<figure><img src="image_36.png" /><figcaption>FIGURE 4
A sketch of polyhierarchical process dependencies.</figcaption></figure>

the food context, they are also often applicable to other domains such as manufacturing and laboratory procedures and are best curated in a general-purpose ontology from which FoodOn can draw. Note that in this study, the “is-a” relationship in the legend refers to OWL rdfs:subclassOf. Additionally, all illustrated relations are RO or OBI ontology relations and have their domain and range constraints held in those ontologies (such as RO “has quality” range “quality” [PATO:0000001]).

FoodOn has an existing “food transformation process” branch, which will be reorganized according to the scheme proposed below. The branch is managed according to a common OBO term maintenance pattern as a spreadsheet template (FoodOn Robot Tables, 2023), which is periodically converted into a stand-alone ontology import file. Figure 1 offers an overview of the new proposed hierarchy with new “process by objective” and “process by mechanism” classes alongside the existing OBI “material processing” term. The “material context change by objective” and “material context change by mechanism” classes cover both packaging and moving of material entities (to some objective location or by some mechanism of transport), but they are not detailed here.

The “material processing by mechanism” branch outlined in Figure 2 covers the application of force or energy and combining materials (and includes some example subclasses). In this study, the relative change effected by a process will modulate a material

quality, such as by reducing particle size, changing temperature, or adding a new quality, but it will not specify an absolute threshold upon which to complete the process. These processes continue unless some inherent process limit occurs, such as an exhausted resource or, with mixing miscible liquids, if maximum homogenization is reached.

The “material processing by objective” branch outlined in Figure 3 includes complete processes when one or more objectives are satisfied. This can involve objectives that are expressed as threshold qualities of a material, such as a turkey with a core temperature of 70°C. Alternatively, objectives may be expressed as characteristics of the process—for example, its duration, energy, or amount of catalyst consumed—which are a proxy for predicted material outcomes. When applied to food products, terms such as “chilling” may have highly industry-specific objective semantics, such as the chilling of animal products (Temperatures and Chilling and Freezing Procedures, 2023), which could be formalized in the ontology. The proposed material processing by objective hierarchy does not preclude objective specifications, so a reasoner should be able to infer that material processing by objective classes falls under more general process mechanism classes, for example, “material processing by cooling threshold” as a subclass of “cooling of the material,” or “fractionation by objective” as a subclass of “fractionation”.

Positioning of a process by objective (for example, bringing a liquid to its boiling point) and by mechanism (for example,
<table><caption>TABLE 1 A new process hierarchy based on objective and mechanism branches.</caption><thead><tr><td>Label</td><td>Definition</td><td>Notes</td></tr></thead><tbody><tr><td>Planned process (OBI)</td><td>A process that realizes a plan that is the concretization of a plan specification.</td><td>This term is from OBI. Paraphrasing: a process that executes a plan specification.</td></tr><tr><td>Process by mechanism</td><td>A planned process that has one or more action specification parts in its plan specification that control a mechanism.</td><td>A convenience class for organizing processes by their physical mechanism or digital algorithm. An action may be physical, such as pushing a button or setting a dial, or it may be about running some software.</td></tr><tr><td>Material processing (OBI)</td><td>A planned process that results in physical changes in a specified input material.</td><td>More than one input material may be involved. Note that ENVO's similar "material transformation process" is unplanned.</td></tr><tr><td>Material processing by mechanism</td><td>Material processing has one or more action specifications in its plan specification.</td><td>This should also be inferred under "process by mechanism." Here, action specifications directly or indirectly control a material input's environmental parameters.</td></tr><tr><td>Energy modulation</td><td>A material processing mechanism in which energy is removed from or added to a material entity.</td><td></td></tr><tr><td>Heating of material</td><td>An energy modulation in which thermal energy (heat) is applied to a material or its environment.</td><td></td></tr><tr><td>Cooling of material</td><td>An energy modulation in which thermal energy (heat) is removed from a material or its environment.</td><td></td></tr><tr><td>Force modulation</td><td>A material processing mechanism in which force is applied to a material or its environment.</td><td></td></tr><tr><td>Separating material</td><td>A material processing mechanism in which materials are separated.</td><td></td></tr><tr><td>Combining material</td><td>A material processing mechanism in which materials are combined.</td><td></td></tr><tr><td>Molecular mechanism</td><td>Material processing is described for specific molecules in the material.</td><td></td></tr><tr><td>The molecular mechanism by reaction type</td><td>A molecular mechanism is categorized by its reaction type.</td><td></td></tr><tr><td>Covalent reaction</td><td>A molecular mechanism by reaction type involving a covalent reaction.</td><td></td></tr><tr><td>Non-covalent reaction</td><td>A molecular mechanism by reaction type involving a non-covalent reaction.</td><td></td></tr><tr><td>The molecular mechanism by spatial location</td><td>A molecular mechanism is categorized by the region in which reactions occur.</td><td></td></tr><tr><td>Surface mechanism</td><td>A molecular mechanism where reactions occur at some surface boundary.</td><td></td></tr><tr><td>Bulk molecular mechanism</td><td>A molecular mechanism where reactions occur throughout a mixture.</td><td></td></tr><tr><td>Process by objective</td><td>A planned process that has one or more objective specification parts in its plan specification.</td><td>A convenience class under which various processes can be grouped or inferred by their objectives.</td></tr><tr><td>Material processing by objective</td><td>Material processing that has one or more objective specification parts in its plan specification.</td><td>This will also be inferred under "process by objective." Here, processes having equivalent objectives can be swapped.</td></tr><tr><td>Direct heating of container (FoodOn)</td><td>A heating container process in which the container conducts heat by being near an open flame, a hot surface, or an oven.</td><td></td></tr><tr><td>Boiling</td><td>A material processing by the heating threshold in which the objective is to keep a liquid at its boiling temperature under atmospheric conditions.</td><td></td></tr><tr><td>Material context change</td><td>A planned process in which the relation of the input material entity and its proximate environment changes.</td><td></td></tr><tr><td>Material context change by objective</td><td>A material context change in which the objective is to change the contextual relation of the input material entity and its environment.</td><td>For example, the objective of a wrapped food or moving some food somewhere specific.</td></tr><tr><td>Material location change process</td><td>A material context change in which the objective is to move the input material to another location.</td><td>The ultimate location may be dynamically ascertained based on other inputs/decision points, for example, in a sorting process [this is also an Industrial Ontologies Foundry term (Kulvatunyou et al., 2022)].</td></tr><tr><td>Material context change by a mechanism</td><td>A material context change is when an action that changes the contextual relations of the input material entity is applied.</td><td>For example, pushing against an object may cause it to move.</td></tr></tbody></table>
<figure><img src="image_37.png" /><figcaption>FIGURE 5</figcaption></figure>

How an objective specification like "boiling water" can be expressed as a universal (class-level) output of a planned process. Boiling water is a class of boiling liquid with certain temperature and pressure characteristics, similar to other liquids like boiling ethanol.

"heating by microwave" or "direct heating of container") is shown in Figure 4, as is the example of the hierarchy of stove top and microwave boiling processes.

While some terms mentioned in the above figures (shown with identifiers) come from existing OBO Foundry and other ontologies, the bulk of this upper-level hierarchy must be created. New key terms are listed in Table 1, along with their definitions and notes. Our motivation for presenting these terms here is to encourage feedback in the spirit of an open-source community so that their labels and definitions can be finalized. Discussion can be held at the GitHub FoodOn issue page https://github.com/FoodOntology/foodon/issues/262 or by contacting the authors directly.

The boiling water process example illustrates the distinction between processes which have more open-ended mechanisms, and those with completion objectives. A "heating liquid" class does not include any objective, but its "heating liquid to boiling point" subclass does require a boiling liquid output. More specifically, an objective to bring some potable or "drinking water" (usually at ambient temperature) to a boiling point may require some context for that boiling, e.g., the proxy objective of it being 100 degrees Celsius (°C) at 1 atmosphere (atm) unit. Additionally, a mechanism invoked to boil this water will require a liquid container, a vector of energy, and either a "heating of the container" process or a "heating by microwave" process. As shown in Figure 5, various liquids have different boiling point temperature x atmospheric pressure objectives. There is the potential for establishing a digital library of such instants—much like the SI library of real-world entities such as the meter and the kilogram—that can be reused to express process objectives. To model the process of "heating liquid to the boiling point," one can reuse a URI that points to a "reference"

instance of "boiling water" with its standard measurements of water and atmospheric conditions and a separate input instance of water with qualities that approach the standard over time (the multicomponent nature of these reference measures precludes a solution at the class level involving owl:hasValue; instead, the "has quantity" and "has unit" properties are in line with OBO's upcoming data model).

## 2.2. Molecular branch

The concepts of mechanism and objective apply to food at both macro and molecular levels, which gives rise to a correspondence between mechanism and objective activity at both levels. We describe the considerations for building the "molecular mechanism" branch and provide an example. This branch describes mechanisms specific to certain molecules that can be key to the molecular composition of a food's processed versions. There are at least two prominent characteristics of molecular-scale mechanisms: the chemistry of interactions between molecules and the spatial location of interactions within food material(s). Molecular interactions are either covalent (e.g., Maillard reaction) or non-covalent (including van der Waals forces, electrostatic forces, and hydrogen bonding) (Yamada, 2014) and may either occur throughout the material or be localized (Doi, 2013). Figure 8 provides a rice cooking example that identifies and differentiates various molecular mechanisms and sensor measurement concepts. Rice cooking is dominated by the molecular mechanism of starch interacting with water through different time and temperature conditions (starch comprises up to 90% of a rice kernel).
<figure><img src="image_38.png" /><figcaption>FIGURE 6
Generic schema of some sensory processes that help determine whether the objectives have been achieved during material processing.</figcaption></figure>

The specific molecular processes are swelling, gelatinization, pasting, and retrogradation, all due to the hydrogen bonding interactions that occur in bulk when rice is cooked by soaking and boiling in water and then cooling. Initially, the components of starch, amylose (AM), and amylopectin (AP) are in the native “granular” state of alternating bands of amorphous and crystalline regions enabled by intramolecular hydrogen bonds. Soaking rice in water at ambient temperature sets off the gradual seepage of water into the structure at a rate proportional to the temperature. Heating this mixture of “soaked” rice and water increases the swelling of native starch granules. During this swelling process, the water creates hydrogen bonds with amylopectin and gradually disrupts the crystallinity of the granule irreversibly. This leads to the breaking of the native structure. Amylose and amylopectin leach into the water, and the gelatinization is complete. This is followed by pasting until the rice is “cooked.” AP and AM reassociate as the rice cools, a process termed retrogradation (Kadam et al., 2015). This is observed as the drying-out of the rice when refrigerated or the thickening of rice porridge (congee). These mechanisms can be sensed either by humans or an instrument and are associated with several objectives, as shown in Figure 6. Specific to the example explored above, instrumental sensors indicate rheological

and physical properties, while humans sense the mouthfeel qualities described as stickiness, chewiness, creaminess, etc.

## 2.3. Applications

The language and hierarchy of terms developed here apply to both scientific experiments and home cooking contexts, as shown in the context of the rice cooking example (Figure 7). From a domestic consumer-end recipe perspective, rice cooking often has a more formulaic approach, specifying a device and ingredient quantity, and completion is assumed by either the cooking time or by a sensory perception of mouthfeel (Naravane and Lange, 2018). From a food science perspective, rice cooking is described by the molecular mechanisms of swelling and gelatinization that specific instruments and protocols can measure. In addition, the language also addresses both macro-level and molecular-scale mechanisms, with the aim that changes in food composition can be explained at the molecular level.

The vast body of research literature on food processing addresses diverse questions to discover the sensory and nutritional profiles of processed foods due to processing conditions. Several
<figure><img src="image_39.png" /></figure>

experimental studies also aim to correlate objective measures with more subjective human sensory scales (Tao et al., 2020). Every experimental dataset typically explores only a few variables for specific outcomes but is taken together. These studies contribute to a vast body of research on food composition and transformative mechanisms. Integrating this experimental body requires a standardized language like this, and such large datasets have the potential for knowledge modeling, as evidenced by the models developed on traditional nutrition-focused datasets (Naravane and Tagkopoulos, 2023).

Figure 8 illustrates the rice cooking use case of applying ontology to experimental studies. Rice cooking predominantly involves the interaction of water with rice through the energy supplied in the form of heat up to the boiling point of 100°C. A progression of rice states is shown in the material entity tier of the figure. Intermediate and final process products can be measured for experimental or process control variables. This abbreviated protocol omits some steps and controls one might have, such as washing rice, using a certain cooking

device, setting the cooking temperature, etc. Specifically, the “gelatinization” process has been detailed since it is essential to cooking by virtue of the water penetrating the rice’s native starch crystal structure, which advances to some extent in “soaking rice”, and the subsequent breakdown of crystal structure requires “heating of rice in water”. However, it will take more detailed modeling to address dried rice types such as having a pertinent kind of starch crystal formation, having husks removed, and water temperature factors to replace this simplified protocol.

Various material entities are observable in both “domestic” cooking and scientific experiments, while instrumental measures (such as peak viscosity and glass transition) that capture certain molecular states are specific to a scientific context. An example of dynamic process control involves modifying time and temperature conditions to affect two outcomes: the recrystallization of rice (which is associated with glycemic index) and the control of the textural properties of cooked rice (for example, soft, hard, and chewy).
<figure><img src="image_40.png" /></figure>

Generally, ontology provides a formal language and framework to align various mechanisms, objectives, and instrumental measures. While the knowledge graph in this figure has been manually curated with food science expertise, this preliminary work could be evolved to support inference. Food science process terms such as peak viscosity and glass transition could also be text-mined from the literature and introduced under the ontology's mechanism and objective hierarchies at either a macro or a molecular scale.

The extracted terms can be used to structure data across food science experiments, and the analytical measurements associated with the process terms can be used for dynamic process control. Once finalized into ontologies such as OBI or FoodOn, this work will support data curation objectives—FAIR guidelines I1, R1.2, and R1.3 (FAIR Principles, 2017)—wherein data are coded in easily interpretable formats with precise provenance, and which use standardized (interoperable, reusable) language throughout. The chain of processes that ultimately generate data can be detailed as an instance of a protocol (whether experimental or operational), enabling a graph of a protocol's process, device, input, output, operator, and other contextual components—via ontology term and

relation identifiers—to achieve disambiguation, comparability, and provenance of resulting datasets.

### 3. Future work and conclusion

This work should enhance clarity in finding a home for each food process under the matching mechanism/action or objective hierarchies. It should enable further research into how OWL logic can support the identification of equivalent processes for use in dynamic, versatile food processing pipelines. These elements are essential for enabling dynamic processing pipelines that can search and select from a library of processing components based on goal and/or resource constraints such as available tools or operators (mechanisms/actions) or material resources—a capability that humans often demonstrate in laboratory, industrial, or home food preparation settings. Additionally, this work should encourage the development of a better food processing protocol detail vocabulary, allowing appropriate comparison of data points within food composition databases and nutritional studies.
## Author contributions

All authors listed have made a substantial, direct, and
intellectual contribution to the work and approved it
for publication.

## Funding

DD was primarily supported by the USDA Non-Assistance Cooperative Agreement 58-8040-8-014-F and Genome Canada Grant 286GET.

## Acknowledgments

The authors would like to acknowledge colleagues Magalie Weber of INRAE and Rhiannon Cameron of CIDGOH for their feedback on the manuscript.

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## 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_41.png" /></figure>

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OPEN ACCESS

EDITED BY
Marie-Angélique Laporte,
Bioversity International, France

REVIEWED BY
Hai Wang,
China Agricultural University, China
Florence Amardeilh,
Elzeard, France

*CORRESPONDENCE

Baptiste Imbert

baptiste.imbert@inrae.fr

Nadim Tayeh

nadim.tayeh@inrae.fr

RECEIVED 21 March 2023

ACCEPTED 10 July 2023

PUBLISHED 03 August 2023

CITATION

Imbert B, Kreplak J, Flores R-G, Aubert G, Burstin J and Tayeh N (2023) Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes.

Front. Artif. Intell. 6:1191122.

doi: 10.3389/frai.2023.1191122

COPYRIGHT

© 2023 Imbert, Kreplak, Flores, Aubert, Burstin and Tayeh. 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.

# Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes

Baptiste Imbert<fnref n="1" />*, Jonathan Kreplak<fnref n="1" />, Raphaël-Gauthier Flores<fnref n="2" />,³, Grégoire Aubert<fnref n="1" />, Judith Burstin<fnref n="1" /> and Nadim Tayeh<fnref n="1" />*

While the continuing decline in genotyping and sequencing costs has largely benefited plant research, some key species for meeting the challenges of agriculture remain mostly understudied. As a result, heterogeneous datasets for different traits are available for a significant number of these species. As gene structures and functions are to some extent conserved through evolution, comparative genomics can be used to transfer available knowledge from one species to another. However, such a translational research approach is complex due to the multiplicity of data sources and the non-harmonized description of the data. Here, we provide two pipelines, referred to as structural and functional pipelines, to create a framework for a NoSQL graph-database (Neo4j) to integrate and query heterogeneous data from multiple species. We call this framework Orthology-driven knowledge base framework for translational research (Ortho_KB). The structural pipeline builds bridges across species based on orthology. The functional pipeline integrates biological information, including QTL, and RNA-sequencing datasets, and uses the backbone from the structural pipeline to connect orthologs in the database. Queries can be written using the Neo4j Cypher language and can, for instance, lead to identify genes controlling a common trait across species. To explore the possibilities offered by such a framework, we populated Ortho_KB to obtain OrthoLegKB, an instance dedicated to legumes. The proposed model was evaluated by studying the conservation of a flowering-promoting gene. Through a series of queries, we have demonstrated that our knowledge graph base provides an intuitive and powerful platform to support research and development programmes.

