based on the requirements and data sources previously identified. For this purpose, the first activity we carried out was to build a conceptual model to define the classes and properties that represent the ontology domain. We defined such model as a diagram following the details of the Chowlk notation (Chávez-Feria et al., 2022), which is a UML-based notation for ontology diagrams. Figure 2 shows the conceptualization diagram we defined for the EPPO ontology. Note that, due to the large number of terms contained in the EPPO Global database, this diagram only shows the main classes and properties represented in the ontology. However, the ontology contains all the hierarchical classifications included in the database for each class depicted in the diagram.

Next, taking as input the structure we defined in the conceptual model, the second activity we performed was the ontology encoding. The goal of this activity was to generate the ontology as a machine-readable model in an ontology representation language. Figure 3 depicts the steps we carried out to generate

the ontology. First, we performed a transformation of non-ontological resources (the data sources identified in the previous stage) into an ontological one. This transformation task was mainly performed automatically using a Python package (eppo_tools) that we implemented for this purpose. This package reuses pre-existing and well-known libraries such as Requests,<fnref n="8" /> SQLAlchemy,<fnref n="9" /> xml,<fnref n="10" /> RDFLib,<fnref n="11" /> among others that allow us to access the data sources, manage the data and build the ontology code. As a result we obtained the ontology encoded in the Web Ontology Language (OWL). Then, as the different types of EPPO phytosanitary categorizations were extracted from the EPPO Global database web page, human intervention was needed to define such categories and their taxonomy in the ontology. For the human intervention, a domain expert lead the manual extraction and definition of the categorization lists in the ontology using the WebProtégé ontology editor (Tudorache et al., 2013). It is worth mentioning that we also use such editor to add relevant ontology metadata (e.g., creator, title, license, among others) which is useful for ontology reusability purposes. Finally, it is important to note that the EPPO ontology reuses several properties from other ontologies. To this end, we applied the *soft reuse* technique which allows referencing the reused ontology elements URIs instead of importing the whole ontology (*hard reuse*) (Fernandez-Lopez et al., 2019). To decide which properties to reuse, we first analyze the semantics of each property and also look at how common its use is in the community.

Then, the third activity we conducted was the ontology enrichment, which is also depicted in Figure 3. The main objective of such activity was to automatically map NCBITaxon IRIs to EPPO ontology elements that match specific annotations (e.g., rdfs:label<fnref n="12" /> or skos:altlabel<fnref n="13" />).<fnref n="14" /> To generate

<footnote n="8">8 https://pypi.org/project/requests/</footnote>

<footnote n="9">9 https://www.sqlalchemy.org/</footnote>

<footnote n="10">10 https://pypi.org/project/xml/</footnote>

<footnote n="11">11 https://rdflib.readthedocs.io/</footnote>

<footnote n="12">12 http://www.w3.org/2000/01/rdf-schema#label</footnote>

<footnote n="13">13 http://www.w3.org/2004/02/skos/core#altLabel</footnote>

<footnote n="14">14 Note that in the manuscript we use typewriter font when referring to parts of the ontology code. In addition, when reusing elements from another ontology, their prefix is included before the colon and then its local identifier is included.</footnote>

<footnote n="5">5 https://data.eppo.int/files/replaced.zip</footnote>

<footnote n="6">6 https://gd.eppo.int/rppo/</footnote>

<footnote n="7">7 https://obofoundry.org/ontology/ncbitaxon.html</footnote>
<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>
such mappings we built Python scripts to automatically include the NCBITaxon IRIs into the EPPO ontology. It is worth mentioning that to include the mappings we also reused ROBOT (Jackson et al., 2019), which is an open-source library and command-line tool to automate ontology development tasks. The mappings were included in the EPPO ontology by means of oboInOwl:hasDbXref<fnref n="15" /> property which represents a reference to an identical or very similar object in another resource. As a result of this activity we obtained an enriched EPPO ontology. More details of the mapping process are provided in Section 3 of the Supplementary material.

Finally, in the fourth activity we evaluated the ontology to verify that it was correctly built according to the competency questions formulated in the Requirements/Kick off stage. To do this, we translated the competency questions into SPARQL queries in order to run them against the ontology to obtain the expected answers. The SPARQL queries we generated for the ontology evaluation are provided in Section 2 of the Supplementary material.

## 3.3. Publication

This stage aims to deliver the ontology online as human-readable documentation and as a machine-readable file. As for the documentation, we built an HTML file to include a human-readable description of the ontology design that includes diagrams and details about the main the classes and properties. In addition, it includes guidelines on the Python package that bundles all the functionality related to the automatic ontology generation. This HTML documentation is published internally and is made available in the BASF intranet. Finally, to facilitate searching and browsing of the ontology, it is registered in our internal Ontology Lookup Service (OLS) (Côté et al., 2006). This service provides a user-friendly interface with search mechanisms that makes the ontology findable by anyone in the company. OLS also makes use of the ontology metadata to display it to users so that they can analyze the ontology in detail. The latest version of the EPPO ontology is available in our BASF GitHub repository.<fnref n="16" />

## 3.4. Maintenance

Ontologies may degrade over time, due to different reasons including changes or additions in the domains the ontology is modeling, a changing view of the world or a change in usage perspective (Noy and Musen, 2003; Tartir et al., 2010). Therefore, a methodical approach to handle, manage and adapt to changes is of utmost importance during an ontology lifecycle. In our case, as we mentioned in Section 1, EPPO codes are not a static data source; therefore, codes can change or new ones may be added. Such a dynamic environment requires a well-defined strategy to ensure that users have access to the latest available knowledge, and this strategy consists of an automated run of our Python package whenever the public EPPO SQLite file is updated.