KEYWORDS

graph database, orthology, ontology, quantitative genetics, gene expression, comparative
omics, Ortho_KB, OrthoLegKB

Introduction

To accelerate plant research and manage costs, model species first emerged as a good
strategy for studying plant development and stress response, thus providing the research
community with data and knowledge. Databases such as TAIR for *Arabidopsis thaliana*
(Berardini et al., 2015), MTGD for *Medicago truncatula Gaertn*. (Krishnakumar et al., 2015),

<footnote n="1">¹Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France,</footnote>

<footnote n="2">²Université Paris-Saclay, INRAE, URGI, Versailles, France, ³Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, Versailles, France</footnote>
miyakogusa-jp for *Lotus japonicus* (Sato et al., 2008) or RAP-DB for *Oryza sativa* (Ohyanagi et al., 2006) were created to centralize, organize and curate the information on model species while providing tools for their analysis. Meanwhile, researchers working on other species have been inferring information from closely-related model plants using orthology and synteny. In fact, orthologs, i.e. genes descending from a common ancestral gene by a speciation event, are likely to have similar and conserved functions (Linard et al., 2021). However, it can prove difficult to identify the correct ortholog of a gene among its homologs based only on sequence similarity because of duplication events. Synteny and collinearity, i.e., conservation of the content and the order of genes on chromosomal regions, respectively, can help identifying orthologous blocks and hence deciphering true orthologous genes (Drillon et al., 2020). Such information is already made available and exploited through platforms such as PLAZA (Van Bel et al., 2012, 2022), sometimes supplemented by tools giving access to gene expression data (Kamei et al., 2016).

With the advent of new technologies, the once daunting sequencing costs have been dramatically reduced (Shendure et al., 2017), allowing for the production of high-quality assembled genomes including for orphan species (Ye and Fan, 2021). These new resources, along with associated annotations, are often being hosted on dedicated websites and/or made available in repositories of well-known databases such as NCBI (Sayers et al., 2022), Ensembl Plants (Yates et al., 2022), Gramene (Tello-Ruiz et al., 2021) or Phytozome (Goodstein et al., 2012). The release of the genome sequences is significantly boosting the production of genetic data to inform on the control of phenotypic traits by genes and the production of -omic data (mostly represented by genomics, transcriptomics, proteomics and metabolomics) to characterize and quantify the different molecules from a biological entity. However, efforts are still uneven across the broad spectrum of species since conducting experiments spanning a wide range of genotypes, tissues and conditions to generate solid data can be very informative, but also expensive and hard to achieve. Also, some quantitative trait loci (QTL) controlling quantitative traits still display low resolution, either due to low marker density or to low recombination rate in the respective genomic regions, which can result in large number of genes within the confidence intervals and long lists of candidate genes. Comparing QTL positions across species can help pinpointing orthologous ones and thus refining the intervals of those with low resolution. Such comparative translational research has also the potential to transfer functional information from one species to another or to a group of species.

Databases are powerful tools to leverage already produced datasets, not only as a mean of storage but also of intelligent exploitation. For example, the Comparative Genomics (CoGe) platform currently allows for the comparison of datasets from a wide range of organisms, with nearly 58,000 genomes available (Lyons and Freeling, 2008). Using sequence homology and synteny, researchers can identify structural and nucleotide variations for their species of interest. Researchers can also use the LoadExp+ extension to import experimental data in various common formats, such as VCF for polymorphism or FASTQ for RNA-seq, process them, and display the results as tracks in the genome browsers (Grover et al., 2017). Nevertheless, the data are predominantly

stored using relational database management system (RDBMS), distributed in category-specific tables. One problem than can arise with RDBMS is that connecting tables containing large datasets during querying requires several joining operations, which are expensive in terms of time and computational resources (Vicknair et al., 2010).

The intuitive idea of structuring intertwined data into a graph was propelled by the World Wide Consortium for semantic web through the Resource Description Framework (RDF), (W3C, 1994). In order to obtain a logical model in RDF, each piece of data is sliced into atomic statements stored as triples, i.e., (1) the subject of the resource to describe, (2) a property assigned to the resource, termed predicate and (3) the object, either a description or another resource. The subject and the object are nodes in the graph, while the predicate is an edge connecting the two nodes (Abuoda et al., 2022). The directional decomposition of information allows the use of ontologies that organize knowledge and greatly improve data sharing in scientific communities (Stevens et al., 2020). However, databases using this format, called triplestores or RDF stores, are characterized by an atomic granularity of nodes which can make database modeling tedious. In addition, deep traversal of the graph requires self-joining of all traversed triples which can make the cost of traversing edges logarithmic (Donkers et al., 2020).

Alongside RDF, labeled-property graph (LPG) databases have emerged, currently led by Neo4j, which are fundamentally designed to improve graph traversal by directly storing on disk all existing edges between nodes. A benchmark from Khayatbashi et al. (2022) comparing RDF triple-stores and LPG databases with twelve queries shows that Neo4j is in fact more efficient to traverse multiple layers of data. Neo4j databases offer high flexibility by adding key-value properties to nodes and edges to effectively compact information, consequently making the modeling easier to read and to incrementally improve (Donkers et al., 2020; Neo4j, 2023b). Considering these assets, Neo4j databases were found advantageous to manage dense networks of information required for systems biology. The Reactome database (Fabregat et al., 2018) and its plant counterpart Plant Reactome (Naithani et al., 2019) have already switched from an RDBMS database to a Neo4j database, since metabolic pathways are intrinsically connected as a graph structure. In fact, using a graph database dropped the average query time of Reactome by 93 % (Fabregat et al., 2018). While a graph is intuitive when representing a biological pathway, the value of such modeling extends to many applications, including translational research. For instance, orthologous relationships across genes required for translational research, could be modeled in a Neo4j database with an "IS_ORTHOLOGOUS_TO" relationship between the two "Gene" nodes. Information regarding the gene identifier or annotation could be stored as internal node properties, available for querying. As the system is adaptable, new layers of data can successively be added and articulated. Omics Database Generator (ODG) is the first LPG designed for translational research as defined by Guhlin et al. (2017). ODG is a Neo4j graph-database, developed primarily for annotation transfer to non-model species of bacteria and plants. The structure of ODG has been made available for researchers to import their own data. Indeed, the comparison of newly generated data with existing data can confirm hypotheses or
help to generate new ones. This is especially useful when datasets do not yield results supporting the initial research hypothesis, end up being set aside and remain unpublished (Raciti et al., 2018). It is therefore crucial to use as many available and high-quality datasets as possible, whether published or unpublished, as valuable sources of knowledge. However, ODG does not offer support for the integration of annotated genetic data, which is necessary for crop improvement, and it is likely to be difficult for non-expert users to understand its model and its underlying potential (Misra et al., 2019; Kaur et al., 2021).

The legume family (Leguminosae or Fabaceae) is the third largest family of flowering plants, with about 750 genera and nearly 19,500 species (The Legume Phylogeny Working Group et al., 2013). The Leguminosae include many taxa of agricultural or other economic importance and significant research efforts are needed to advance legume breeding and address the new challenges imposed to agriculture, namely production under climate change, with less pesticides and fertilizers. *Pisum sativum* L. (pea), *Lens culinaris* Medik. (lentil) and *Vicia faba* L. (faba bean) are examples of grain legumes that produce protein-rich seeds and play a key role in sustainable cropping systems (Guiguitant et al., 2020; Rubiales et al., 2021; Semba et al., 2021). Because of their large genomes, sometimes up to 30 times larger than the genome of the model legume *M. truncatula* (Jayakodi et al., 2023), the creation of omics data on these species has lagged behind. In addition, data on a given species were mostly produced by the research community in the country of production, as the dominant production areas are sometimes different. Several databases have been developed that attempt to inventory the diversity of published datasets and provide tools to analyse and visualize them, including Soybase (Grant et al., 2010), the Pulse Crop Database (Humann et al., 2019), KnowPulse (Sanderson et al., 2019) and the Legume Information System (Berendzen et al., 2021). However, there is still a lack of options to link multi-species datasets together for further study.

LegumeIP is a relational database, initially created to transfer knowledge from model to crop legume species, and recently transformed into an integrative platform to support translational research, hosting homology, gene annotation and expression data for 17 legume species in its latest version (Li et al., 2012, 2016; Dai et al., 2021). Some recently sequenced cool-season legumes are however missing, including *P. sativum* (Kreplak et al., 2019), *L. culinaris* (Ramsay et al., 2021) and *V. faba* (Jayakodi et al., 2023). In addition, the interface of LegumeIP is designed to facilitate pairwise comparisons, from model species to less studied crop species, making the current design unsuitable for simultaneous comparison of multi-species experiments.

Here, we developed Ortho_KB, a robust framework for translational research in diploid plant species. We developed a first pipeline to compute homology and define syntenic chromosomal regions across species. This method was chosen to identify putative orthologs among homologs, thus establishing links between corresponding genes and connecting chromosomes. We designed a second pipeline to execute custom scripts that reformat all heterogeneous data files, including omics datasets, for input into the database. Users can integrate published and unpublished information related to their species of interest including genotype associations from QTL data and expression information from transcriptomic resources and use the provided framework

to get the most out of their data. Ortho_KB provides an intuitive database model that can be queried using Cypher language, to extract meaningful information in comma-separated values (CSV) files for further analysis. The framework has been applied to a subset of legume species, resulting in a database called OrthoLegKB, a multi-species and multi-omics graph-based database for collecting, integrating and querying heterogeneous data. OrthoLegKB currently allows the comparison of genetic, and omic data from 5 legume species, i.e., *P. sativum*, *V. faba*, *L. culinaris*, *Vigna radiata* (L.) R.Wilczek and *M. truncatula*. Finally, a use-case is described to demonstrate how the combination of quantitative genetics and expression data is possible in OrthoLegKB and can benefit translational research.

# Materials and methods

## Orthology and synteny

As illustrated in Figure 1A, in order to identify homologous genes and syntenic regions, genome FASTA and annotation files as well as an optional conversion table for chromosomes are used as input files. The conversion table must include the original chromosome ID and the desired ID in the database. Unique chromosome and scaffold IDs across species are more convenient for querying and are also required by synteny-visualization tools such as SynVisio (Bandi and Gutwin, 2020). The steps for synteny and orthology discovery are the following: (1) curate annotation files using the agat_convert_sp_gxf2gxf.pl parser from agat v0.9.1 by automatically removing duplicated features and/or IDs, inferring missing IDs or parent features; (2) filter annotation files to keep only the longest isoform using the agat_sp_keep_longest_isoform.pl script; (3) extract coding DNA sequences (CDS) using the agat_sp_extract_sequences.pl script (Dainat et al., 2022); (4) generate protein sequences using the Seqkit v2.3.0 translation module (Shen et al., 2016); (5) submit protein sequences in FASTA format to OrthoFinder v2.5.4 with its default parameters using Diamond v2.0.12 in ultra sensitive mode for the alignment instead of BLAST (Emms and Kelly, 2015, 2019; Buchfink et al., 2021). Finally, to connect homologous chromosomal regions, the OrthoFinder output is used to obtain syntenic blocks. First, alignment files are filtered to retain only pairs of proteins that are part of the same orthogroup. Second, these filtered alignment files are provided to MCScanX along with a merge of annotation files from all considered species (Wang et al., 2012). A minimum number of 10 genes to form a collinear block is set by default in the pipeline. All above-mentioned steps were included in a single pipeline, called the structural pipeline, using Nextflow (Di Tommaso et al., 2017).

## Functional gene annotation

Functional annotations were conducted by manually submitting CDS sequences to annotation tools either available on online platforms or to be run locally. The TRAPID online tool returned gene families, RNA families, and Gene Ontology (GO) terms associated with submitted genes (Bucchini et al., 2021).
<figure><img src="image_42.png" /></figure>

Genes that share sequence homology are gathered in gene families. “GeneFamily” nodes hold links to the Plaza website on which information regarding family-associated GO annotations and InterPro domains are available (Van Bel et al., 2022). To attribute

summarized functions to genes, we assigned MapMan bins using the online Mercator4 (https://www.plabipd.de/portal/web/guest/mercator4) which resulted in a hierarchical annotation (Lohse et al., 2014; Schwacke et al., 2019). We also used eggNOG-mapper
to obtain human-readable annotation and gene symbols from protein sequences (Huerta-Cepas et al., 2019; Cantalapiedra et al., 2021). A name is assigned to each gene when available in the literature. For instance, *MtrunA17Chr3g0135361* is annotated as *ELF3* for *EARLY FLOWERING 3*. Predicted proteins of each species were further annotated by locally running InterProScan v.5.53 with the “iprlookup” option (Jones et al., 2014; Blum et al., 2021), notably using databases such as Pfam (Mistry et al., 2021), Gene3D (Lees et al., 2012) or PANTHER (Mi and Thomas, 2009).

## Genetic data extraction

The exact set of mandatory and optional information required to describe QTL data in Ortho_KB are described in the documentation available on the dedicated Git repository (see “Data availability statement” section). Briefly, the identifier, the trait name, and the associated markers are essential. A QTL arising from a study on a biparental mapping population is defined by a physical position on a chromosome between two flanking genetic markers and a peak marker within the confidence interval, if the information is available. A QTL from a genome-wide association study (GWAS) analysis is defined by a single marker location on a chromosome corresponding to the peak marker unless linkage disequilibrium data are available, then data is processed similarly to a QTL from a biparental population. Therefore, a QTL record might have information for one up to three markers. QTL data in the current version of OrthoLegKB were collected from published research articles (Supplementary Table S3).

## Transcriptomic data extraction and expression quantifications

RNA-seq datasets were manually selected from NCBI. Sample IDs associated within a BioProject were collected in each case using research from entrez-direct v16.2 (Kans, 2013). The sample list was fed into nf-core/fetchngs pipeline v1.7 with the option “nf_core_pipeline rnaseq” to obtain all FASTQ files along with a metadata file (Patel et al., 2022). The nf-core/rnaseq v3.8 pipeline (Patel et al., 2023) was then run with the genome files, metadata file and FASTQ files with the arguments “skipAlignment”, “pseudo Aligner salmon” and “salmonaderlibtype A” to automatically assess strandedness (Patro et al., 2017). Salmon result files were finally processed into matrices for downstream analyses using tximport (Soneson et al., 2016). The “salmon.merged.gene_counts.tsv” file containing read counts and the “salmon.merged.gene_tpm.tsv” file containing the Transcript Per Million (TPM) normalized quantification were used for further processes. Samples listed in the metadata file originating from the nf-core/fetchngs pipeline were manually annotated to indicate the tissues used, the environmental conditions applied, and the experimental area (field, greenhouse, etc.), using the Plant Ontology (PO) and the Plant Experimental Condition Ontology (PECO) (Cooper et al., 2018).

# Database construction and implementation

## Graph database conceptualization

The current release of Ortho_KB was built as a NoSQL database framework to store and display data in a graph structure, using the Neo4j Community Edition v4.4.18 (Neo4j, 2023b).

We chose the Neo4j graph-database management system because of (1) its efficiency in handling highly connected data, (2) the graph algorithms already implemented and (3) the expressive Cypher query language it uses and (4) its capacity to import/export data using semantic web technologies. Entities, also called nodes, and edges, also referred to as relationships, were designed in a way to carry the biological information. Each gene or transcript is represented by a node and each gene is linked to its corresponding transcript by a relationship (e.g., gene A has a transcript RNA A1). Multiple properties can be stored and queried on nodes (e.g., RNA A1 sequence length) and relationships (e.g., position of a protein domain on the protein sequence). In addition, one or more labels can be applied to nodes to group them into a set to facilitate querying. In this paper, labels are indicated by double quotation marks, for instance the “RNA” label for nodes of transcripts.

## Input files processing

A Nextflow pipeline called functional pipeline, was created to process heterogeneous data from the previously described sources. The pipeline requires genome files, functional annotation files, RNA-seq files and QTL files to run. For the functional annotations, the pipeline includes a set of scripts to filter and format them into nodes and relationships, following the database model. For the GO annotations obtained from TRAPID, by default, only the most specific GO terms are retained for each gene by selecting those with parameter “is_hidden” equal to 0, resulting in a 90% reduction in the number of GO terms directly associated to genes. The GO W3C Web Ontology Language (OWL) file is downloaded and parsed to import the “is_a” and “part_of” predicates as relationships in the graph to allow graph traversal (W3C, 1994). Similarly, the provided annotation files from MapMan are used to create an ontology in TURTLE syntax using rdflib v.4.2.2 (Grimnes et al., 2023). For RNA-seq, salmon pseudo-counts are by default filtered to retain only genes for which the sum of TPM across samples is >5, to avoid creating many relationships for non-expressed genes. Gene expressions in all samples from the same condition are averaged, and both arithmetic and geometric means are stored on the edge between the “Gene” and the “Condition” nodes. For genetic data, previously formatted files are processed to identify genes included in the confidence interval of QTL using pybedtools v.0.9.0 (Quinlan and Hall, 2010; Dale et al., 2011).

Briefly, for all processes, the pipeline creates CSV files to populate the database and a summary file listing all CSV files to be imported in a format readable by Neo4j. All nodes and relationships that can be generated are described in Supplementary Tables S2, S3.

## Database implementation

A Bash script was written to create and populate the database. It includes three steps. The first step prepares the import environment by building a Docker container. Running the
Docker container will start the database, by default available at http://0.0.0.0:7474/browser/. The second step performs the import to populate the Neo4j database using the neo4j-admin import command. The third step imports the PO, PECO, GO and MapMan ontologies using the n10s.onto.import.fetch method from the neosemantics (n10s) plugin (Barrasa, 2022). The import creates a node per term, connected to broader terms by a "SCO" relationship obtained from the property rdf:subClassOf. The nodes of the resulting subgraph are then labeled according to their source (PO, PECO etc.) and connected to the rest of the graph using a set of Cypher queries.

## Plant species selection for OrthoLegKB

For this study, five species were chosen. These include the model legume *M. truncatula*, three cool-season legumes of agronomic importance, i.e., *P. sativum*, *L. culinaris*, *V. faba*, and a relatively distant warm-season legume species, *V. radiata*. All species belong to the Galegoids subclade, with the exception of *V. radiata*, which is part of the sister group, the Milletoids subclade. We selected the latest genomic data from *P. sativum* cultivar Cameor v.1 assembly (Kreplak et al., 2019), *M. truncatula* accession A17 v.5 assembly and v.1.9 annotation (Pecrix et al., 2018), *V. faba* accession Hedin/2 v.1.0 assembly (Jayakodi et al., 2023), *L. culinaris* cultivar CDC Redberry v.2.0 assembly (Ramsay et al., 2021) and *V. radiata* subsp. *radiata* cultivar VC1973A v.6 assembly (Ha et al., 2021). All genomes were assembled into chromosomes, generated using long-read technology, except for *P. sativum*. The *M. truncatula* annotation file was filtered to keep only features from EuGene and BioFileConverter. Gene prefixes were also modified using a custom script. Details on selected genome assemblies and genome statistics are available in Table 1.

## Data visualization

The visualization of the graph model was created using Arrows (Neo4j, 2023a). The UpSet plot was created using UpSetR (Conway et al., 2017). Visualization of large-scale synteny was performed with the SynVisio online tool (Bandi and Gutwin, 2020) or with tailored R scripts, while microsynteny was plotted using the R package gggenomes (Hackl and Ankenbrand, 2023).

## Hardware and query time

The server hosting OrthoLegKB is based on an OpenStack infrastructure, with 4 virtual CPUs and 8 Gb of RAM. For each query presented in the Results section, the average response time over five iterations was indicated.

## Results

### Ortho_KB is a framework for translational research in plant species

Studying a particular trait or gene often requires the collection of different types of information available on different websites and databases, for the species of interest as well as for close species. We have created Ortho_KB, a database framework built with successive pipelines to facilitate the exploration of all data relevant to a trait or gene of interest in a single environment. Ortho_KB provides a unique and multi-functional structure that can be populated with datasets of interest and then queried for comparative and functional genomics studies. The current Ortho_KB modeling aims at enabling translational research across a wide range of selected species by making data easily searchable and the process more straightforward. The framework relies heavily on orthology and synteny relationships to build bridges between species, and transfer and/or compare genetic and genomic information between them. A Nextflow pipeline, called the structural pipeline (Figure 1A), first identifies groups of homologous genes – orthogroups – based on protein sequence similarity. It then looks for conserved gene order between pairs of chromosomes, within or between species, to highlight collinear regions. Homologs in collinear regions are more likely to be orthologs and therefore have similar functions. A second Nextflow pipeline, called the functional pipeline, connects information from the first pipeline and additional data available from separate tables including gene annotation, gene expression and QTL positions (Figure 1B). All heterogeneous data are thus properly formatted for integration into the database.

### Ortho_KB uses Neo4j graph-database management system

The Neo4j graph-database management system handles entities as nodes and their connections as relationships. In Ortho_KB, the data model revolves around "Gene" nodes, characterized by their start and stop positions on chromosomes (Figure 2). The "Gene" nodes are connected to their putative transcript ("RNA") nodes, themselves connected to the predicted proteins ("Protein") resulting from the translation of their RNA sequences. Homology and collinearity information computed using the structural pipeline create bridges across species at the gene and the chromosome levels, respectively. The current version of Ortho_KB includes 29 core categories of nodes tagged either by a single label, like "Gene" nodes or by a set of labels, like "RNASeq" supplemented by "Condition". They are connected by directed relationships, sometimes bearing additional properties (Figure 2). Individual nodes are defined by a unique identifier. For example, a "Gene" node is defined by a gene ID, matching the feature ID from the General Feature Format 3 (GFF3) annotation file, unique across species. Ortho_KB can be queried through the web Neo4j Browser, the terminal or other interfaces provided by Neo4j (Neo4j, 2023b).

### Ortho_KB integrates different categories of data including gene annotation, genetic and transcriptomic resources

As shown in Figure 1, Ortho_KB gathers different categories of data.

In terms of functional annotation, complementary information sources are handled. These include TRAPID's gene families, GO annotations, MapMan bins and InterPro that are each integrated
<table><caption>TABLE 1 Specifications of species included in OrthoLegKB.</caption><thead><tr><td>Species</td><td>Genotype</td><td>Assembly size (Mb)</td><td>Number of chromosomes</td><td>Protein coding genes</td><td>Assembly references</td></tr></thead><tbody><tr><td>Lens culinaris</td><td>CDC Redberry</td><td>3,760</td><td>7</td><td>58,243</td><td>Ramsay et al., 2021</td></tr><tr><td>Medicago truncatula</td><td>A17</td><td>430</td><td>8</td><td>44,626</td><td>Pecrix et al., 2018</td></tr><tr><td>Pisum sativum</td><td>Cameor</td><td>3,920</td><td>7</td><td>46,905</td><td>Kreplak et al., 2019</td></tr><tr><td>Vicia faba</td><td>Hedin/2</td><td>11,900</td><td>6</td><td>34,221</td><td>Jayakodi et al., 2023</td></tr><tr><td>Vigna radiata</td><td>VC1973A</td><td>476</td><td>11</td><td>30,882</td><td>Ha et al., 2021</td></tr></tbody></table>

Values for “Protein coding genes” take into account a single isoform per gene.

<figure><img src="image_43.png" /></figure>
in a separate node type. TRAPID gene families and Mapman bins provide synthetic overviews of gene functions while GO annotations and InterPro provide detailed descriptions focusing on gene functions and protein domains, respectively (Figure 2).

Regarding genetic data, the model includes connections between genes and QTL information either resulting from QTL mapping in biparental populations or GWAS in diversity panels. Since the two mapping approaches are grouped with the “QTL” label, we added a second label, either “BiparentalPopulation” or “DiversityPanel” to differentiate them. All genes located within the confidence interval of a QTL are connected to the “QTL” node with a “COLOCALIZES_WITH” relationship. The closest gene to the peak marker is additionally connected to the “QTL” node with a “IS_CLOSEST_TO_PEAK” relationship with its distance to the peak marker included as a property. Additional information to describe a QTL are included in connected nodes such as the experimental geographical “Site”, the studied “Population” and the “Trait” (Figure 2).

For transcriptomics, we have developed scripts to handle read counts. Read counts can either be generated using the pipeline of Patel et al. (2023) to optimize comparability of data (see “Materials and methods” section), or according to the method chosen by the user. The user is also free to integrate data previously analyzed with other methods. Replicates originating from the same biological condition are summarized into a condition that has to be manually annotated with ontology terms describing best the experimental conditions and biological material. The PECO and PO ontologies were selected for this purpose (Cooper et al., 2018). Using n10s inference, this model allows to traverse the ontology and unveil datasets from experiments performed in similar conditions (Barrasa, 2022). If no ontologies are available to appropriately describe conditions, free terms might be introduced (Figure 2).

## OrthoLegKB was developed with Ortho_KB to provide a translational tool for grain legumes

To prove how Ortho_KB can serve translational approaches and the research goals of a scientific community, we chose to apply it to five diploid legume species belonging to the Galegoid (cool-season legumes) and Milletoid (warm-season legumes) clades creating the OrthoLegKB database.

To leverage data from all five species using comparative genomics, we started by searching for orthologs with the structural pipeline using the latest genome assemblies. The pipeline was run for 740 CPU hours with 20 CPUs allocated (7 h 40 in real time), with a maximum physical memory usage of 46 Gb. In total, 14,565 out of 29,428 total orthogroups (49.49 %) were shared by all five species and 8.24 % by all species excluding *V. radiata*, the only representative of the Milletoid clade (Figure 3).

Then, public datasets with QTL and RNA-Seq data were mined, annotated with the ontologies used in Ortho_KB and included in the database using the functional pipeline. The pipeline was run for 11 CPU hours (14 min in real time), with a maximum physical memory usage of 2 Gb. A list of these datasets is available in Supplementary Table S1. OrthoLegKB currently contains more

than 815,000 nodes and close to 15,000,000 relationships associated to the different types of data. The exact number of nodes in each category can be found in Supplementary Table S4.

## OrthoLegKB can be used to address various scientific questions including the conservation of the control of flowering time in legumes

As a use-case to demonstrate how to exploit OrthoLegKB, we searched for the orthologs of a previously-studied flowering time regulator, the *FLOWERING LOCUS T* from *M. truncatula* (*MtFTA1*) and sought evidences for potential conserved function across species. For this use-case, we have decided to work only on cool-season legume species. *MtFTA1* has been thoroughly studied (Hecht et al., 2007, 2011; Laurie et al., 2011; Cheng et al., 2021) and its physical position on *M. truncatula* chromosome 7 (Mt07) is known. It is identified as *Medtr7g084970* (Laurie et al., 2011; Cheng et al., 2021) or *MtrunA17Chr7_39606925_39618489* in the GFF3 of the Mt5.0 (r1.9) genome annotation version. The first step was the identification of candidate orthologous genes from *P. sativum*, *L. culinaris* and *V. faba*. Several candidates could be found across chromosomes through a single query in 11 ms (Figure 4). OrthoLegKB was then searched for syntenic blocks encompassing these candidate genes. Synteny between chromosome 3 from *P. sativum* (Ps03), chromosome 6 from *L. culinaris* (Lc06), chromosome 5 from *V. faba* (Vf05) and Mt07 at the *MtFTA1* locus was revealed highlighting the orthologs (Figure 5A). The syntenic blocks in *L. culinaris* and *V. faba* displayed each one orthologous *FTA1* gene, while two possible orthologous genes were detected in *P. sativum* namely *Psat3g090720* and *Psat3g090680* (Figure 5B). According to the conservation of protein length and domains’ annotation information from the PANTHER database stored in OrthoLegKB, *Psat3g090720* seemed to be more similar to *MtFTA1* (Figure 6). In fact, *Psat3g090680* corresponds to *FTA2* described in Hecht et al. (2011). To examine any possible links with flowering control and thus function conservation, we searched for all QTL related to flowering contained in the previously identified syntenic blocks, allowing to also return QTL that did not include *FTA1* genes in their confidence intervals (Supplementary Table S5). As depicted in Figure 7, the query identified three QTL from Aguilar-Benitez et al. (2021) on Vf05, located at the same nucleotidic positions, that were linked to the number of days from the sowing until 50 % of the plants had visible open flowers (DF50_09-10(2)_1) and the number of days from the sowing until the appearance of the first flower (DF1_07-08(3)_1 and DF1_06-07(2)_1). On Ps03, a QTL from Gali et al. (2018) corresponding to the number of days to flowering (PR15_26_1) was found upstream of the *FTA1* locus (2018). Two QTL from Williams et al. (2022), closer to the *P. sativum* locus and associated to the number of days to flowering (DTF3_1) and number of nodes on the main stem to the first flower in long days (DTF3_3) were also identified. The *L. culinaris* *FTA1* gene was the only gene to be part of the confidence interval of a flowering-related QTL, qDTF.6-2_1. qDTF.6-2_1 is a number of days to flowering QTL from Haile et al. (2021), and close to the qDTFL-6A_1 from Yuan et al. (2021) related to the number of
<figure><img src="image_44.png" /></figure>

days to flowering under low red/far-red light quality. Regarding expression, the *MtFTA1* gene is known to be mainly expressed in leaves and stems in *M. truncatula* (Laurie et al., 2011; Thomson et al., 2019). Therefore, we sought to investigate the top three tissues from the shoot system in which its orthologs were mostly expressed. Thanks to the inference allowed by the annotation of conditions with ontologies, we show that in the collection of RNA-seq datasets available for *M. truncatula* in OrthoLegKB, *MtrunA17Chr7_39606925_39618489* was mainly expressed in vegetative shoot apex, reproductive shoot apex and vegetative shoot system. *Psat3g090720* was expressed in the peduncle, stem and leaf tendrils. Transcripts from *Vfaba.Hedin2.R1.5g087000* were

predominantly detected in adult vascular leaves, pods, and stems. For *L. culinaris*, all the experiments integrated were performed on leaves, in which the expression of *Lcu.2RBY.6g043850* was detected (Figure 8) and more particularly under far-red light conditions.

Discussion

This paper presented the use of knowledge graphs to integrate genetic and -omics data with the aim of facilitating translational research. The main philosophy was to provide a single environment where heterogeneous datasets from multiple species can be
<figure><img src="image_45.png" /><figcaption>FIGURE 4</figcaption></figure>

Illustration of the query used to search for putative orthologs of *MtFTA1* in OrthoLegKB. Putative orthologs in pea (psat), lentil (lcul), faba bean (vfab) and mung bean (vrad; **top panel**) were queried in Cypher (**middle panel**), and several properties were returned in CSV format (**bottom panel**). Genes belonging to the same orthogroup as *MtFTA1* were selected and their positions on the respective chromosomes were returned. Note that relationships' names were not displayed in the query section to keep it concise but were specified when running the query. The number of records returned in the output table and the average response time of the query are shown in light gray below the table.

accessed and examined with quasi-instantaneous querying time, thus allowing to address relevant biological questions, generate hypotheses, and transfer information from a single or group of species to others. The current version of the framework handles genome annotations, QTL and transcriptomic data. Users can identify orthologs, highlight candidate genes for specific traits, pinpoint possible pleiotropy and reveal conserved functional synteny. Ortho_KB gives the opportunity to capitalize on both published and unpublished datasets for further valorisation. The interest of such a database was demonstrated by populating Ortho_KB to create OrthoLegKB, a database dedicated to research on legume crop species, and supported by a use-case study focusing on a flowering-time gene.

## Ortho_KB leverages recent analytical workflows and ontology standards to host high-quality data and ensure comparability across datasets

The Ortho_KB framework was built with the hypothesis that homologous genes found in collinear regions are most likely to be orthologs. Collinearity mitigates the effects of genome duplication and fractionation and thus most likely pinpoints true orthologs (Tang et al., 2008). Besides bridges between genomes based on orthology, additional information layers were incrementally integrated and connected to gene entities, taking advantage of the modeling flexibility allowed by Neo4j. The integration of

such information was planned following homogenization rules for quality purposes. For expression data, we chose to use a single pipeline to process all transcriptomic datasets and avoid prejudice related to discrepancies in bioinformatic analysis protocols including alignment procedure, GC bias treatment. A similar initiative was taken for the gene atlas dedicated to *M. truncatula* (Carrere et al., 2021). We further decided to integrate normalized expression but not differential expression (DE). In fact, since the aim with Ortho_KB is to explore gene expressions across multiple samples and experiments, including expression in the form of DE would restrict analyses to a specific imposed comparison. Yet, the support for differential expression might be provided in the near future. Several actively updated ontologies (PO, PECO) were further selected to best describe the various experimental conditions from which the transcriptomic data were obtained. Since it requires human expertise, the annotation of samples with ontologies remains manual in the current version of the framework.

Regarding QTL, and unlike transcriptomics data, the reprocessing approach in sake of comparability could not be established so far as the analysis requires access to metadata, which are often sparsely provided in the literature. However, as FAIR standards are gaining in popularity, a unified approach might be considered for genetic data analysis in an upcoming version (Wilkinson et al., 2016). To ensure that positions of QTL for similar traits can be compared within and between species, homogeneity in traits denominations is required. This constraint is difficult to meet as a trait can be measured or named differently. For example, the flowering time might be considered by some authors as the time
<figure><img src="image_46.png" /></figure>

<caption>FIGURE 5</caption>

Macro- and micro-synteny of the chromosomal regions harboring *FTa1* or its orthologs in *M. truncatula*, *P. sativum*, *L. culinaris* and *V. faba*. (A) Macro-synteny at the chromosome level. *FTa1* and its orthologs are represented by gray dots on syntenic chromosome sections depicted as lines. Synteny between chromosomes is represented by ribbons. The positions of the two orthologs from *P. sativum* are shown even though they do not belong to any syntenic block in the database. (B) Micro-synteny of the *FTa1* loci. Genes are represented with arrows indicating the orientation of the open reading frames. Ribbons connect orthologous gene pairs. The IDs of *FTa1* orthologous genes are in orange and ribbons connecting them are filled in dark green. Since the four species have high genome size heterogeneity and variable intergenic sizes, intergenic regions were removed from the plot. Some gene names are not displayed due to space limitations. However, the gene sizes remain proportional.
<figure><img src="image_47.png" /></figure>

until the first plant has flowered, 50% of the plants have flowered or even 90% of them. Flowering time can also be expressed as the number of days between sowing and flowering or the number of degree-days. Nonetheless, a common vocabulary can be achieved with multi-species ontologies and needs to be developed. Such initiatives exist, such as the BBCH-scale framework to describe the phenological development stages of plants and serialized in RDF (Roussey, 2021), with instances for pea and faba bean but remain under-utilized. In the case of legumes, a higher-level ontology, not restricted to phenological stages, could use existing legume ontologies from the Crop Ontology, including the Lentil Ontology (CO_339) and the Faba bean Ontology (CO_365) (Shrestha et al., 2012). A general, consensus, ontology will however require manual work for the mapping of ontologies and its curation (Oellrich et al., 2015; Laporte et al., 2016; Cooper et al., 2018).