<table><caption>TABLE 2 EPPO ontology metrics taken from the Protégé ontology editor.</caption><thead><tr><td>Ontology elements</td><td>Count</td></tr></thead><tbody><tr><td>Axioms</td><td>2,191,211</td></tr><tr><td>Logical axioms</td><td>49,2712</td></tr><tr><td>Declaration axioms</td><td>13,8149</td></tr><tr><td>Classes</td><td>13,8099</td></tr><tr><td>Object properties</td><td>20</td></tr><tr><td>Annotation properties</td><td>35</td></tr></tbody></table>

Then, if a new categorization list appears in the latest version of the database, we inform our domain experts so that they can manually classify it in the corresponding class or, if necessary, create a new class in which to classify it. Lastly, our mappings to the NCBITaxon are also run to ensure that the new version of the ontology contains the references to that external knowledge source.

As defined in our GOMO best practices, the maintenance process is performed in a git repository, where we use different environments to deal with ontology changes. Whenever, an update to the ontology occurs, it is deployed to the DEV environment, which contains work in progress not yet available to end users. Likewise, the content is also deployed into the QA (Quality Assessment) environment, where users can access and notify potential problems they may encounter in the updated version. Then, once a week, the ontology from QA is deployed to the PROD environment, which involves the ontology release. New ontology releases are notified to EPPO ontology users through our internal communication channels, so that they are well-aware of the new information available.

## 4. EPPO ontology description

In this section, we provide further details on the ontology in terms of its main metrics and structure. First, we present the ontology metrics which are listed in Table 2. Such table presents the count of the different ontology elements we generated. In summary, we created more than 130 thousand classes, 20 object properties, and 35 annotation properties which allow representing the EPPO codes concepts, their attributes and the relationships among such concepts.

Then, we provide further details on the ontology structure that was previously depicted in our conceptualization model shown in Figure 2. Note that all prefixes used in this section are listed in Figure 2. The following subsections describe the most relevant classes and properties of the ontology as well as the main relationships among such classes. Finally, we present an example of the ontological representation of an EPPO code using an excerpt from the EPPO ontology.

<footnote n="15">15 http://www.geneontology.org/formats/oboInOwl#hasDbXref</footnote>

<footnote n="16">16 https://github.com/basf/EPPOontology</footnote>
## 4.1. EPPO code

It represents the core class of our ontology, as it contains the most relevant information about codes and their links to all the EPPO Code names. In addition, it is the parent class of several concepts, such as Taxonomic, Non-Taxonomic, and Commodity Group, which allow representing the codes in a more granular way. As previously described in this work, Taxonomic codes represent organisms or organisms groups known as taxa, and Non-Taxonomic codes represents entities of interest for PPP. As for Commodity Group codes, they represent a subset of codes which allow grouping plant commodities (e.g., fruit plants, aquarium plants, conifers, etc.) liable to spread a pest in international trade.

Going into more details of the EPPO Code, each code contains information about its name (rdfs:label), creation date (dcterms:created<sup>17</sup>), and whether it is active or not (active). Optionally, a code can also contain a synonym (oboInOwl:hasExactSynonym<sup>18</sup>), alternative name (skos:altLabel), modification date (dcterms:modified<sup>19</sup>), whether it is deprecated or not (owl:deprecated<sup>20</sup>), and definition (rdfs:comment<sup>21</sup>). More precise details are also included in the name, alternative name, and synonym properties, since the ontology also represents their creation and modification dates, whether they are active or not, and what was the Name's authority (has_authority), e.g., *Gennadius*. It is worth mentioning that all names and synonyms have a corresponding language tag. As for the scientific name (preferred name) and other scientific names the language tag assigned is Latin (la), since it is the official language in which scientific names are defined and, therefore, the language provided by the database. While for common names the language tag is assigned depending on the language in which it is available in the database.

Furthermore, many EPPO Codes belonging to the Taxonomic Code class include information about their phytosanitary status, which represents the categorization list in which they have been classified. For this purpose, such codes are linked to their corresponding Categorization Status by means of the has_categorization property. In addition, EPPO Codes represent their corresponding taxonomy level (has_taxonomy_level), i.e., the integer value representing the distance between a term and its higher-level taxonomic group. Finally, these codes can also include information about their hosts or pests (has_host or has_pest) to represent host-pests or pest-hosts relationship. In regard to the has_pest property, it holds several subproperties which represent all the categories of pest/host plant combinations provided in the database.<fnref n="22" /> For example, the “Alternate” category is represented by the has_pest_type_alternate subproperty which defines a relationship between an organism and the distinct

hosts it needs to complete its life cycle. As for the has_host property, it represents the inverse property of has_pest property. For example, the has_host_type_alternate subproperty represents a host which is used by a pest during its life cycle.