## The graph model of Ortho_KB is intended to be regularly updated to enhance querying possibilities

The current version of Ortho_KB includes QTL and expression data but only allow the comparison of species based on single reference genomes. Lately, efforts on pangenomes and on the description of large diversity panels highlighted the importance of considering a wider set of accessions rather than a single representative one. As a first step toward the integration of structural variation, we intend to upgrade the graph model to allow hosting polymorphism variants in Ortho_KB. Since single nucleotide polymorphism (SNP) matrices constitute a large

amount of data, the filtering and modeling will have to be thoroughly tested.

At the functional level, Ortho_KB presently provides solely transcriptomic data evidences. To provide complementary evidences regarding the function of genes of interest and their regulation at the post-transcriptional level, we plan to support the integration of proteomic data with Ortho_KB. This addition is also motivated by the ongoing standardization of proteomics output such as the mzTab format and downstream analyses (Griss et al., 2014; Ewels et al., 2020; Deutsch et al., 2022; Dubbelaar et al., 2022). Indeed, a recently published knowledge graph designed for clinical proteomic data namely the Clinical Knowledge Graph (CKG) accepts community-developed formats including mzTab and SDRF for metadata (Santos et al., 2022). Combining omics layers can bring further evidence to a hypothesis and also open doors toward the understanding of complex underlying phenomena. Since, Ortho_KB was designed to be modular, one could even consider the inclusion of epigenomics information to gain insights on chromatin rearrangement during stress conditions for example. In this case, the integration of non-genic regions such as promoters and enhancers in the database could be evaluated.

## Ortho_KB should be constantly evaluated to maintain performance and to facilitate its integration in the current databases ecosystem

As more biological data and data types are included, the Ortho_KB framework will have to be regularly fine-tuned to find
<figure><img src="image_48.png" /></figure>

the optimal graph model, but also in terms of the underlying configuration. In fact, for both the orthology backbone and the additional layers of the graph, single-property indexes have been created on properties that are regularly used as anchors to improve search performance at a small cost in storage space. Further guidance on the configuration of Neo4j has been previously published and will help to ensure high efficiency and scalability of Ortho_KB (Yoon et al., 2017). Several platforms already exist to study comparative genomics (Lyons and Freeling, 2008; Van Bel et al., 2022). The goal of Ortho_KB is different, since it mainly uses orthology and synteny as a way to transfer curated knowledge across species. Therefore, any created instance can

be queried freely to answer complex tailored questions in a comprehensive manner.

As OrthoLegKB is primarily populated with published datasets, interoperability with already existing databases is essential. For RNA-seq, the NCBI Sequence Read Archive stores datasets according to defined rigorous standards (NCBI, 2023). QTL data, on the other hand, are typically scattered across multiple databases that store the information in different formats. Unlike the GWAS Catalog available for human (Sollis et al., 2023), no integrative databases store legumes QTL data in a unified format. Therefore, we plan to facilitate the integration of the content from existing legume databases. Other knowledge graph to understand the
<figure><img src="image_49.png" /></figure>

role of genes are already available. The KnetMiner software was created to analyse genome-scale knowledge graphs, with a recent support for the Cypher graph query language (Hassani-Pak et al., 2021). This platform allows to build gene networks based on semantics and information primarily extracted from the literature, including genetic data, phenotypes associated to SNPs or biological pathways. It was recently applied to wheat, generating networks for the *TT2* gene involved in pre-harvest sprouting (Hassani-Pak et al., 2021). In the specific case of legumes, the AgroLD triplestore is to our knowledge the only phenomics agronomy-centered database aiming at an integrative storage of biological information in the form of a knowledge graph (Venkatesan et al., 2018; Larmande and Todorov, 2021). Since Neo4j can handle RDF import and export, thanks to the neosemantics plugin, data exchange between OrthoLegKB and AgroLD could be considered to take advantage of both technologies. This goal is further supported by the ongoing development of the RDF-star extension which could support properties on edges of the graph (Abuoda et al., 2022). This would bridge the gap between LPG and RDF technologies for improved interoperability (Hartig, 2014). While the SPARQL RDF query language is common to all triplestores, Cypher from Neo4j is only used by the proprietary. However, the open-source GraphQL initiative known as GQL is seen as a potential technology agnostic standardization query language for graph databases (Donkers et al., 2020). We envisage that the legume research community will participate in the data collection and provide feedback on OrthoLegKB for regular improvement.

## Ortho_KB offered an opportunity to develop a valuable tool for translational research in legumes, OrthoLegKB

We decided to select legume species to showcase how the Ortho_KB framework can serve translational research. OrthoLegKB is currently centered on few members mostly diploid cool-season legumes as the identification of orthologs is more straightforward than in polyploid species. Still, having a high-quality assembly is crucial for synteny detection and therefore true orthologs identification. The *FTa* locus in *P. sativum* (*PsFTa*) is in fact incorrectly assembled and annotated in the current version of the Cameor genome. While the *FTa1* gene we identified in *P. sativum* (*Psat3g090720*) is consistent with results from Hecht et al. (2011), the other copy (*Psat3g090680*) was reported as *FTa2* in the same study, both genes displaying similar expression patterns in leaves and apices, but with a weaker expression for *FTa2* (Hecht et al., 2011). In our study, the incomplete annotation of *Psat3g090680* in the version 1 assembly of *P. sativum* cv. Cameor most-likely prevented the creation of orthogroups correctly encompassing the *FT* gene family, and the subsequent inclusion of *PsFTa1* in syntenic blocks. The locus displayed increased synteny with the other studied species, this time including *PsFTa1* and *PsFTa2* when considering the more recent *P. sativum* genome assembly from the Zw6 accession (Yang et al., 2022). A new assembly of the Cameor genome is expected soon and should improve the assembly and annotation of this region. Furthermore, supplementing OrthoLegKB with transcriptomic data will provide
stronger support when searching for *FT* orthologous genes, by comparing their expression profiles. Fortunately, more legume genomes, have been lately assembled in high-quality using high-throughput chromosome conformation capture sequencing or long-read technologies, namely chickpea (Garg et al., 2022) or common vetch (Xi et al., 2022), which might reveal to be novel sources of data for OrthoLegKB. Thus, the graph will encompass more connected datasets, including information on abiotic and biotic stress response and be useful to a larger part of the legume research community.

## The Ortho_KB framework is for the plant community and beyond

As demonstrated for legumes, the Ortho_KB framework is suitable for translational research within plant families to address common biological questions. Therefore, Ortho_KB could for instance be used in Solanaceae to study late blight attacking potato, tomato but not eggplants nor pepper. Genomes were sequenced for all these diploid species with long-reads technologies (Pham et al., 2020; Wei et al., 2020; Su et al., 2021; Liao et al., 2022). With more caution regarding the identification of orthologs, this resource would also meet the needs of research across plant families, or the needs of polyploids in the Brassicaceae and Poaceae families. Precise study of gene expression bias would be then crucial to identify expressologs (Das et al., 2016). While the scope of Ortho_KB was limited to plants for annotation reasons, its concept could be adapted for the benefit of other communities. For example, the wealth of draft-assembled diploid genomes profiting the Chelicerates community was recently exploited to highlight the conservation of chemosensory genes through comparative genomics (Vizueta et al., 2018). As more qualitative assemblies and associated omics data are generated across plant and animal groups, we can only anticipate that the need for integrative multi-species databases will increase and Ortho_KB can contribute in this regard.

## Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repositories and accession numbers can be found below in the article/Supplementary material. OrthoLegKB with its user-guide is available for legume translational research at http://ortholegkb.versailles.inra.fr/browser/. The functional pipeline for synteny is available at: https://forgemia.inra.fr/geapsi/pipeline/specifics_syntenymcscanx. The pipeline to create the translational database is available at: https://forgemia.inra.fr/geapsi/pipeline/specifics_ortho_kb. All

scripts used to create the figures presented, including microsynteny from OrthoLegKB data, are available at: https://forgemia.inra.fr/geapsi/ecp-paper/ortholegkb_data.

## Author contributions

BI, JK, and NT contributed to the conception and design of this work. BI was responsible for developing the pipelines, running the use-case, and writing the manuscript. JK and R-GF helped BI to build the graph database and provided bioinformatic support. GA collected information on the datasets to be included in OrthoLegKB and participated in the development of the use-case. JB provided ideas and managed funding acquisition. NT contributed to the scientific management of this work and was involved in drafting and writing the manuscript. All authors participated to manuscript revision, read, and approved the submitted version.

## Funding

BI is the recipient of a PhD grant from French Ministry of Higher Education and Research. The work was part of the SPECIFICS project (ANR-20-PCPA-0008) funded by the French Priority Research Programme Growing and Protecting crops Differently (PPR-CPA), part of the national investment plan operated by the French National Research Agency (ANR).

## 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.

## Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frai.2023.1191122/full#supplementary-material

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<figure><img src="image_50.png" /></figure>

OPEN ACCESS

EDITED BY
Qichun Yang,
The University of Melbourne, Australia

REVIEWED BY
Shuai Xie,
Changjiang River Scientific Research Institute
(CRSRI), China
Angela Kranz,
Helmholtz Association of German Research
Centres (HZ), Germany

*CORRESPONDENCE
Catherine Roussey
catherine.roussey@inrae.fr

RECEIVED 16 March 2023
ACCEPTED 14 August 2023
PUBLISHED 27 September 2023

CITATION
Amardeilh F, Aubin S, Bernard S, Bravo S, Bossy R, Faron C, Michel F, Raphel J and Roussey C (2023) Combining different points of view on plant descriptions: mapping agricultural plant roles and biological taxa.
*Front. Artif. Intell.* 6:1188036.
doi: 10.3389/frai.2023.1188036

COPYRIGHT
© 2023 Amardeilh, Aubin, Bernard, Bravo, Bossy, Faron, Michel, Raphel and Roussey. 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.

# Combining different points of view on plant descriptions: mapping agricultural plant roles and biological taxa

Florence Amardeilh¹, Sophie Aubin², Stephan Bernard³,
Sonia Bravo², Robert Bossy⁴, Catherine Faron⁵, Franck Michel⁵,
Juliette Raphel¹ and Catherine Roussey³,⁶*

¹Elzeard, Bordeaux, France, ²DipSO, INRAE, Paris, France, ³Université Clermont Auvergne, INRAE, UR TSCF, Clermont-Ferrand, France, ⁴MalAGE, INRAE, Université Paris-Saclay, Jouy-en-Josas, France, ⁵Université Côte d'Azur, Inria, I3S, Sophia-Antipolis, France, ⁶MISTEA, University of Montpellier, INRAE & Institut Agro, Montpellier, France

This article describes our study on the alignment of two complementary knowledge graphs useful in agriculture: the thesaurus of cultivated plants in France named French Crop Usage (FCU) and the French national taxonomic repository TAXREF for fauna, flora, and fungi. FCU describes the usages of plants in agriculture: "tomatoes" are crops used for human food, and "grapevines" are crops used for human beverage. TAXREF describes biological taxa and associated scientific names: for example, a tomato species may be "Solanum lycopersicum" or a grapevine species may be "Vitis vinifera". Both knowledge graphs contain vernacular names of plants but those names are ambiguous. Thus, a group of agricultural experts produced some mappings from FCU crops to TAXREF taxa. Moreover, new RDF properties have been defined to declare those new types of mapping relations between plant descriptions. The metadata for the mappings and the mapping set are encoded with the Simple Standard for Sharing Ontological Mappings (SSSOM), a new model which, among other qualities, offers means to report on provenance of particular interest for this study. The produced mappings are available for download in Recherche Data Gouv, the federated national platform for research data in France.

KEYWORDS

mapping, SSSOM, crop, taxa, thesaurus French Crop Usage, French national taxonomic repository, TAXREF, SKOS model

## 1. Introduction

While the Web of linked data makes more and more knowledge graphs available, their cross-use often remains a challenge. This study presents a dataset containing mappings between two knowledge graphs representing different points of view on the same objects. This mapping set should allow to query simultaneously these graphs to enrich object descriptions by combining these points of view. Agriculture offers a particular use case of mappings, linked to the modeling of cultivated plants. Several expertises are needed to describe a cultivated plant: farmer vs. agronomist, agronomist vs. ecologist. The scientific world (ecologists or agronomists) tends to use scientific names from taxonomic science to designate living organisms (plants, insects). These scientific names are stored in biological taxonomies. The world of users (farmers) generally uses vernacular names or domain specific categories (e.g., cereals)
to designate the living organisms involved in their practice. In parallel, a plant can have several usages in agriculture: (1) plants cultivated in a plot for production purposes such as vegetables or cereals, in other word crops, (2) weeds that appear on a plot without being cultivated for which farmers want to limit the development or remove them from the plot, (3) a first cultivated plant that provides a service to a second cultivated plant for production purposes. Both plants are cultivated on the same plot but sometime not at the same time. The first cultivated plant will be destroyed without being harvested and is called service plant. The second cultivated plant will be harvested and is called the crop. For reasons of conciseness, we will limit this article to the plant usages for production purposes that is to say crops.

We present our study on the mappings of two complementary knowledge graphs useful in the agricultural domain: the French Crop Usage thesaurus (FCU) and the French national taxonomic register TAXREF for fauna, flora, and fungi. FCU describes the usage of plants in agriculture: “tomatoes” are crops used for human food, “grapevines” are crops used for human food or beverage. It represents the farmers’ point of view. TAXREF describes biological taxa and associated scientific names: for example, a tomato species may be “Solanum lycopersicum” or a grapevine species may be “Vitis vinifera”. TAXREF represents the agronomists’ point of view. Both knowledge graphs contain vernacular names of plants. Vernacular names are often ambiguous and not consensual, which renders the matching activity particularly challenging.

Our previous studies (Michel et al., 2022) have implemented several automatic alignment methods based on vernacular names comparison. Those automatic methods reused existing reference sources such as EPPO global database<fnref n="1" /> and the official French catalog of species and varieties of cultivated plants GEVES.<fnref n="2" /> The results show that it is necessary to clean the automatically produced alignments due to the ambiguity of vernacular names. Therefore, a group of agricultural experts has produced a set of valid mappings. Those mappings are published as open data on the French Recherche Data Gouv repository.<fnref n="3" /> Thus, they could be used as a gold standard to validate any automatic alignment methods.

The remainder of the study is organized as follows: Section 2 describes first the knowledge graphs and vocabularies used in our mapping set (Section 2.1), followed by the manual method applied to align the two knowledge graphs (Section 2.2). Section 3 presents our analysis of the challenge encountered in matching the graphs and representing the mapping set using the SSSOM model. In Section 4, we summarize the results and provide an outlook for future improvement.

# 2. Methods

## 2.1. Materials

First, we describe in detail the two aligned knowledge graphs: TAXREF-LD and FCU. Second we present the RDF vocabulary

that we defined to declare new types of mapping relations between plant descriptions. Indeed, SKOS properties are not sufficient to align an agricultural usage with a scientific taxon. Third, the SSSOM vocabulary is presented to store the set of mappings and their metadata.

### 2.1.1. TAXREF and TAXREF-LD

TAXREF (Gargominy et al., 2021) is the French taxonomic repository for fauna, flora, and fungi. In addition to a Web portal, a REST service, and a set of downloadable CSV files, TAXREF is available in the form of a knowledge graph complying with the Linked Data principles, named TAXREF-LD (Michel et al., 2017). TAXREF-LD is available on the AgroPortal repository.<fnref n="4" /> This study has been developed using the 15.2 version of TAXREF-LD which contains 287,229 classes and more than 1,000,000 instances.

To accurately reflect the distinction between taxonomy (a taxon gathers biological individuals that share common characteristics) and nomenclature (the scientific names assigned to taxa), TAXREF-LD has two distinct levels of modeling, as shown in Figure 1. At the taxonomic level, each taxon is modeled as an OWL class whose members are the biological individuals of that taxon. The parent class is the higher ranked taxon (e.g., “Daucus carota” is of rank species, the parent class “Daucus” is of rank genus). At the nomenclatural level, scientific names are represented as concepts in a SKOS thesaurus. Each name (instance of skos:Concept) is linked to a taxon (an OWL class) by a property indicating whether it is the reference name (accepted name in zoology or valid name in botany) or a synonym. The figure also presents vernacular names that are represented as a simple literal as well as a blank node of type skos xl:Label that reifies the vernacular name and makes it possible to provide additional information such as the geographic area in which this vernacular name is valid or a bibliographic reference. In addition to strictly taxonomic information, TAXREF-LD also represents other types of information not shown on this figure, such as habitats, conservation status, biogeographical status, interactions between species, and the bibliographical references associated with this information. Notably, TAXREF-LD sometimes associates the same vernacular name with several taxa. These vernacular names are taken from the publications where the scientific names are declared. Furthermore, TAXREF-LD is linked to several third-party taxonomic repositories including Agrovoc Thesaurus and NCBI Organismal Taxonomy.