Finally, to represent specific relationships among codes, the ontology reuses two properties: (1) the obo:BFO_0000050<sup>23</sup> property to represent that a Non-Taxonomic or Commodity Group code is part of a subset of them, and (2) the sio:SIO_001403<sup>24</sup> property to represent that a Taxonomic or a Non-Taxonomic code is associated with a Commodity Group code.

## 4.2. Categorization status

This class contains phytosanitary categorization for a given EPPO Code in a region or country, based on the corresponding specific RPPO phytosanitary categorization list (has_status) and a nomenclature for that list as defined in the EPPO Global Database (categorization_q_list, note that "q" stands for "quarantine"). To provide granular details of a categorization, it includes the continent (has_categorization_continent) and country (has_categorization_country) names, and the ISO country code (has_categorization_iso_code) to which the list is applicable. Relevant dates are also represented for each categorization list, such as the year it was added (has_categorization_yearadded), the year it was removed (has_categorization_yeardeleted) or the year it was transferred (has_categorization_year_trans) to another categorization.

## 4.3. Categorization

This class represents the general types of categorizations in which EPPO Codes may be listed. These categorizations are used to draw the attention of countries and regions to the status of plant pests and diseases in terms of the potential phytosanitary risks that they may pose. For example, a pest categorized as part of a quarantine list (QuarantinePest) constitutes a regulatory requirement in terms of phytosanitary measures to be implemented for that pest. As mentioned in Section 3.2, the Code Categorization class contains a hierarchy manually generated by our domain experts. This hierarchy provides a higher-up grouping for the categorizations existing in the EPPO Global database. For example, a quarantine list (QuarantinePest) belongs to (rdfs:subclassOf) the quarantine organism (QuarantineOrganism) class defined

<footnote n="17">17 http://purl.org/dc/terms/created</footnote>

<footnote n="18">18 http://www.geneontology.org/formats/oboInOwl#hasExactSynonym</footnote>

<footnote n="19">19 http://purl.org/dc/terms/modified</footnote>

<footnote n="20">20 http://www.w3.org/2002/07/owl#deprecated</footnote>

<footnote n="21">21 http://www.w3.org/2000/01/rdf-schema#comment</footnote>

<footnote n="22">22 Further explanation of categories is available in the Host Plants section of the EPPO Global Database guide: https://gd.eppo.int/media/files/general_user-guide.pdf.</footnote>

<footnote n="23">23 http://purl.obolibrary.org/obo/BFO_0000050</footnote>

<footnote n="24">24 https://semanticscience.org/resource/SIO_001403</footnote>
by our experts. More details on the definition of the Code
Categorization hierarchy are provided in Section 4 of the
*Supplementary material*.

## 4.4. EPPO code taxonomy level

This class defines the different types of taxonomy
levels, such as Kingdom, Family, or Species, among
others, to which an EPPO Code belongs. To this end,
each code is related to its taxonomic level by means of
the has_taxonomy property. It is worth mentioning
that only those codes that belong to the Taxonomic
Code class can be linked to a taxonomy level. Finally,
each taxonomy level contains a cross-reference to
its corresponding term defined in the NCBITaxon
ontology.

## 4.5. EPPO code type

This class allows representing a more granular classification
of the EPPO codes to group them into different levels:
species level, higher taxonomic group of organisms, or non-
taxonomic entities. For taxonomic EPPO codes at species
level, the EPPO Code Type class distinguishes between plant,
animal, and microorganism. As for higher taxonomic groups
(e.g., genus, family etc.) it includes plant taxonomic group,
animal taxonomic group, and microorganism taxonomic
group. For other non-taxonomic entities it includes non-
taxonomic and commodity groups. In addition to its
label, each type also contains the identifier assigned by
the coding system. Finally, EPPO Codes are related to
their specific code type by means of the has_eppo_type
property.

## 4.6. EPPO replaced codes

As mentioned earlier during the ontology development process,
the ontology also represents the superseded codes available in
the EPPO Global Database. To this end, all these codes contain
similar properties to those included in the EPPO Codes that are
still active. However, the Replaced codes have two annotation
properties that allow them to be identified as part of the
coding system archive. First, the boolean property defined in the
ontology to represent whether a code is active (active) is
declared as false. Second, following our GOMO Standard for
deprecation of ontology elements, the boolean property defined
to specify that an IRI is deprecated (owl:deprecated) is
declared as true. In addition, the term that replaces the code
is defined with the obo:IAO_0100001²⁵ property that allows
the term to be related. to another term that is used as a
substitute. In this manner, the EPPO ontology also represents

codes that are not active but that can be relevant for traceability
purposes.

## 4.7. Example of the ontology representation of an EPPO code

In order to illustrate how the main classes and properties have
been defined in the ontology, we present an example that represents
the information of an EPPO code using the ontology elements.
For this purpose, we use the information from the TRZAW code
(which is the code referred to in the first Competency Question
presented in Table 1). The most relevant information of this code
can be retrieved from the “Overview” menu of the EPPO Global
Database website, as shown in Figure 4. As can be seen in this
figure, the TRZAW code is presented as a non-taxonomic code,
along with its code, preferred scientific name, and other common
names in different languages. In addition, a classification tree is
presented to navigate through the hierarchy to which it belongs.
Moreover, TRZAW is shown as the taxon associated to the TRZAW
code (note that this relationship provides the answer to our first
Competency Question). Finally, the creation date of the code is also
shown.

The ontological representation of the information shown
above for the TRZAW code is provided in Listing 1. This
listing (written in Turtle<fnref n="26" /> format) is an excerpt from the
EPPO ontology that also includes extra information that is
not retrieved from the TRZAW code overview presented in
Figure 4. Going into detail, this listing begins with the definition
of the TRZAW code as a subclass of the NonTaxonomicCode
class and its linkage to the 3SWHC code (soft wheat crops<fnref n="27" />)
via the part of (obo:BFO_0000050) property. In addition,
several properties have been defined to represent the values
of TRZAW's preferred name (rdfs:label), other name
(skos:altLabel), EPPO code (dc:identifier),
creation and modification dates (dcterms:created and
dcterms:modified), other common names in different
languages (hasExactSynonym),<fnref n="28" /> active status (is_active),
and its specific code type (has_eppo_type). The TRZAW code
type corresponds to Non Taxonomic (NTX), which is defined later
in this listing as a subclass of the EPPOCodeType class along
with its code (dc:identifier), and name (rdfs:label).
Moreover, the TRZAW code contains a reference to a similar
term of the NCBITaxon. This reference is represented by the
oboInOwl:hasDbXref property and its value corresponds
to Triticum aestivum (obo:NCBITaxon_4565). Finally,
it should be noted that rdfs:label, skos:altLabel,
and oboInOwl:hasExactSynonym properties contain
additional annotations (dcterms:created and is_active),

<footnote n="26">26 https://www.w3.org/TR/turtle</footnote>

<footnote n="27">27 Note that the 3SWHC code definition is not included in this listing, but is represented in the ontology using similar properties and structure as the TRZAW code presented in this example.</footnote>

<footnote n="28">28 Note that, for simplicity, we have included few synonyms for the codes shown in this listing.</footnote>

<footnote n="25">25 http://purl.obolibrary.org/obo/IAO_0100001</footnote>
<figure><img src="image_30.png" /></figure>

as is the case for the synonym vinterhvede included in this listing.

Then, Listing 1 provides details on the representation of the TRZAX code, which is represented as subclass of the 1TRZG code (Triticum). The ontological representation of this code includes almost the same properties as those described for the TRZAW code, but also details about its taxonomy (has_taxonomy) and taxonomy level (has_taxonomy_level). Moreover, this code is linked to the TRZAW code by means of the sio:SIO_001403 (is associated with) property. It is worth mentioning that, thanks to this last link we can answer our first Competency Question. Finally, TRZAX is linked to the AGMYOR code (Agromyza oryzae) via the has_pest_type_host property, which means that TRZAX is the host of AGMYOR.

Lastly, in Listing 1, the AGMYOR code is defined as a subclass of the 1AGMYG code (Agromyza). The ontological representation of AGMYOR includes all the properties described for the TRZAX code. Moreover, it includes the has_host_type_host relationship to represent that TRZAX is the pest for which AGMYOR is relevant; that is, the inverse relationship of the property previously defined above with has_pest_type_host. In addition, the AGMYOR code is linked to a specific categorization status via the has_categorization property. This categorization is defined at the end of this listing as a subclass of the Categorization_Status class and is linked to the QuarantinePest categorization list by means of the has_status property. Finally, this categorization status also includes information about its categorization continent, (has_categorization Continent), country (has_categorization country), country's iso code (has_categorization iso_code), q list (has_categorization q_list), and year it was added (has_categorization year added).

1 @prefix dc: <http://purl.org/dc/elements/1.1/> .

2 @prefix obo: <http://purl.obolibrary.org/obo/> .

3 @prefix owl: <http://www.w3.org/2002/07/owl#> .

4 @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

5 @prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

6 @prefix eppo: <https://ontology.basf.net/ontology/BASF/Bioscience/EPPO/> .

7 @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

8 @prefix skos: <http://www.w3.org/2004/02/skos/core#> .

9 @prefix dcterms: <http://purl.org/dc/terms/> .

10 @prefix oboInOwl: <http://www.geneontology.org/formats/oboInOwl#> .

11 @prefix sio: <http://semanticscience.org/resource/> .

eppo:TRZAW rdf:type owl:Class ;
rdfs:subClassOf eppo:EPPONonTaxonomicCode ,
[ rdf:type owl:Restriction ;
owl:onProperty obo:BFO_0000050 ;
owl:someValuesFrom eppo:3SWHC ] ;
rdfs:label "soft wheat (winter)"@la ;
skos:altLabel "Triticum aestivum"@la ;
dc:identifier "TRZAW" ;
dcterms:created "2002-03-05T00:00:00"^^xsd:datetime ;
dcterms:modified "2015-04-13T17:58:00"^^xsd:datetime ;
oboInOwl:hasDbXref obo:NCBITaxon_4565 ;
oboInOwl:hasExactSynonym "vinterhvede"@da ;
"winter wheat"@en ,
"wintertarve"@nl ;
eppo:has_eppo_type eppo:NTX ;
eppo:is_active "true"^^xsd:boolean .