### 2.1.2. French Crop Usage thesaurus

The French Crop Usage (FCU) thesaurus normalizes crop names in French. Moreover, it organizes these crop names in categories, according to their usages on the French territory. The usages represent also the agricultural sectors.

As shown in Figure 2, the thesaurus hierarchy has two main branches. The branch named “Multiusages” contains all the cultivated plants that have several usages in agriculture. For example, “carotte” (carrot) may be used as vegetable or fodder. The branch “Usages_plantes_cultivees” organizes cultivated plants according to their usages and represents agricultural sectors. In this

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

<footnote n="2">2 Catalogue officiel des espèces et variétés de plantes cultivées en France accessible at https://www.geves.fr/catalogue/.</footnote>

<footnote n="3">3 https://doi.org/10.57745/LVRFWJ</footnote>

<footnote n="4">4 https://agroportal.lirmm.fr/ontologies/TAXREF-LD</footnote>
<figure><img src="image_51.png" /><figcaption>FIGURE 1
Modeling principle of TAXREF-LD distinguishing taxonomic and nomenclatural information. The species taxon "*Daucus carota*" has for scientific name "*Daucus carota* L, 1758".</figcaption></figure>

branch, the crop usage "carotte potagère" is linked to the vegetable category "légume racine" (root vegetable).

The FCU thesaurus is formalized using the Simple Knowledge Organization System (SKOS) vocabulary proposed by W3C (Miles and Bechhofer, 2009). Each crop usage or category is represented by an instance of skos:Concept. The thesaurus is published on the Web using Linked Data principles.

The thesaurus is available on the AgroPortal repository.<fnref n="5" /> This study has been developed using the 3.3 version of FCU which contains 707 instances of skos:Concept. The maximum depth of the hierarchy is 6 levels. Each skos:Concept is defined by several properties, as shown in Figure 3. The description of a crop usage or category contains the following:

* The value of property skos:prefLabel is the crop name in French. The term is the vernacular name of the cultivated plant or the category name. To avoid ambiguity in the case of a cultivated plant with different usages, the crop name is the combination of the vernacular name of the plant and its usage. For example, in Figure 3, the crop name is "carotte" + "potagère".

* The value of property skos:altLabel is other possible labels that can be used for the crop. For example, in Figure 3, an alternative crop name is "carotte cultivée".

* The value of property skos:definition is the definition of the crop usage in French. The definition accounts for the crop position in the hierarchy.

* The value of property skos:note is at least one definition from another source, such as the French Wikipedia. The definition always ends by the indication of the source. For example, in Figure 3, the crop "carotte potagère" was found in The Official Catalog of Species and Varieties of Cultivated Crops in France.<fnref n="6" /> Thus, depending on the source, the same crop may have different names which show the ambiguity of crop names.

### 2.1.3. Mapping properties: taxon vs. usage

In TAXREF-LD, a taxon is defined by an OWL class, and the names of taxon are defined by instances of skos:Concept. In FCU, a crop usage is defined by an instance of skos:Concept.

We have defined 10 annotation properties to link an OWL class representing a taxon to an instance of skos:Concept representing a crop usage. The main annotation property is ontofcu:hasTaxon (/ its inverse property is ontofcu:hasUsage). This property (/ its inverse property) links a crop usage to a taxon. This relation indicates that the taxon is a candidate to fulfill the crop usage. For example, the species "*Daucus Carota*" can be used as "*carotte fourragère*" (fodder

<footnote n="5">5 https://agroportal.lirmm.fr/ontologies/CROPUSAGE/?p=summary</footnote>

<footnote n="6">6 https://www.geves.fr/catalogue-france/</footnote>
<figure><img src="image_52.png" /><figcaption>FIGURE 2
An extract from the FCU thesaurus, visualized with the SKOS Play tool.</figcaption></figure>

carrot). This property is specialized into four annotation properties (and their inverse) as follows:

* **ontofcu:hasGenericTaxon** ( / ontofcu:hasSpecificUsage) annotation property represents a relationship from a crop usage to a taxon. This relation indicates that one of the descendants of the taxon is the reference taxon to fulfill the crop usage. It is used when the descendant is not defined in the taxonomy source (e.g., TAXREF-LD). For example, the form "Cichorium intybus var. foliosum Hegi f. cylindricum" is known to fulfill the vegetable usage "chicorée pain de sucre" (sugarloaf chicory). Unfortunately, this form does not appear in TAXREF-LD. Its parent, the variety "Cichorium intybus var. Foliosum," belongs to TAXREF-LD. Thus, the mapping between "chicorée pain de sucre" (sugarloaf chicory) and the variety "Cichorium intybus var. Foliosum" will use the property ontofcu:hasGenericTaxon.

* **ontofcu:hasInvalidTaxon** ( / ontofcu:hasInvalidUsage) annotation property represents a relationship from a crop usage to a taxon. This relation indicates that the taxon can not be used to fulfill the crop usage. For example, the subspecies "Daucus carota subsp. gadecaei" is not a cultivated plant and can not be used as "carotte potagère" (vegetable carrot). This property is used to invalidate the output of automatic alignment tool.

* **ontofcu:hasReferenceTaxon** ( / ontofcu:hasReferenceUsage) annotation property represents a relationship from a crop usage to a taxon. This relation indicates that the taxon is the

reference known to fulfill the crop usage. For example, the species "Daucus Carota" is one of the reference taxa used as "carotte potagère" (vegetable carrot). The subspecies "Daucus carota subsp. sativus" is another reference taxon to be used as "carotte potagère" (vegetable carrot).

* **ontofcu:hasSpecificTaxon** ( / ontofcu:hasGenericUsage) annotation property represents a relationship from a crop usage to a taxon. This relation indicates that one of the descendants of the crop usage is the reference usage of the taxon. It is used when the descendant of the crop usage is not defined in FCU. For example, the variety "Solanum lycopersicum var. cerasiforme" is known to fulfill the crop usage "tomate cerise" (cherry tomato). Unfortunately, this type of tomato is not defined in FCU. Thus, the mapping between "tomate" (tomato) and the variety "Solanum lycopersicum var. cerasiforme" will use the property ontofcu:hasSpecificTaxon.

We have defined 12 object properties to link an instance of skos:Concept representing a scientific name to an instance of skos:Concept representing a crop usage. Those object property triples should be associated with annotation property triples listed above used as documentation. The main object property is ontofcu:hasScientificName ( / ontofcu:hasVernacularName). This property ( / its inverse property) is a relation from a crop usage to a taxon scientific name. Both are represented as an instance of skos:Concept. This relation indicates that the taxon scientific name is a candidate to identify the crop usage. For example, "Daucus carota L., 1753" can be the scientific name of the crop usage
Carottes potageres at French Crop Usage

http://ontology.inrae.fr/frenchcropusage/Carottes potageres

<figure><img src="image_53.png" /></figure>

<table><thead><tr><th>Property</th><th>Value</th></tr></thead><tbody><tr><td>skos:altLabel</td><td><ul><li>carotte cultivée (fr)</li></ul></td></tr><tr><td>skos:broader</td><td><ul><li>fcu:Carottes</li><li>fcu:Legumes_racines</li></ul></td></tr><tr><td>skos:definition</td><td>Plante potagère cultivée pour sa racine comestible par l'homme sous forme de légume: la carotte (fr)</td></tr><tr><td>skos:inScheme</td><td><ul><li>&lt;http://ontology.inrae.fr/frenchcropusage&gt;</li><li>fcu:def/thesaurus_FrenchCropUsage</li></ul></td></tr><tr><td>rdfs:isDefinedBy</td><td><ul><li>fcu:2.1</li></ul></td></tr><tr><td>dce:issued</td><td><ul><li>2021-05-25T00:00:00+01:01 (xsd:datetime)</li></ul></td></tr><tr><td>dct:issued</td><td><ul><li>2021-05-25T00:00:00+01:01 (xsd:datetime)</li><li>2021-05-25T00:00:00Z (xsd:datetime)</li></ul></td></tr><tr><td>dct:modified</td><td><ul><li>2022-06-30T00:00:00+01:01 (fr)</li><li>2022-06-30T00:00:00Z (xsd:datetime)</li></ul></td></tr><tr><td>skos:note</td><td><ul><li>Carottes (Daucus carota L.): sans fanes. Toutes les carottes pour l'alimentation humaine : primeur (type Nantaise améliorée ou assimilé), de saison ou ... »more« (fr)</li><li>La Carotte (Daucus carota subsp. sativus) est une plante bisannuelle de la famille des Apiacées (aussi appelées Ombellifères), largement cultivée pour ... »more« (fr)</li><li>Source Cultures et précisions. Campagne 2019. Liste des cultures à utiliser pour renseigner le descriptif des parcelles (fr)</li><li>Source Le Catalogue officiel des espèces et variétés de plantes cultivées en France (fr)</li><li>Source registre parcellaire data.gouv.fr (fr)</li></ul></td></tr><tr><td>skos:prefLabel</td><td>carotte potagère (fr)</td></tr><tr><td>rdfs:seeAlso</td><td><ul><li>&lt;http://www.larousse.fr/archives/agricole/page/115#t937&gt;</li><li>&lt;https://fr.wikipedia.org/wiki/Carotte&gt;</li></ul></td></tr><tr><td>rdf:type</td><td><ul><li>owl:NamedIndividual</li><li>skos:Concept</li></ul></td></tr></tbody></table>

This page shows information obtained from the SPARQL endpoint at http://ontology.irstea.fr/frenchcropusage/query.

As Turtle | As RDF/XML | Browse in Disco | Browse in Tabulator | Browse in OpenLink Browser

<caption>FIGURE 3</caption>

The information related to the skos:Concept instance "fcu:Carottes potageres".

<figure><img src="image_54.png" /><figcaption>FIGURE 4</figcaption></figure>

Some mappings between the crop usage "carotte potagère", the taxon "Daucus carota", its scientific name "Daucus carota L, 1758", and the taxon "Daucus carota subsp. sativus" using the CHOWLK language.

"carotte fourragère" (fodder carrot). Notably, the crop usage and the associated taxon should also be linked by the annotation property *ontofcu:hasTaxon* / *ontofcu:hasUsage*.

*   **ontofcu:hasGenericScientificName** (/ontofcu:hasSpecificVernacularName) object property represents a relationship from a crop usage to a taxon scientific name. This relation indicates that one of the descendants of the taxon name is known to be the scientific name of the crop usage. This is used when the descendant is not defined in the taxonomy. For example, the form scientific name "Cichorium intybus var. foliosum Hegi f. cylindricum" is known to be the scientific name of the vegetable usage "chicorée pain de sucre" (sugarloaf chicory). Unfortunately, this form does not belong to TAXREF-LD. But its parent, the variety scientific name "Cichorium intybus var. Foliosum Hegi, 1928" belongs to TAXREF-LD. Thus, the mapping between "chicorée pain de sucre" (sugarloaf chicory) and the variety scientific name "Cichorium intybus var. Foliosum Hegi, 1928" will use the property *ontofcu:hasGenericScientificName*. Notably, the crop usage and the associated taxon should also be linked by the annotation property *ontofcu:hasGenericTaxon*.

*   **ontofcu:hasInvalidScientificName** (/hasInvalidVernacularName) object property represents a relationship from a crop usage to a taxon scientific name. This relation indicates that the taxon scientific name can not be
used to identify the crop usage. For example, the subspecies "*Daucus carota* subsp. *gadecaei*" is not a cultivated plant. Thus, "*Daucus carota* subsp. *gadecaei* (Rouy & E.G.Camus) Heywood, 1968" is not the scientific name of "*carotte potagère*" (vegetable carrot). Notably, the crop usage and the associated taxon should also be linked by the annotation property *ontofcu:hasInvalidTaxon*.

*   **ontofcu:hasReferenceScientificName** (*ontofcu:hasReferenceVernacularName*) object property represents a relationship from a crop usage to a taxon scientific name. This relation indicates that the taxon scientific name can be used to identify the crop usage. For example, "*Daucus carota* L., 1753" is the reference scientific name of "*carotte fourragère*" (fodder carrot). Notably, the crop usage and the associated taxon should also be linked by the annotation property *ontofcu:hasReferenceTaxon*.

*   **ontofcu:hasSpecificScientificName** (*ontofcu:hasGenericVernacularName*) object property represents a relationship from a crop usage to a taxon scientific name. This relation indicates that one of the descendants of the crop usage is the reference vernacular name of the taxon. This is used when the descendant of the crop usage is not defined in FCU. For example, "*Solanum lycopersicum* var. *cerasiforme* (Alef.) Fosberg, 1955" is known to be the scientific name of the crop usage "*tomate cerise*" (cherry tomato). Unfortunately, "*tomate cerise*" does not belong to FCU. Thus, the mapping between "*tomate*" (tomato) and the variety scientific name "*Solanum lycopersicum* var. *cerasiforme* (Alef.) Fosberg, 1955" will use the property *ontofcu:hasSpecificScientificName*. Notably, the crop usage and the associated taxon should also be linked by the annotation property *ontofcu:hasSpecificTaxon*.

*   **ontofcu:hasSynonymousScientificName** object property represents a relationship from a crop usage to a taxon scientific name. This relation indicates that the scientific name is not the reference name but one of its synonyms and can also be used to identify the crop usage. For example, "*Daucus communis* Rouy & E.G.Camus, 1901" is the synonymous scientific name of "*carotte fourragère*" (fodder carrot). Notably, the crop usage and the associated taxon should also be linked by the annotation property *ontofcu:hasReferenceTaxon*.

*   **ontofcu:hasSynonymousVernacularName** object property represents a relationship from a taxon scientific name to a crop usage. This relation indicates that the crop usage is the synonymous vernacular name of the taxon scientific name. For example, FCU contains a new collection of crop usage dedicated to subsistence crops. A new crop usage "*carotte du jardin*" (garden carrot) is added which is defined as synonymous (same as) to "*carotte potagère*" (vegetable carrot). Thus, "*carotte du jardin*" (garden carrot) will be the synonymous vernacular name of "*Daucus carota* L., 1753." Notably, the both crop usages and the associated taxon should also be linked by the annotation property *ontofcu:hasReferenceTaxon*.

Figure 4 shows an exception of the mapping between the FCU concept “*carotte potagère*” and two TAXREF-LD

classes "*Daucus carota*" and "*Daucus carota* subsp. *sativus*."<fnref n="7" />

Those properties are visible on the property tab of AgroPortal.<fnref n="8" />

The generic properties *ontofcu:hasTaxon* *ontofcu:hasScientificName* (*ontofcu:hasUsage* *ontofcu:hasVernacularName*) can be used to declare any automatic mappings, potentially associated with their score but without validation concern. The generic properties just state that mappings were produced by any automatic method, and they are candidate mappings that need validation. The specific properties will be used to declare valid, cleaned, and precised mappings with their confidence value.

### 2.1.4. Alignment metadata model SSSOM

Simple standard for Sharing Ontology Mappings (SSSOM) is a recent standard model developed by the biomedical community around OBO Foundry and described by Matentzoglu et al. (2022). It provides a rich set of metadata to describe mappings (individual mappings between a pair of entities) and a mapping set (a set of individual mappings). For this work, we used SSSOM version 0.15 that was released in July 2023.

The main objective of the SSSOM project is to propose a catalog of metadata allowing to have information about the provenance of the mappings, whether they are calculated manually or automatically. The expected impact is an augmented trustworthiness resulting in an increased reuse of mappings by third parties. Meanwhile, there is also a real desire to produce a model that is easy to use. Indeed, the project proposes, on the one hand, the serialization in RDF/OWL for the Semantic Web community and, on the other hand, a TSV format for a larger community which can thus exchange mappings in a simple format yet with rich semantics.

We chose to publish the mapping set in the TSV format as a first step. We opted for the embedded mode where mapping set level metadata are integrated in the mapping TSV file as commented YAML (prefixed with #). The properties used to describe the mapping set are presented in Table 1 while those used to describe each mapping are shown in Table 2. Mandatory properties are marked with an asterisk (*). For each property, we indicate its description as stated in the SSSOM model version 0.15. In Section 3, we present a short analysis of advantages and limits of the SSSOM model.