[ rdf:type owl:Axiom ;
owl:annotatedSource eppo:TRZAW ;
owl:annotatedProperty oboInOwl:hasExactSynonym ;
owl:annotatedTarget "vinterhvede"@da ;
dcterms:created "2017-07-11T22:41:00"^^xsd:datetime ;
eppo:is_active "true"^^xsd:boolean ] .

eppo:TRZAX rdf:type owl:Class ;
rdfs:subClassOf eppo:1TRZG ,
[ rdf:type owl:Restriction ;
owl:onProperty sio:SIO_001403 ;
owl:someValuesFrom eppo:TRZAW ] ,
[ rdf:type owl:Restriction ;
owl: onProperty eppo: has_pest_type_host;
owl: someValuesFrom eppo: AGMYOR ];
dc: identifier "TRZAX";
rdfs: label "Triticum aestivum" @la;
skos: altLabel "Triticum sativum" @la,
"Triticum vulgare" @la;
dc: identifier "TRZAX";
dcterms: created "2002-02-03T00:00:00" "^^xsd: dateTime ;
dcterms: modified "2002-02-03T00:00:00" "^^xsd: dateTime ;
oboInOwl: hasDbXref obo: NCBITaxon_4565 ;
oboInOwl: hasExactSynonym "Saatweizen" @de ,
"bread wheat" @en ;
eppo: has_eppo_type eppo: PFL ;
eppo: has_taxonomy eppo: Species ;
eppo: has_taxonomy_level 9 ;
eppo: is_active "true" "^^xsd: boolean .
eppo: NTX rdf: type owl: Class ;
rdfs: subClassOf eppo: EPPOCodeType ;
dc: identifier "NTX" @en ;
rdfs: label "Non taxonomic" @en .
eppo: PFL rdf: type owl: Class ;
rdfs: subClassOf eppo: EPPOCodeType ;
dc: identifier "PFL" @en ;
rdfs: label "Plant" @en .
eppo: Species rdf: type owl: Class ;
rdfs: subClassOf eppo: EPPOCodeTaxonomyLevel ;
oboInOwl: hasDbXref obo: NCBITaxon物种 ;
rdfs: label "Species" @en .
EPPO: AGMYOR rdf: type owl: Class ;
rdfs: subClassOf eppo: IAGMYG ,
[ rdf: type owl: Restriction ;
owl: onProperty eppo: has_categorization ;
owl: someValuesFrom eppo: Categorization_AGMYOR_QuarantinePest_US ] ,
[ rdf: type owl: Restriction ;
owl: onProperty eppo: has_host_type_host ;
owl: someValuesFrom eppo: TRZAX ] ;
dc: identifier "AGMYOR";
dcterms: created "2002-11-05T00:00:00" "^^xsd: dateTime ;
dcterms: modified "2002-11-05T00:00:00" "^^xsd: dateTime ;
oboInOwl: hasExactSynonym "Japanese rice leaf miner" @en ,
"agromyze du riz" @fr ;
rdfs: label "Agromyza oryzae" @la ;
skos: altLabel "Agromyza oryzella" @la ,
"Oscinis oryzae" @la ,
"Oscinis oryzella" @la ;
eppo: has_eppo_type EPPO: GAI ;
eppo: has_taxonomy EPPO: Species ;
eppo: has_taxonomy_level 8 ;
eppo: is_active "true" "^^xsd: boolean .
eppo: Categorization_AGMYOR_QuarantinePest_US rdf: type owl: Class ;
rdfs: subClassOf EPPO: Categorization_Status ,
[ rdf: type owl: Restriction ;
owl: onProperty eppo: has_status ;
owl: someValuesFrom EPPO: QuarantinePest ] ;
eppo: has_categorization Continent "America" ;
eppo: has_categorization_country "United States of America";
eppo: has_categorization iso_code "US";
eppo: has_categorization_q_list "X" ;
eppo: has_categorization_year added 1994

Listing 1 Excerpt from the EPPO ontology representing the TRZAW code.

5. Adoption of the ontology

The EPPO ontology is a first step to align the whole Agricultural
Solutions division on a similar vocabulary need. In BASF, we have
four main agricultural focus areas: Crop Protection, Seed and
Traits, Vegetable Seeds, and Digital Farming. By means of the

EPPO ontology, we align these departments to work on a common
vocabulary when referring to organisms.

Currently, the EPPO ontology is being used as a key element of different applications, including Bioregister. Dotmatics’ Bioregister is a Web-based application for registering sequence-based, chemically modified and structure-less biological materials, allowing biologics discovery organizations to ensure entity uniqueness and protect their intellectual property. Bioregister supports management of a broad set of biological materials, including DNA, RNA, peptides and proteins, antibodies, conjugates, non-natural peptides and nucleotides, plasmids, cell lines, and user-defined entities. It also enables users to record batches and samples for these entities, purification and expression information, and other protein production data.

When users enter, for example, a new microorganism record in
the application, it needs to be associated to a plant or pest. In other
previous applications, the reference to these terms was manually
added using a free text input area, so different terms were being
used to refer to the same concept. Even when it was agreed that the
EPPO codes should be used instead, there were still plenty of errors
as users could inadvertently misspell the codes or use different
names to refer to the same concept. Having such naming and
format heterogeneity, as well as mistaken data, led to inefficiencies
when exploiting Bioregister data for further analysis purposes.

To prevent users from making errors when inserting EPPO
codes, the latest version of Bioregister uses the ontology. As seen
in Figure 5, Bioregister’s interface has a dropdown list for users
to select a specific term from the EPPO ontology. To populate
such dropdown list, the application consumes the EPPO ontology
through a specific API call, so that the latest version is always
available, and the terms that appear in the list are dynamically
updated based on what users have entered in the text area. It
is worth mentioning that, to facilitate the consumption of the
EPPO ontology, we have configured a REST API service which
offers a whole set of generic API calls which can be used by
other applications. In addition, it is worth remembering that
microorganisms are just one entity example that can be included in
Bioregister. Therefore, EPPO codes for the associated organism are
also used for other entities such as plants or the donor organisms
for constructs, enzymes, cell lines, among others.

Since the knowledge represented by the EPPO ontology is
pertinent to different types of users, with different background and
different IT skills, the consumption via APIs may not be enough to
ensure the access to the information. Therefore, another way users
consume the EPPO ontology is by means of our internal OLS. This
way users may search and navigate across the different concepts
when looking for information relevant to their work.

Finally, we are reusing the ontology in the development and
enrichment of internal ontologies, such as for example the BASF
Crop Protection Experiments ontology. This ontology aims to
represent the process that is carried out in our labs to design, plan,
prepare, execute, and assess experiments to identify new active
ingredients or traits protecting crops against pests and diseases.

6. Conclusions and future work

In this work, we presented the ontology we developed to
represent the EPPO coding system. The ontology includes the
<figure><img src="image_31.png" /></figure>

data available in several files from the EPPO Global Database and also the information provided in its REST API. In addition, we defined a granular hierarchy of the EPPO Code phytosanitary categorizations that represents the general categories defined in the EPPO lists, European Union lists, and beyond. Finally, we enriched the ontology with NCBITaxon cross-references to allow consuming further information from such knowledge base.

During the development of this work, we have learned several lessons that will help us to improve our ontology developments in the future. First, although the automatic development of ontologies is a valuable method for representing huge data sources, the intervention of domain experts during the process is essential. In our experience the experts have been key to define the requirements, develop the competency questions, and validate both conceptual model and the results obtained after the execution of our Python package. Several relationships that were not implicitly defined in the EPPO codding system have been defined by our experts, and as a result we have a more granular categorization of EPPO Code phytosanitary categorizations. Second, the ontology development is a process that is time and resource intensive, but this is insignificant compared to what we save up by having only one source of EPPO codes. Third, adoption of ontology has not been an easy path in our company because, as happens in most organizations whenever a new technology appears, there is a certain

skepticism about the results that can be obtained by applying it. However, more and more departments are being encouraged to use it to improve their processes.

Despite the advantages of reusing traditional upper-level ontologies (e.g., DOLCE, Masolo et al., 2002) to ease interoperability, we are not reusing them at BASF. The main reason for such a decision is that this kind of monolithic ontologies introduce strong commitments that make it difficult to represent in a lightweight manner our domains of interest. However, parallel to the development of the EPPO ontology, a new work team was formed to develop BASF core ontologies that encapsulate the terms and relationships that are of crucial relevance to the company and that will path the way to facilitate our internal interoperability. Therefore, as part of future work, we will improve the representation of categorization locations of the ontology. For this purpose, we plan to reuse our recently released BASF Core Locations ontology which represents the geographical locations across BASF including administrative areas (such as countries, cities, among others) and location of points of interest (such as production plants and sites, among others). Therefore, we can reuse the concepts from that core ontology to represent countries, regions and ISO country codes instead of representing them as string values as currently done in the classes defined as part of the EPPO Categorization Status.
By having such concepts linked to our ontology, we will be able to get more details to, for example, infer in which cities the phytosanitary categorization is applicable and therefore know in which of our production plants we have to take special care in case of a pest. We can also take advantage of the geometric values contained in the core ontology to have a map that can provide us with alerts on the categorizations in a customized way for the points of interest relevant to our company.

Within BASF, the Biosafety function has oversight on the use of all types of biological material in facilities with the aim of protecting human health and the environment and to prevent their misuse (biosecurity) while ensuring compliance with regulatory and company requirements. Hence, a possible future direction is the development of a Risk Group Classification ontology aimed to represent not only the list of phytosanitary categorizations included in the EPPO ontology, but also data whether organisms are regulated as human or animal pathogens in selected countries around the world. Having the regulatory categorization of plant, human and animal pathogens in a single data source which can be easily queried allows to identify in a single effort the applicable government regulations pertaining to these organisms in a certain geography, instead of having to manually consult various public/external data sources, as well as supports aligned biorisk management approaches across different BASF sites and countries.