## 2.2. Manual alignment method

Previously, we have tested some automatic alignment methods (Michel et al., 2022) that reused existing reference sources such as EPPO global database (see text footnote¹) and the official French catalog of species and varieties of cultivated plants GEVES (see text footnote²). The final automatic method computes a confidence score of the mapping between a taxon from TAXREF-LD and a

<footnote n="7">7 The UML based CHOWLK language is used to present the knowledge graph. More information available on https://chowlk.linkeddata.es/notation.html.</footnote>

<footnote n="8">8 https://agroportal.lirmm.fr/ontologies/CROPUSAGE/?p=properties</footnote>
<table><caption>TABLE 1 SSSOM (V0.15) properties used to describe our mapping set.</caption><thead><tr><td>Property</td><td>Description</td><td>Example</td></tr></thead><tbody><tr><td>mapping_set_id</td><td>A globally unique identifier for the mapping set (not each individual mapping). Should be IRI, ideally resolvable.</td><td>"https://doi.org/10.57745/LVRFWJ"</td></tr><tr><td>creator_id</td><td>Identifies the persons or groups responsible for the creation of the mapping. The creator is the agent that put the mapping in its published form, which may be different from the author, which is a person that was actively involved in the assertion of the mapping.</td><td>#creator_id: "https://ror.org/01pd2sz18"</td></tr><tr><td>creator_label</td><td>A string identifying the creator of this mapping.</td><td>#creator_label: "Mathématiques, Informatique et Statistique pour l'Environnement et l'Agronomie"</td></tr><tr><td>curie_map</td><td>A valid curie map that allows the unambiguous interpretation of CURIES.</td><td>#curie_map: #fcu: "http://ontology.inrae.fr/frenchcropusage" #taxref: "http://taxref.mnhn.fr/lod/taxref-ld"</td></tr><tr><td>subject_source</td><td>URI of ontology source for the subject.</td><td>#subject_source: "http://ontology.inrae.fr/frenchcropusage"</td></tr><tr><td>subject_source_version</td><td>Version IRI or version string of the source of the subject term.</td><td>#subject_source_version: "3.3"</td></tr><tr><td>object_source</td><td>IRI of ontology source for the object. Version IRI preferred.</td><td>#object_source: "http://taxref.mnhn.fr/lod/taxref-ld"</td></tr><tr><td>object_source_version</td><td>Version IRI or version string of the source of the object term.</td><td>#object_source_version: "15.2"</td></tr><tr><td>license</td><td>A url to the license of the mapping. In absence of a license we assume no license.</td><td>#license: "https://creativecommons.org/licenses/by/2.0/"</td></tr></tbody></table>

crop usage from FCU using EPPO database and GEVES catalog. The main problems come from name ambiguity: depending on the source (1) the scientific name may follow or not the botanical nomenclature code used in TAXREF-LD. For example, the scientific name of carrot may be “*Daucus carota*”, “*Daucus carota* L”, or “*Daucus carota* L, 1753”. (2) The vernacular name presents in the source may not match exactly the vernacular name displayed in FCU. For example, the vernacular name of carrot presents in the source may be “carotte sauvage” (“wild carrot”). This name does not appear in FCU. (3) The vernacular name present in the source may not identify precisely a crop usage, that is to say a FCU concept narrower than “*Usages_plantes_cultivees*”. For example, carrot has two crop usages in FCU “carotte potagère” (“vegetable carrot”) and “carotte fourragère” (“folder carrot”). The output of the automatic method should be cleaned by human experts due to the inaccuracy of name comparison.

Thus, we decided to create a new mapping set by asking experts to propose correct and well-known mappings between crop usages, taxa, and their scientific names. All the proposed mappings should have a high confidence value. If any ambiguity existed, the mapping should not be created. First, we provided the experts with guidelines to help them in their decisions. Second, some research tools were proposed to search terms into the two knowledge graphs. Third, three curation rules were written to contextualize the mappings they created and indicate the provenance of the mappings. We focused on specific crops: grapevine, carrot, chicory, and tomato according to the availability of experts.

As shown in Figure 5, two kind of experts are involved. First, the mapping reviewer proposes some mapping specifications based on its knowledge or other information source. A mapping specification looks like : the taxon “*Daucus carota*” is used as “carotte potagère” (“vegetable carrot”). Second, the reviewer

author has to find the correct URI from TAXREF-LD and FCU knowledge graphs to produce the SSSOM mappings following the mapping specifications.

### 2.2.1. Generic guidelines

We provide the following guidelines to help the experts create their mappings:

1. Only the mappings from FCU crop usages to TAXREF-LD taxa are represented. We focus on crop usages that belong to the branch “*Usages_plantes_cultivees*” to avoid ambiguity. The goal is to select the most specific crop usage from FCU and align it to some TAXREF-LD taxa using the properties defined in Section 2.1.3.

2. If possible the experts should select a reference information source used to identify the mappings. Each information source is associated with a curation rule to describe the mapping identification method.

3. First, the experts should create a mapping between a crop usage and its reference taxon using the annotation property *ontofcu:hasReferenceTaxon*. We impose that the first reference taxon has the rank species.

4. Second, if a more specific type of taxon, for example, a subspecies or a variety, is well known as the reference taxon of the crop usage, another mapping is created using the annotation property *ontofcu:hasReferenceTaxon*.

5. If the known reference taxon of the crop usage is not available in TAXREF-LD, another type of mapping should be used. A triple based on property *ontofcu:hasGenericTaxon* should be created.

6. Based on the above annotation properties, the mapping between the crop usage and the taxon scientific names
<table><caption>TABLE 2 SSSOM (V0.15) properties used to describe our mappings.</caption><thead><tr><td>Property</td><td>Description</td><td>Example</td></tr></thead><tbody><tr><td>subject_id*</td><td>The ID of the subject of the mapping.</td><td>fcu:Carottes_fourrageres</td></tr><tr><td>subject_label</td><td>The label of subject of the mapping.</td><td>carotte_fourragère</td></tr><tr><td>predicate_id*</td><td>The ID of the predicate or relation that relates the subject and object of this match.</td><td>fcu:def/hasReferenceTaxon</td></tr><tr><td>predicate_label</td><td>The label of the predicate/relation of the mapping.</td><td>Has reference taxon</td></tr><tr><td>object_id*</td><td>The ID of the object of the mapping.</td><td>taxref:taxon/133744</td></tr><tr><td>object_label</td><td>The label of object of the mapping.</td><td>Daucus carota subsp.sativus</td></tr><tr><td>confidence</td><td>A score between 0 and 1 to denote the confidence or probability that the match is correct, where 1 denotes total confidence.</td><td>1</td></tr><tr><td>mapping_justification</td><td>A mapping justification is an action (or the written representation of that action) of showing a mapping to be right or reasonable.</td><td>semapv: ManualMappingCuration</td></tr><tr><td>mappingCardinality</td><td>A string indicating whether this mapping is from a 1:1 (the subject_id maps to a single object_id), 1:n (the subject maps to more than one object_id), n:1, 1:0, 0:1 or n:n group. Note that this is a convenience field that should be derivable from the mapping set.</td><td>N:N</td></tr><tr><td>subject_type</td><td>The type of entity that is being mapped.</td><td>skos:Concept</td></tr><tr><td>object_type</td><td>The type of entity that is being mapped.</td><td>owl:Class</td></tr><tr><td>author_id</td><td>Identifies the persons or groups responsible for asserting the mappings. Recommended to be a (pipe-separated) list of ORCIDs or otherwise identifying URLs, but any identifying string (such as name and affiliation) is permissible.</td><td>https://orcid.org/0000-0002-3076-5499</td></tr><tr><td>author_label</td><td>A string identifying the author of this mapping. In the spirit of provenance, consider to use author_id instead.</td><td>Catherine Roussey</td></tr><tr><td>reviewer_id</td><td>Identifies the persons or groups that reviewed and confirmed the mapping. Recommended to be a (pipe-separated) list of ORCIDs or otherwise identifying URLs, but any identifying string (such as name and affiliation) is permissible.</td><td>https://orcid.org/0000-0002-5872-5034</td></tr><tr><td>reviewer_label</td><td>A string identifying the reviewer of this mapping. In the spirit of provenance, consider to use author_id instead.</td><td>Juliette Raphel</td></tr><tr><td>mapping_date</td><td>The date the mapping was asserted. This is different from the date the mapping was published or compiled in a SSSOM file.</td><td>2023-02-03T00:00:00Z</td></tr><tr><td>curation_rule_text</td><td>The textual representation of curation rule is intended to be used in cases where (1) the creation of a resource is not practical from the perspective of the mapping_provider and (2) as an additional piece of metadata to augment the curation_rule element with a human readable text.</td><td>CR_Experts: the experts found the mapping in the Official Catalog of Species and Varieties of Cultivated Crops in France [https://www.geves.fr/catalogue-france/]</td></tr><tr><td>comment</td><td>Free text field containing either curator notes or text generated by tool providing additional informative information.</td><td>Many subspecies of Daucus carota can be eaten and used as forage: the most well known is Daucus carota subsp. sativus</td></tr></tbody></table>

are derived. First, a mapping is created between the crop usage and the reference scientific name using the object property *ontofcu:hasReferenceScientificName*. In TAXREF-LD, the reference scientific name of a taxon can be found by following the link *taxref:hasReferenceName*.

7. If the reference source of information (or the experts' knowledge) indicates another scientific name than the one provided in TAXREF-LD as reference name, the experts should try to find it in the synonym names of the taxon and create a new mapping between the crop usage and the scientific name using the object property *ontofcu:hasSynonymeScientificName*.

The name of the experts are indicated in mapping information as mapping reviewer or mapping author. The reviewer is the expert who searched into the reference source the mapping information

(or who knew for sure the taxon that fulfills the crop usage). The author is the expert who searched into the knowledge graph the URI entity based on reviewer information. The reviewer information is synthesized into the comment of the mapping (see Table 2).

### 2.2.2. Search tools

To find a crop usage in FCU thesaurus, the following query interfaces are available:

One simple solution is to navigate through the AgroPortal interface concept tab,<fnref n="9" /> to select the most specific instance of *skos:Concept*. Figure 6 shows the hierarchy exploration to find the

<footnote n="9">9 https://agroportal.lirmm.fr/ontologies/CROPUSAGE/?p=classes</footnote>
instance named "*fcu:Carottes potageres.*". Notably, this interface presents first English labels, and if no English label is provided, the French labels are presented. Thus, Figure 6 presents a mixture of English and French labels.

To be sure to search French labels, another solution is to query the SPARQL Endpoint.<fnref n="10" /> For example, the query presented in Figure 7 search for an instance of *skos:Concept* that contains the French word "carotte" in their preferred French label.

As shown in Figure 7, there exist three *skos:Concept* instances that have a French preferred label containing the word "carotte." Remember that the expert should select FCU concept narrower than "Usages plantes cultivees". When clicking on one of the results of the SPARQL query displayed on Figure 7, e.g., *fcu:Carottes potageres*, the expert accesses the RDF description of the FCU concept, as depicted in Figure 3.

To find a taxon in TAXREF-LD, a text search interface is accessible at http://taxref.i3s.unice.fr/fct/. For example, looking for the text expression "*Daucus carota*" provides the results presented in Figure 8.

The experts should select the entity of the first line "taxref:taxon/94503." The prefix "taxref" indicates that the entity belongs to TAXREF-LD. The URL part "taxon" indicates that the entity represents a taxon in TAXREF-LD, that is to say an *OWL class*. Then, the experts click on the Web link to display more information about the entity, as shown in Figures 9, 10. By navigating through the Web interface, the experts follow the property "*has reference name*" to find its scientific name, as presented in Figure 11.

### 2.2.3. Curation rules

Three curation rules were created to document our mappings. The goal of curation rules is to prove that the source of information contains the mapping and can be used as mapping justification. Along with the rule, we indicate how to find the relevant elements in each of the mapped resources.

#### 2.2.3.1. CR_Geves

The *Official Catalog of Species and Varieties of Cultivated Crops in France* from GEVES is a good source of information to find which crop usage is associated with a taxon. As shown in Figure 12, this catalog indicates for each cultivar (seed used by farmers):

* Its vernacular name in the field *Common species*,

* Its crop usage in the field *Category*,

* Its scientific name in the field *Botanical species*.

Notably, most of the time, the taxon rank indicated in this catalog is a species. First, the most specific crop usage was selected in the FCU branch "Usages plantes cultivees" by using the information from fields *Common species* and *Category*. Second, the taxon with the scientific name indicated in the field *Botanical species* is searched in TAXREF-LD. If it is possible to find the crop

usage and the taxon without ambiguity, the mapping is created and its confidence value is fixed to one.

For example, based on the information provided in Figure 12 about a carrot cultivar, the expert should find in FCU thesaurus the instance of *skos:Concept* *fcu:Carottes potageres* (see Figure 7). The expert should also find in TAXREF-LD the instance of *skos:Concept* identified by *taxref:name/94503* (see Figure 11) and the *OWL class* identified by *taxref:taxon/94503* (see Figure 9). Thus, two mappings are created with a confidence value of one:

* One between the *fcu:Carottes potageres* crop usage and the *taxref:taxon/94503* species taxon using the *ontofcu:hasReferenceTaxon* annotation property.

* One between the crop usage *fcu:Carottes potageres* and the *taxref:name/94503* reference scientific name using the *ontofcu:hasReferenceScientificName* object property.

#### 2.2.3.2. CR_C3PO_KB

The Crop Planning and Production Process Ontology and Knowledge Base (C3PO KB) is a knowledge graph created by the Elzeard Enterprise (Darnala et al., 2021, 2022). This KG is another reference source about vegetable.<fnref n="11" /> The knowledge graph is accessible as several TTL files on a git repository.<fnref n="12" /> To create a mapping between a crop usage and a taxon, the experts should search into the TTL file related to the plant module.<fnref n="13" /> Figure 13 presents an excerpt of this file. The vegetable description contains a crop usage indicated by the property *c3poplant:hasFCUTaxon* and a TAXREF-LD scientific name indicated by the property *c3poplant:hasScientificName*. The experts should find the corresponding taxon by searching in TAXREF-LD Web interface (following the link "*is the reference name of*"). Based on those information, some new mappings are created with a confidence value of one.

For example, Figure 13 presents the RDF description in TTL format of the instance of *c3poplant:VegetablePlant* related to carrot. Based on this description, the expert can construct two mappings with a confidence value of one as follows:

* One between the *fcu:Carottes potageres* crop usage and the *taxref:taxon/133744* subspecies taxon using the *ontofcu:hasReferenceTaxon* annotation property.

* One between the crop usage *fcu:Carottes potageres* and the *taxref:name/133744* reference scientific name using the *ontofcu:hasReferenceScientificName* object property.

#### 2.2.3.3. CR_Experts

The experts can also use their own knowledge to state that a scientific name is linked to a given crop usage. In this case, the taxon name is searched in TAXREF-LD. If the scientific name is retrieved

<footnote n="10">10 http://ontology.inrae.fr/frenchcropusage/sparql</footnote>

<footnote n="11">11 The ontology is accessible on AgroPortal repository https://agroportal.lirmm.fr/ontologies/C3PO/?p=summary.</footnote>

<footnote n="12">12 https://gitlab.com/serre-des-savoirs/c3po-kb</footnote>

<footnote n="13">13 The file is untitled "cultivatedplants-infos-result-triples.ttl" and is accessible at https://gitlab.com/serre-des-savoirs/c3po-kb/-/blob/main/plants/rdf-generated%20triples/cultivatedplants-infos-result-triples.ttl.</footnote>
<figure><img src="image_55.png" /><figcaption>FIGURE 5
The overview of the method.</figcaption></figure>

<figure><img src="image_56.png" /><figcaption>FIGURE 6
The AgroPortal concept tab interface showing the vegetable carrot crop usage.</figcaption></figure>

and this name is the reference name for the taxon, two mappings are created as follows:

* One between the crop usage and the taxon using the `ontofcu:hasReferenceTaxon` annotation property.

* One between the crop usage and the scientific name using the `ontofcu:hasReferenceScientificName` object property.

If the scientific name is retrieved but the name is not the reference name for the taxon but a synonym name, three mappings are created as follows:
<figure><img src="image_57.png" /><figcaption>FIGURE 7
The results of the SPARQL query related to carrot.</figcaption></figure>

* One between the crop usage and the taxon using the *ontofcu:hasReferenceTaxon* annotation property.

* One between the crop usage and the reference scientific name using the *ontofcu:hasReferenceScientificName* object property.

* One between the crop usage and the synonym name using the *ontofcu:hasSynonymousScientificName* object property.

If the scientific name is not retrieved (for example, “Cichorium intybus var. foliosum Hegi f. cylindricum” is the scientific name of a form taxon), but the name of a parent taxon can be retrieved (“Cichorium intybus var. foliosum Hegi” is the scientific name of the variety taxon), the experts should restart the process by looking for the reference scientific name of the parent taxon. This implies that the properties used will be *ontofcu:hasGenericTaxon* and *ontofcu:hasGenericScientificName*.

The confidence value all these mappings is fixed to one.

# 3. Analysis

## 3.1. Alignment challenges

Although the taxonomies and lists of cultivated plants (or crop usages) refer to living organisms, their alignment raises several challenges that require manual curation in practice.

A taxonomy is a hierarchical structure that presents a set of hypotheses uttered by taxonomists on taxa and their relationships. The names of taxa are regulated by written conventions recorded in nomenclature codes. The current code in force for plants is the *International Code of Nomenclature for algae, fungi, and plants*, also known as the *Shenzhen Code* (Zhang et al., 2013). By their very nature, taxonomies change over time as the scientific consensus between taxonomists evolves. Taxa may merge, split, spawn, or change taxonomic rank (e.g., from *species* to *subspecies*). Nomenclature codes acknowledge this fact and were designed to stabilize taxon names as much as possible, given the versatility of taxonomies.

At a given time, a taxon has a single valid name and possibly a list of synonyms that have spawned from previous studies. Changes in the nomenclature are published on an *ad hoc* basis in the scientific literature. However, taxonomies and lists of cultivated plants are updated at a different pace. As a consequence, different references might not be completely up-to-date and may exhibit discrepancies on specific taxa. For instance, a list of cultivated plants may use a scientific name which is no longer valid or whose taxon has changed parent taxon or rank.

Additionally, nomenclature codes apply unambiguously to the species and subspecies taxon ranks but not to the lower ranks (e.g., *variety* or *cultivar*). On the other hand, the naming of cultivated
<figure><img src="image_58.png" /></figure>

OPENLINK
SOFTWARE

Displaying Ranked Entity Names and Text summaries where:

?s1 has any Attribute with Value "Daucus carota" Drop.