Additionally, there are plans to reuse the ontology in applications that are used internally such as Ceres (for managing the inventory of biological materials in our R&D laboratories and greenhouses) or PhenomeOne (for managing the entire plant research information of the organization, providing support for all the stages of our experimental processes). Finally, since ontologies can change, we will implement a monitoring and updating mechanism to track NCBITaxon updates. This way, if something changes in that ontology, our EPPO ontology will be aligned with it.

## Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: https://gd.eppo.int.

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## Author contributions

AA-B, JB-D, BM, KH, and DB: conceptualization and design. AA-B, JB-D, TC, CC, and NP: development. PE-A: writing—original draft preparation. PE-A, IE-G, DB, AA-B, and JB-D: writing—review and editing. PE-A, IE-G, and AA-B: supervision. All authors contributed to the article and approved the submitted version.

## Acknowledgments

We would like to thank Alexander García Castro for the conceptualization of the EPPO ontology development.

## Conflict of interest

AA-B, JB-D, PE-A, and IE-G were employed by BASF Digital Solutions. DB was employed by BASF Belgium Coordination Center CommV, Innovation Center Gentm. BM was employed by BASF Corporation. KH was employed by BASF SE Data Management and Data Governance, Global Research Services APR/HP. TC was employed by TalentBay. CC and NP were employed by Linking Data SAS.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

## Supplementary material

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<figure><img src="image_32.png" /></figure>

OPEN ACCESS

EDITED BY
Catherine Roussey,
INRAE Occitanie Montpellier, France

REVIEWED BY
Patrice Buche,
Institut National de recherche pour
l'agriculture, l'alimentation et l'environnement
(INRAE), France
Sylvie Despres,
Université Sorbonne Paris Nord, France
María Poveda-Villalón,
Universidad Politécnica de Madrid, Spain

*CORRESPONDENCE

Damion Dooley

damion_dooley@sfu.ca

†These authors have contributed equally to this work

RECEIVED 05 January 2023

ACCEPTED 05 June 2023

PUBLISHED 04 July 2023

CITATION

Dooley D and Naravane T (2023) Ontological
how and why: action and objective of planned
processes in the food domain.
*Front. Artif. Intell.* 6:1137961.
doi: 10.3389/frai.2023.1137961

COPYRIGHT

© 2023 Dooley and Naravane. This is an
open-access article distributed under the terms
of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/).
The use, distribution or reproduction
in other forums is permitted, provided the
original author(s) and the copyright owner(s)
are credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted which
does not comply with these terms.

Ontological how and why: action
and objective of planned
processes in the food domain

Damion Dooley<fnref n="1" />*† and Tarini Naravane<fnref n="2" />†

The computational modeling of food processing, aimed at various applications including industrial automation, robotics, food safety, preservation, energy conservation, and recipe nutrition estimation, has been ongoing for decades within food science research labs, industry, and regulatory agencies. The datasets from this prior work have the potential to advance the field of data-driven modeling if they can be harmonized, but this requires a standardized language as a starting point. Our primary goal is to explore two interdependent aspects of this language: the granularity of process modeling sub-parts and parameter details and the substitution of compatible inputs and processes. A delicate semantic distinction—categorizing planned processes based on the objectives they seek to fulfill vs. categorizing them by the actions or mechanisms they utilize—helps organize and facilitate this endeavor. To bring an ontological lens to process modeling, we employ the Open Biological and Biomedical Ontology Foundry ontological framework to organize two main classes of the FoodOn upper-level material processing hierarchy according to objective and mechanism, respectively. We include examples of material processing by mechanism, ranging from abstract ones such as “application of energy” down to specific classes such as “heating by microwave.” Similarly, material processing by objective—often a transformation to bring about materials with certain qualities or composition—can, for example, range from “material processing by heating threshold” to “steaming rice”.

KEYWORDS

food processing, ontology, mechanism, action, data specification, material processing

1. Introduction

The post-harvest treatment of food up to the point of consumption from both industrial and domestic food preparation perspectives is an active area of research that is not yet comprehensively covered by an integrated set of ontologies. Here, we propose for discussion, as part of a larger life-sciences family of ontologies, the basic terms required for a standardized process ontology that can enable and integrate data-driven analysis of research datasets on the one hand and (with data at the relevant resolution and data size) support dynamic process control applications on the other hand. Formalized language is required to integrate what have often been siloed food composition datasets (FCD) containing foods that result from simple processes such as boiling, freezing, and roasting. Standardized language is a prerequisite to the manual or automated alignment of different food entities across FCD datasets. For example, the nutritional content of frozen carrots can only be compared across datasets if the experimental protocols for storing, soaking, blanching, boiling, and (flash) freezing processes are comparable (Hinojosa-Nogueira et al., 2021; Westenbrink et al., 2021).