View query as SPARQL

Facet permalink

<table><caption>FIGURE 8
The results of the text query "Daucus carota" in TAXREF-LD.</caption><thead><tr><th>Entity</th><th>Title</th></tr></thead><tbody><tr><td>taxref:taxon/94503</td><td>Daucus carota</td></tr><tr><td>nodeID://b62882985</td><td>IUCN Red List status LC</td></tr><tr><td>nodeID://b62276608</td><td>Biogeographical status P (Présent (indigène ou indéterminé)) in France métropolitaine</td></tr><tr><td>nodeID://b62882986</td><td>IUCN Red List status LC</td></tr><tr><td>nodeID://b62276609</td><td>IUCN Red List status LC</td></tr><tr><td>nodeID://b62882987</td><td>IUCN Red List status LC</td></tr><tr><td>nodeID://b62276610</td><td>IUCN Red List status LC</td></tr><tr><td>nodeID://b62882988</td><td>Biogeographical status P (Présent (indigène ou indéterminé)) in France métropolitaine</td></tr><tr><td>nodeID://b62276611</td><td>IUCN Red List status LC</td></tr><tr><td>nodeID://b62882989</td><td>IUCN Red List status DD</td></tr><tr><td>nodeID://b62276612</td><td>IUCN Red List status LC</td></tr><tr><td>nodeID://b58212134</td><td>Biogeographical status P (Présent (indigène ou indéterminé)) in France métropolitaine</td></tr><tr><td>nodeID://b62276613</td><td>Biogeographical status I (Introduit) in Réunion</td></tr><tr><td>nodeID://b58791801</td><td>IUCN Red List status LC</td></tr><tr><td>nodeID://b58212132</td><td>IUCN Red List status LC</td></tr><tr><td>nodeID://b58791799</td><td>IUCN Red List status LC</td></tr></tbody></table>

plants is not regulated in any way, and a cultivated plant name is not even required to refer to a scientific taxon.

Furthermore, due to their complexity, nomenclature codes are not always strictly observed. For instance, the *Official Catalog of Species and Varieties of Cultivated Crops in France* gives the authority without the date (e.g., “L.” instead of “L. 1758”). Lists of cultivated plants are often built by aggregating several primary sources. However, these resources seldom cite primary sources, hindering the assessment of the confidence of their content.

Finally, vernacular names used to denote cultivated plants are sometimes specific to a given region, making alignment locale-dependent.

There are more technical difficulties related to modeling choices for representing taxa, names, and cultivated plants. For example, TAXREF-LD (Michel et al., 2017) strictly separates taxonomy and nomenclature. Other resources do distinguish taxonomy and nomenclature, representing both taxa and their names at the same level. Some classifications represent only scientific names, such as the Catalog of Life (Hobern et al., 2021). The lists of cultivated

plants often retain only a scientific name instead of a taxon, a name which may no longer be valid.

These design and modeling variations raise recurring questions on the type of objects to be aligned: do we align two taxa, or a taxon and a name, or a cultivated plant and a taxon, etc.?

Facing these challenges, we have proposed to define new mapping properties to represent the link between a crop usage, a taxon, and its scientific name.

## 3.2. SSSOM model analysis

SSSOM is a Simple Standard for Sharing Ontological Mappings which provides means to represent rich metadata for mappings and mapping sets. This standard representation has several advantages as follows:

* Entities of different nature can be mapped, for example, an instance of *owl:Class* with an instance of *skos:Concept*.
<figure><img src="image_59.png" /></figure>

* SSSOM does not impose the use of specific mapping properties such as *owl:equivalentClass*, the *oboInOwl hasDbXref*, or the well-known SKOS mapping properties, e.g., *skos:exactMatch*. In the context of this study, we were, thus, able to define the mapping properties relevant to our application (see Section 2.1.3).

* A set of mappings can become a dataset to be stored and shared independently of the aligned resources. Provenance metadata at dataset level allow to document the context in which the mappings are valid, for example, for the needs of an application. It is also possible to publish successive versions of a mapping set in a transparent way.

* Mappings can be described as first class objects and be referenced individually with an identifier (URI). This allows to declare equivalences between mappings coming from various sources to build aggregated mappings.

* Authors and reviewers of the mappings can be credited for their study as SSSOM recommends the use of ORCIDs with the author_id and reviewer_id properties.

The SSSOM project provides a rich documentation with many examples which facilitates the study. In addition, the SSSOM community is very active in working on improving and extending the model based on feedback from use cases issued from varied communities. This study of publishing mappings led us to
<figure><img src="image_60.png" /><figcaption>FIGURE 10
Second page of the description of taxon "Daucus carota" in TAXREF-LD.</figcaption></figure>

contribute to the discussions taking place on the SSSOM github repository.<fnref n="14" /> We expressed our need to document the rules that led us to assess a given mapping and discussed with the SSSOM authors on how to represent and expose these rules. As this new feature was plebiscited by several users, two new properties *sssom:curation_rule* and *sssom:curation_rule_text* were recently added to the SSSOM model. The rule can be represented either directly in the mapping set (using *sssom:curation_rule_text*) or be published as a RDF resource and be referenced with its URI in the mapping set (using *sssom:curation_rule*). As we work in TSV, we decided to give a short version of each curation rule in the TSV file of the mapping set, to refer to this article for a much finer description of our curation rules 2.2.3.

SSSOM, however, still shows some limitations or elements that are not fully mature as follows:

* Complex mappings: In our case, a crop usage may be fulfilled by a combination of taxa; for instance in viticulture, a vine plant can be composed of a rootstock and a graft. The rootstock is the buried part of the vine and serves as a support for the graft. Here, we may want to align *crop A* with a combination of a *taxon B* (rootstock) and a *taxon C* (graft) and indicate their respective roles. The future extension of SSSOM to handle complex

mappings may include a property to assert the role of each component of a combination. If so, our RDF vocabulary would be extended to provide the types of roles in our specific context.

* Negative mappings: the open world assumption holds in Semantic Web models. This means that the absence of mapping does not mean that this does not exist or is incorrect. Moreover, we want to declare that some mappings are false or irrelevant. The SSSOM community investigated two solutions: adding a modifier column or creating negative mapping properties. As we developed our own mapping vocabulary, we decided to create some specific mapping properties to indicate that a mapping should not exist between FCU and TAXREF-LD: *ontofcu:hasInvalidTaxon* / *ontofcu:hasInvalidUsage* / *ontofcu:hasInvalidScientificName* / *hasInvalidVernacularName*.

* Confidence is defined in SSSOM as "A score between 0 and 1 to denote the confidence or probability that the match is correct, where 1 denotes total confidence." We are wandering if this property is relevant in the case of manual alignment. Concretely, the experts working on this mapping set faced two difficulties in filling out this field. First, it was difficult to fix a confidence value when they were not completely sure about a mapping, or even worse, when they disagreed. For this reason, and also because the SSSOM property lacks some clarity and is actually under discussion, we agreed

<footnote n="14">14 https://github.com/mapping-commons/sssom/issues</footnote>
<figure><img src="image_61.png" /><figcaption>FIGURE 11</figcaption></figure>

Description of scientific name "*Daucus carota* L., 1753" in TAXREF-LD given as a whole by *rdfs:label* and split into its binomial name "*Daucus carota*" (*skos:prefLabel*) and the authority "L., 1753" (*txrfp:hasAuthority* whose label has scientific name authority).

on publishing only mappings with a confidence value equal to one.

* Cardinality: as recommended in the guidelines, this property should be automatically filled in. It took a long time to compute the values and would be difficult to maintain when more mappings are added to the mapping set.

Overall, SSSOM is a rising metadata standard for sharing, analyzing, and integrating mappings. It covers our needs pretty well. The SSSOM project also offers a forum to discuss solutions with experts and practitioners from various domains.

## 4. Conclusion and perspectives

This article describes our work on the alignment of two complementary knowledge graphs useful in agriculture: the crop usage defined in the thesaurus of cultivated plants in France named French Crop Usage (FCU) and the taxa and associated scientific names defined in the French national taxonomic repository TAXREF for fauna, flora, and fungi. Due to the fact that automatic alignment methods provide poor results, a group of agricultural experts has produced a set of valid mappings between crop usages, taxa, and associated scientific names. To do so, a new RDF
<figure><img src="image_62.png" /><figcaption>FIGURE 12
The description of the carrot cultivar "Blanche de Küttingen" in the GEVES Catalog.</figcaption></figure>

<figure><img src="image_63.png" /><figcaption>FIGURE 13
The description of the carrot in the C3PO KB.</figcaption></figure>

vocabulary of mapping properties was defined to align those plant descriptions. The metadata for the mappings and the mapping set are encoded with the Simple Standard for Sharing Ontological Mappings (SSSOM), a new model which offers means to report on the mapping provenance. To help the mapping creation, we provided some guidelines and tools to the experts. The produced mappings are available for download in Recherche Data Gouv, the federated national platform for research data in France.

Those mappings can be viewed as a first effort to test SSSOM Model using the TSV format. The mappings are manual and simple ones with high confidence value. Thus, they represent valid and consolidate mappings. We would like to enrich this mapping set by taking into account the whole Catalog of GEVES and C3PO KB. Both are evaluated as good source of information by our experts. We also plan to exploit these valid mappings to evaluate automatic alignment methods. The difficulty will be to manage the evolution of FCU and TAXREF-LD and keep up to date the mappings between those graphs. We would like to detect automatically

obsolete mappings. This study is a first step to test the description of mappings produced by experts. We hope that it can help other use cases of mapping between complementary description defining different points of view on the same objects of study. We also hope that the provision of curated mappings will allow testing of new automatic methods capable of working with scientific names and vernacular names.

## Data availability statement

The datasets presented in this study can be found in online repositories. TAXREF-LD: The version 15.2 of TAXREF-LD graph used for this study can be found in the AgroPortal repository https://agroportal.lirmm.fr/ontologies/TAXREF-LD. The github repository is https://github.com/frmichel/taxref-ld. The SPARQL EndPoint is https://taxref.mnhn.fr/sparql. FCU: The version 3.3 of the FCU thesaurus used for this study can
be found in the AgroPortal repository: https://agroportal.lirmm.fr/ontologies/CROPUSAGE. The gitlab repository is https://gitlab.irstea.fr/copain/frenchcropusage. The SPARQL EndPoint is http://ontology.inrae.fr/frenchcropusage/sparql. SSSOM: The github repository is https://github.com/mapping-commons/sssom C3PO KB: The version 1.0 of the C3PO KB can be found in the gitlab repository https://gitlab.com/serre-des-savoirs/c3po-kb. The associated ontology can be found on the AgroPortal repository https://agroportal.lirmm.fr/ontologies/C3PO/?p=sum mary mapping set FCU TAXREF-LD. The mapping set between FCU and TAXREF-LD generated for this study can be found in the Research Data Gouv repository https://doi.org/10.57745/LVRFW] the CSV file version presented in the article is available in article/Supplementary material.

## Author contributions

SA and SB: SSSOM documentation. CF and FM: conceptualization and design of TAXREF-LD knowledge graph and associated research tool. CR and SB: conceptualization and design of FCU knowledge graph and associated research tool. FA and JR: conceptualization and design of C3PO knowledge graph. CR, FA, and JR: guidelines definition and test. CR and SA: writing—original draft preparation. CR, SA, CF, FM, and RB: writing—review and editing. CR: supervision. All authors contributed to the article and approved the submitted version.

## Funding

This study was achieved with support from 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). This study was also carried out by the French government as part of the Recovery Plan and the Future Investment Program “i-Nov”.

## Conflict of interest

FA and JR were employed by Elzeard.

The remaining 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.

## Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frai.2023.1188036/full#supplementary-material

SUPPLEMENTARY DATA SHEET 1

SSSOM files that contains 63 mappings between vegetables or grapevine crop usage and their biological taxa.

## References

Darnala, B., Amardeilh, F., Roussey, C., and Jonquet, C. (2021). "Crop Planning and Production Process Ontology (C3PO), a new model to assist diversified crop production," in *IFOW 2021 - Integrated Food Ontology Workshop* @ 12th International Conference on *Biomedical Ontologies (ICBO)* (Bolzano, Italy).

Darnala, B., Amardeilh, F., Roussey, C., Todorov, K., and Jonquet, C. (2022). "Ontological representation of cultivated plants: linking botanical and agricultural usages," in *MK 2022 - 1st Workshop on Modular Knowledge @ ESWC 2022 of CEUR Workshop Proceedings*, eds L. Bozzato, V. A. Carrier, T. Hahmann, and A. Zimmermann (Hersonissos, Greece), 165-173.

Gargominy, O., Tercerie, S., Régnier, C., Ramage, T., Dupont, P., Daszkiewicz, P., et al. (2021). *TAXREF v15, référentiel taxonomique pour la France: méthodologie, mise en uvre et diffusion*. Technical report.

Hobern, D., Barik, S.K., Christidis, L., Garnett, S. T., Kirk, P., Orrell, T. M. et al. (2021). Towards a global list of accepted species VI: The Catalogue of Life checklist. *Org. Divers. Evol.* 21, 677–690. doi: 10.1007/s13127-021-00516-w

Matentzoglu, N., Balhoff, J. P., Bello, S. M., Bizon, C., Brush, M., Callahan, T. J., et al. (2022). A simple standard for sharing ontological

mappings (SSSOM). *Database* 2022, *baac035*. doi: 10.1093/database/baa c035

Michel, F., Amardeilh, F., Bossy, R., Faron, C., Roussey, C., and Noûs, C. (2022). "Alignement entre sources: cas d'usage des plantes cultivées," in *Journées francophones d'Ingénierie des Connaissances* (Saint-Étienne, France).

Michel, F., Gargominy, O., Tercerie, S., and Faron-Zucker, C. (2017). "A model to represent nomenclatural and taxonomic information as linked data. Application to the french taxonomic register, TAXREF," in *Proceedings of the ISWC2017 workshop on Semantics for Biodiversity (S4BioDiv)* (Vienna, Austria: CEUR Workshop Proceedings). Available online at: http://ceur-ws.org/Vol-1933/paper-3.pdf (accessed September 01, 2023).

Miles, A., and Bechhofer, S. (2009). *SKOS Simple Knowledge Organization System Reference. W3C Recommendation*. World Wide Web Consortium, United States.

Zhang, N., Rossman, A., Seifert, K., Bennett, J., Cai, Y., Hillman, B., et al. (2013). *Impacts of the international code of nomenclature for algae, fungi, and plants (melbourne code) on the scientific names of plant pathogenic fungi*. APS Journal, Available online at: https://www.apsnet.org/edcenter/apsnetfeatures/Pages/Melbourne.aspx (accessed September 01, 2023).
<figure><img src="image_64.png" /></figure>

Check for updates

## OPEN ACCESS

EDITED BY
Qichun Yang,
The University of Melbourne, Australia

REVIEWED BY
Shailesh Tripathi,
Tampere University of Technology, Finland
Zhen Liu,
The Hong Kong University of Science and
Technology, China

*CORRESPONDENCE

Arjun Pakrashi

<figure><img src="image_65.png" /></figure>

arjun.pakrashi@ucd.ie

RECEIVED 20 April 2023

ACCEPTED 08 September 2023

PUBLISHED 04 October 2023

## CITATION

Pakrashi A, Wallace D, Mac Namee B, Greene D and Guéret C (2023) *CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring*. *Front. Artif. Intell.* 6:1209507.
doi: 10.3389/frai.2023.1209507

## COPYRIGHT

© 2023 Pakrashi, Wallace, Mac Namee, Greene and Guéret. 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.

# CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring

Arjun Pakrashi¹,²,³*, Duncan Wallace¹,²,³, Brian Mac Namee¹,²,³, Derek Greene¹,²,³ and Christophe Guéret⁴

¹School of Computer Science, University College Dublin, Dublin, Ireland, ²Insight Centre for Data Analytics, Dublin, Ireland, ³VistaMilk SFI Research Centre, Teagasc Moorepark, Fermoy, Ireland, ⁴Accenture Labs, Dublin, Ireland

Dairy is an economically significant industry that caters to the huge demand for food products in people's lives. To remain profitable, farmers need to manage their farms and the health of the dairy cows in their herds. There are, however, many risks to cow health that can lead to significant challenges to dairy farm management and have the potential to lead to significant losses. Such risks include cow udder infections (i.e., mastitis) and cow lameness. As automation and data recording become more common in the agricultural sector, dairy farms are generating increasing amounts of data. Recently, these data are being used to generate insights into farm and cow health, where the objective is to help farmers manage the health and welfare of dairy cows and reduce losses from cow health issues. Despite the level of data generation on dairy farms, this information is often difficult to access due to a lack of a single, central organization to collect data from individual farms. The prospect of such an organization, however, raises questions about data ownership, with some farmers reluctant to share their farm data for privacy reasons. In this study, we describe a new *data mesh* architecture designed for the dairy industry that focuses on facilitating access to data from farms in a decentralized fashion. This has the benefit of keeping the ownership of data with dairy farmers while bringing data together by providing a common and uniform set of protocols. Furthermore, this architecture will allow secure access to the data by research groups and product development groups, who can plug in new projects and applications built across the data. No similar framework currently exists in the dairy industry, and such a data mesh can help industry stakeholders by bringing the dairy farms of a country together in a decentralized fashion. This not only helps farmers, dairy researchers, and product builders but also facilitates an overview of all dairy farms which can help governments to decide on regulations to improve the dairy industry at a national level.