<footnote n="1">¹Centre for Infectious Disease Genomics and One Health, Simon Fraser University, Burnaby, BC, Canada,</footnote>

<footnote n="2">²Biological Systems Engineering, University of California, Davis, Davis, CA, United States</footnote>
Ontologies can be used to normalize the comparable portions of data selected from the body of scientific literature on food processing so that data-driven analysis and models can be used to address a range of research questions/hypotheses on the causal factor(s) driving sensory and nutritive effects.

This process ontology work also aims to support dynamic process control by providing a framework for describing process input and output phenotype objective thresholds that can trigger mechanism start/stop/pause operations and by providing a framework for choosing among comparable mechanisms to achieve an objective. The ontology has been designed to differentiate between the objectives and mechanisms of a process and to address food processing at both macro (food product) and molecular scale transformations. The latter is especially challenging to describe in food science literature datasets (a task similar to material science engineering modeling). We provide a macro/micro transformation example model that shows the parallels between the food entity and molecular resolutions. This framework addresses the various details of a process to support various decision-making methods for dynamic process control, ranging from simple inferences/linear relationships to more complex ML models.

Given the natural context of food as mainly derived from organisms, our process ontology leverages the framework established by the Open Biological and Biomedical Ontology Foundry (OBO) consortium of ontologies (Jackson et al., 2021), which focuses on life science research. In this study, the modeling of natural physio-chemical or biologically rooted processes can be found in places such as the Gene Ontology's cellular metabolic process [GO:0008152] branch classes (including, for example, fermentation, an enzyme-catalyzed process), which are triggered when some combination of materials and/or environmental context aligns. Unplanned processes can be controlled by planned processes that exhibit human or computer agency/intentionality. To organize processes that satisfy various objectives in transforming things, OBO's Ontology for Biomedical Investigations (OBI) (Bandrowski et al., 2016) introduced the "planned process" class [OBI:0000011], which contains processes that execute a "plan specification" and include a set of instructions and/or objectives.

A recent paper (Dooley et al., 2022) covers a gap analysis of the technical side of modeling processes using W3C OWL ontologies (SOSA, SSN, PO2, and OWL-Time) in comparison to an OBO

<figure><img src="image_33.png" /><figcaption>FIGURE 1
A new material processing hierarchy organized under a planned process, with new "process by objective" and "process by mechanism" branches.</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>FIGURE 2
A material processing by mechanism hierarchy with example subclasses, in which no absolute end-point material qualities are specified.</figcaption></figure>
<figure><img src="image_35.png" /><figcaption>FIGURE 3
A hierarchy of "material processing by objective" classes and some lower-level examples. Each has a plan specification containing an objective specification that supplies a completion metric.</figcaption></figure>

Foundry ontology approach and recommendations, implemented mainly in OBO Foundry's FoodOn food ontology (Dooley et al., 2018) for extending OBO with some select relationships and classes adapted from the aforementioned ontologies to fill the gap. The paper details the OBI "planned process" related classes and relations and discusses how experimental independent and dependent variables, observations, and characteristics of materials could be structured in a multi-step process model. A brief discussion of measurement data properties is included, but in that (and current) work, we avoid focusing on this topic and note that upcoming OBO work will recommend knowledge graph data structures for measurement values. That paper finishes with a simple recipe model that illustrates ingredient input and output relations at work in a sample selection of food processes but skirts the issue of organizing a hierarchy of food processes; our new work focuses on this topic.

A plan specification can have one or more “action specifications” [IAO:0000007] parts that directly or indirectly control the input material’s environmental parameters, such as container pressure, kinetic or thermal energy exposure, or the addition of chemicals or biological substances. An action specification might be to operate a tool or device setting or control to some effect or to give hands-on instruction to an operator to shape a material directly or combine materials. It may reference other planned processes or directly control (via duration, catalysts, or energy supply) an unplanned physio-chemical process. Natural fermentation would be considered unplanned, but a planned process can harness it through action specification(s), devices, and subprocess stages. Other examples are the application of force to a material or to a blade in the material; introducing bacteria to a food substance; controlling atmospheric storage conditions for food; or

allowing the fruit to ripen before harvest or consumption (Osorio et al., 2013).

In time, the effect of environmental interventions (whether constant or in flux) yields physical or chemical changes in material input(s) that satisfy process objectives. A material processing "objective specification" is often an expression of the quality(ies) or phenotype(s) of the output material, such as "water at 100 degrees Celsius", which is the causal result of the process. Other examples are sensory, logistical, food safety, or food formulation functional objectives. In short, an objective is an expression of some desired state of affairs, and the "Process by Objective" class, thus, necessarily includes such an expression either as a final output specification to reach or by a formula of operating parameters. The recognition that an objective has been attained (whether by a human or a device) can be a component objective of a larger process.

# 2. Methodology and results

## 2.1. Process terms

In OBO, currently, there are no “convenience classes” for organizing processes by action or objective, so our proposal involves adding those new process terms and underlying ones within an appropriate OBO ontology. FoodOn could temporarily accommodate them, but OBO’s best practice entails consulting about the possible adoption of mid/upper-level terms by the curators of OBI or the in-development Core Ontology for Biology and Biomedicine (COB) (Core Ontology for Biology and Biomedicine, n.d.), which is taking on commonly used OBO terms. Although specific processes such as boiling are examined here in