## KEYWORDS

data mesh, data fabric, digital agriculture, dairy industry, decentralized data

## 1. Introduction

The dairy industry is experiencing strong global growth (Douphrate et al., 2013; Bhat et al., 2022) accompanied by significant transformations through the increasing adoption of digital technologies (Borchers and Bewley, 2015; Gargiulo et al., 2018; Hansen et al., 2019; Gabriel and Gandorfer, 2023). As the industry enables this growth by producing dairy products more efficiently, it becomes essential to focus on cow
health, as well as long-term factors, such as environmental impacts and profitability (Barkema et al., 2015; Bhat et al., 2022). The adoption of digital technologies on dairy farms (Gabriel and Gandorfer, 2023) means that considerably more data are being generated from dairy farms. These data are used to help farmers monitor their farms, make decisions, and achieve their goals around production, profitability, and cattle welfare and also to help administrative bodies to set national and international policies.

Additional availability of farm data also allows advanced statistical and machine learning techniques to be applied to support farm decision-making. Cow health has received significant attention in this regard. Early prediction of ailments in cows can reduce financial losses and improve cattle welfare. Recent adoption of technology in farm and data availability have triggered several advanced predictive analytic studies focused on dairy farms. For example, mastitis (an udder infection that afflicts cows) is one of the top reasons for monetary loss in dairy farms (Yalcin et al., 1999; Petrovski et al., 2006; Viguier et al., 2009), and several data-driven approaches to detecting mastitis are described in the literature (Ebrahimi et al., 2019; Bobbo et al., 2021; Ryan et al., 2021). There are also a number of studies that use data-driven approaches to detect lameness (Shahinfar et al., 2021; Altay and Albayrak Delialioğlu, 2022; Zhou et al., 2022) and ketosis (a metabolic disease in cows; Bauer and Jagusiak, 2022; Wang et al., 2023) using machine learning. As well as cow health, there are other aspects of dairy farming where data-centric advanced systems are being employed, including predicting herbage yield and composition (O'Hara et al., 2021; Albert et al., 2022), and estimating greenhouse gas emission (Chianese et al., 2009; Martin et al., 2017; Kadam and Vijayumar, 2018).

With the digitization of the dairy industry, research and product development are becoming more interdisciplinary. Sophisticated machine learning and statistical systems exploiting the data collected on farms are being developed by research groups in universities and organizations involving farmers, geneticists, computer scientists, and statisticians. A general trend is that several research groups perform research independently, with limited data sharing. This is often due to limited interoperability between data sources, data sharing challenges, and a lack of trust among stakeholders. Nonetheless, sharing the data generated on farms, as well as integrating different products (e.g., analysis, results, and services), has the potential to bring significant economic value to the agriculture industry (Wysel et al., 2021).

Wolfert et al. (2017) identified several major challenges in digital agriculture, which are also present in dairy farming: data ownership, data quality, sustainable integration of data sources, intelligence processing and analytics, business models, and openness of platforms. The World Economic Forum (WEF, 2023) is a non-profit organization that brings together global leaders to address critical issues and promote public-private cooperation, serving as a platform for networking, dialog, and shaping agendas for positive changes. The WEF summarizes the challenges in digital agriculture in three categories: fragmentation, standards, and access. A recent study by Fadul-Pacheco et al. (2022) describes data-oriented issues and demands of dairy farms. It was found that dairy farmers and non-dairy farmers (related to the dairy industry) believed that data sharing is important. However, issues

with data ownership and data quality represented a significant area of concern. A significant portion was unsure about the chain of custody of data. Non-farmers (e.g., researchers and organizations without dairy farming expertise) were concerned about the lack of integration of data and, in some cases, not aware of the usefulness of data integration. The issue of trust when sharing data was raised in the study by Jakku et al. (2019), indicating that transparency, trust, and data ownership are major issues in sharing data. This highlights the need for a framework that ensures good data quality, effective data integration, transparent data use and ownership, and effective use of the data to build analytics and predictive systems. This, in turn, demonstrates that there is a requirement for a reliable data sharing framework in the dairy industry context which addresses the above issues.

In this study, we introduce a data mesh architecture, CowMesh, that addresses the challenges in data-driven dairy farming as described above. Specifically, we propose an architecture with a central semantic data product that provides interoperability among other data products and data domains by providing a high-level ontology of dairy farm components and a uniform data access protocol.

The rest of the study is structured as follows. Section 2 discusses related work. The proposed CowMesh architecture is described in detail in Section 3. A series of use cases from the Irish dairy farming context are then presented in Section 4, to show the typical usage of the CowMesh architecture. Key advantages and opportunities for the architecture are discussed in Section 5, and finally, Section 6 concludes the study.

## 2. Related work

According to WEF (2023), the key challenges around data in the agricultural domain can be summarized as follows:

* **Fragmentation:** Data are gathered from a variety of sources (sensors, satellites, etc.) and made available as different topical silos (soil data, seed data, etc.);

* **Standards:** There is no global standard or standardization body facilitating the expression of agricultural data;

* **Access:** Data need to be exchanged and connected in order to deliver value.

However, these challenges are actually not restricted to agriculture and are a larger concern across many different industry sectors. The proposal for making scientific data Findable, Accessible, Interoperable, and Reusable (FAIR; Wilkinson et al., 2016) is a pragmatic approach toward producing data in a better way. It has been designed with the scientific community in mind, but the principles are more globally applicable. FAIR pushes forward a number of core principles which align largely with the W3C Data on the Web Best Practices (DWBP)<fnref n="1" /> but do not push forward any particular technology stack.

According to both FAIR and DWBP, vocabularies are a pillar of data publication. In the agricultural domain, the thesaurus

<footnote n="1">1 https://www.w3.org/TR/dwbp/ (visited August 17, 2023).</footnote>
AGROVOC<fnref n="2" /> from the Food and Agriculture Organization is an important resource that is made available as a SKOS-based ontology (Caracciolo et al., 2013). This vocabulary is essential for describing agricultural concepts uniformly and in multiple languages. However, AGROVOC does not solve any of the access or fragmentation issues itself.

The Knowledge Graph “Agronomy Linked Data (AgroLD)” from the study by Larmande and Todorov (2021) (D2KB) is a good example of an integrated dataset. The content is FAIR-compliant and incorporates data coming from 15 different silos into a single, integrated dataset. The resulting dataset can be used to answer complex questions around plants and biology. The portal AgroLD<fnref n="3" /> is the main entry point to explore the Knowledge Graph. In terms of an agriculture-related portal, and with a slightly different focus, LandPortal<fnref n="4" /> serves integrated data about land use worldwide. The objective is to support queries around land ownership and arable land utilization.

We remark that, although vocabularies such as AGROVOC can support the creation of data portals aimed at particular needs, there is still a requirement for a more holistic approach. As outlined in the study by WEF (2023), there are a number of services needed around these portals in order to unlock their capabilities. It could also be interesting to consider creating a more flexible alternative to data portals constructed on a data aggregation approach. To achieve this aim, we propose the adoption of the recent and growing approach of the data to the agricultural domain (Joshi et al., 2021; Butte and Butte, 2022; Hooshmand et al., 2022; Bode et al., 2023; Dolhopolov et al., 2023; Goedegebuure et al., 2023; Pongpech, 2023).

# 3. The CowMesh architecture

In this section, we will introduce the CowMesh architecture by first providing a general overview of the Data Mesh approach and then explaining how this is adopted in our context. Finally, we describe the central Semantic Layer component, which is a Data Fabric, and its key role in CowMesh.

## 3.1. The data mesh

The data mesh concept was introduced by Dehghani (2022a,b) to define a set of principles for publishing data (Christ et al., 2022). From a technology point of view, a data mesh can be implemented using a variety of solutions and standards. The only particularity is the focus of application programming interface (API) to replace the (manual) handling of data dumps across systems. The novelty of the data mesh approach is not the data integration itself but rather how it is approached and considered from an organizational point of view. In particular, a data mesh is centered around four core concepts as follows:

* The preservation of **domain ownership** for the different domains may serve. As teams working together in a company system, the different data domains are expected to directly collaborate with each other and own their work—for example, in terms of data exchange or analytical work usage.

* The notion of **data products** replaces the older idea of data assets. The application of a product logic to data assets turns them into things that need to match a demand, whose value is assessed and production cost studied. Data domains may share datasets as product and/or analytical applications leveraging one or more other data products as a product of their own.

* A **data infrastructure platform** is put in place to let each data domain easily make data products available to the rest of an organization. This platform must not be limited to a particular domain and should facilitate both the creation and consumption of data products.

* An overall **federated governance** approach is applied to establish data standards and best practices to use the data mesh. This ensures the technical compatibility of all data products and can ensure compliance to rules and regulations.

It is interesting to note that these concepts describe one very well-known data publication platform, the World Wide Web. The Web features a strong notion of domain ownership. Each website publisher is responsible for its own websites and research communities publishing the outcomes of analytics on the Web, or the data within it, own those publications. Websites are by default treated as products and are routinely checked for view performances as well as optimized toward increasing those views. The web infrastructure platform based on a set of accessible software and programmatic tools make it possible for anyone to publish a new product on the Web. The W3C defines the standards and best practices that make the web run smoothly (HTTP, CSS, etc.). Finally, the Web often uses different attribution mechanisms such as Creative Common licensing<fnref n="5" /> and document object identifiers<fnref n="6" /> for attribution of content. Any new technical platform or data domain willing to join the mesh can easily do so as long as the compatibility with those standards is ensured.

## 3.2. Architecture

To tackle the previously discussed challenges, we propose an approach based on two emerging design patterns: a data fabric and a data mesh. Whereas, these two approaches can be described as opposing each other, especially in terms of data centralization and human versus process focus, we propose to combine the two patterns so that they complement each other. Our architecture, presented in Figure 1, is composed of:

* **Data Domains** such as research institutions, public institutions, and private actors. In the Irish context, these might be the Irish Cattle Breeding Federation (ICBF),<fnref n="7" />

<footnote n="2">2 https://agrovoc.fao.org/browse/agrovoc/en/ (visited August 17, 2023).</footnote>

<footnote n="3">3 http://agrold.org (visited August 17, 2023).</footnote>

<footnote n="4">4 https://landportal.org/ (visited August 17, 2023).</footnote>

<footnote n="5">5 https://creativecommons.org/licenses/ (visited August 17, 2023).</footnote>

<footnote n="6">6 https://www.doi.org/ (visited August 17, 2023).</footnote>

<footnote n="7">7 https://www.icbf.com/ (visited August 17, 2023).</footnote>
<figure><img src="image_66.png" /><figcaption>FIGURE 1
The CowMesh architecture shows the semantic layer (as a data product), several data domains, and a governance layer. A particularity of this architecture is the data product “Semantic Layer” which is a data fabric acting as a data infrastructure and interoperability layer for other data products.</figcaption></figure>

Ornua,<fnref n="8" /> or Teagasc.<fnref n="9" /> Each of these stakeholders will have some datasets and tools that they contribute as data products (not depicted in Figure 1).

* **Data Products** are contributed by data domains. Examples in the Irish context include PastureBase<fnref n="10" /> and the ICBF databases.<fnref n="11" />

* **A Semantic Layer** implemented as a data fabric is a core element of the proposed architecture and the only data product presented in Figure 1. The role of the semantic layer is to provide an integrated view over key data coming from different data domains. This does not prevent consumers of this data product from going back to the data domain sources in it—as, for example, illustrated in the link between milk processors and governmental research institutions (meaning, for instance, specific data access negotiations)—but does make it easier to consume the data.

* The **Governance** layer decides on the standards being used for the mesh overall, and the ontology driving the semantic layer.

It can be observed that the semantic layer is a data product lacking a defined data domain owning it. This is because we consider this as an open question left to specific implementations. In some cases, a research consortium might assume this role, while,

in other cases, this would be one of the industrial stakeholders. For the specific Irish context, the research program VistaMilk<fnref n="12" /> would assume this role.

In our architecture, the semantic layer is a data fabric, created as a data product and a central part of CowMesh. Since a key objective of a data mesh is to promote decentralization, having a central semantic layer might appear to be contradictory. However, its inclusion enhances the effectiveness of the data mesh by keeping the data decentralized while connecting them at a uniform semantic and conceptual level, thus establishing interoperability. Additionally, the semantic layer sets standards and provides governance to address data privacy, ownership, and access issues.

## 3.3. Semantic layer: a data fabric

The semantic layer data product is a data fabric in our architecture, which enables interoperability among different data domains and enables governance. Although this sits at the core of the architecture, and a data fabric is a centralized approach to manage data, this does not hinder the decentralized properties of the data mesh. Both data mesh and data fabric are architectural patterns to manage data in a distributed and complex environment. The main contrasting properties of the data mesh and data fabric are as follows:

<footnote n="8">8 https://www.ornua.com/ (visited August 17, 2023).</footnote>

<footnote n="9">9 https://www.teagasc.ie/ (visited August 17, 2023).</footnote>

<footnote n="10">10 https://pasturebase.teagasc.ie (visited August 17, 2023).</footnote>

<footnote n="11">11 https://www.icbf.com/the-icbf-database/ (visited August 17, 2023).</footnote>

<footnote n="12">12 https://www.vistamilk.ie/ (visited August 17, 2023).</footnote>
* **Scope:** A data fabric is typically designed to manage data across an entire organization, while a data mesh is more focused on specific domains, groups, or business units within an organization.

* **Approach:** A data fabric is more centralized in its approach, with a single unified architecture that connects and integrates data from different sources. On the other hand, a data mesh is more decentralized in its approach, with individual domains or teams responsible for managing their own data.

* **Governance:** A data fabric provides a centralized governance framework for managing data, while a data mesh relies on a decentralized governance model, where each domain or team is responsible for defining and enforcing its own governance policies.

* **Culture:** A data mesh is more focused on promoting a culture of data ownership and collaboration among teams, while a data fabric is more focused on standardization and consistency in the data management process.

Therefore, a data fabric is a centralized approach to managing data across an entire organization, while a data mesh is a more decentralized approach that focuses on promoting data ownership and collaboration within specific domains or business units. We use these contrasting properties to compliment each other to address the previously mentioned issues in dairy farms. The data fabric, used as a data product, establishes a uniform data model, access protocol, and governance model. On the other hand, the data mesh enables decentralized development of data products.

The role of the semantic layer in the CowMesh architecture involves:

* Providing an ontology of the concepts in the farm data that provides a uniform data model.

* Defining a set of protocols (through APIs) for accessing farm data through the concepts in the ontology.

* Integrating heterogeneous data domains in the CowMesh architecture to make them interoperable.

In summary, the semantic layer enables decentralized development in the data mesh while also providing interoperability, integration, and governance.

### 3.3.1. Ontology

Like any other domain, the data in dairy farming have a set of concepts related to them. For example, all data related to cows in a single herd or a collection of dairy farms can be observed as a concept “cow.” The concept “cow,” with respect to the data, is an abstract view of a cow which is described through its properties. A cow can be described by its unique identifier, date of birth, body weight, and various other attributes. Several concepts like this—such as farm, milk, and paddock—can also be defined. Different concepts will be interconnected to describe the abstract data representation of a farm. We propose an ontology for a dairy farm, which is shown in Figure 2.

An ontology such as the one shown in Figure 2 shows an example of how dairy industry data can be

structured. The different concepts in our ontology are as follows:

* **Cow:** Describes the cow, date of birth, etc.

* **Herd:** Describes properties of a herd related with Cow.

* **Soil:** Properties of the farm's soil.

* **Grass:** Properties of the farm's grass.

* **Paddock:** Information about a farm's paddocks related to soil and grass.

* **Milk Record:** milk composition including fat, protein, lactose, urea, and somatic cell count.

* **Daily Milk Record:** Properties for daily milking, milk yield, milk flow etc.

* **Milk:** Describes overall milk.

* **Farm:** Information about the farm, e.g., location, area, nos. of herd, and nos. of cows per herd.

* **Disease Occurrence:** Record of occurrences of disease in cows such as clinical mastitis, sub-clinical mastitis, lameness, and respiratory disease.

* **Treatment:** Treatment performed for any disease.

* **Farmer:** Information about the farmer.

Such an ontology sits centrally in the semantic layer, abstracting the dairy farm data to the users. Some components of the ontology might describe sensitive or personal information, such as exact farm location or farmer information. The exact data to be shared and how it is shared will be controlled by the source and governed by the semantic layer according to the agreed privacy policy. Therefore, such data instances should be ideally anonymized, or omitted based on the policy, so as to preserve data privacy (e.g., the anonymization of data in the MilkMap system described later in Section 4.2). It is important to note that the semantic layer only enables interoperability by providing an abstract model of the data (ontology) and a protocol (though an API) for data access.

### 3.3.2. Access and access protocol

One of the main services of the semantic layer as a data product is to provide an access protocol for the farm data with respect to the provided ontology. All the data products in the CowMesh should be able to use this protocol to access data. This process can be implemented through an API, which refers to a set of protocols and tools that allows different software applications to communicate and interact with each other, thus enabling data and functionality sharing across systems. An API defines the methods and data structures that developers can use to integrate services or features into their own applications. This allows consumers to retrieve farm data in the CowMesh uniformly, even though the different data domains may have differently structured data and access protocols. The API is implemented in the semantic layer and is a data product, although the API can also reside in the source data domain, if required. For example, if a data domain is public, the data can be mapped by the semantic layer, and the API implementation can reside in the semantic layer. On the other hand, if the data domain is privately owned, the API implementation can reside as a data product with the private owner, but the access protocol and the compliance with the ontology are ensured. The particular implementation depends on the specific case. The semantic layer