# Explicit versus tacit knowledge in duquenne-guigues basis of implications: Preliminary results

Johanna Saoud, Alain Gutierrez, Marianne Huchard, Pascal Marnotte, Pierre

Silvie, Pierre Martin

► To cite this version:

Johanna Saoud, Alain Gutierrez, Marianne Huchard, Pascal Marnotte, Pierre Silvie, et al. Explicit versus tacit knowledge in duquenne-guigues basis of implications: Preliminary results. Worshop Analyzing Real Data with Formal Concept Analysis (RealDataFCA’2021), Jun 2021, Strasbourg, France. pp.20-27. [hal-05177370](https://hal.science/hal-05177370v1)

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<figure><img src="image_1.png" /></figure>
Proceedings of the workshop on

Analyzing Real Data with Formal Concept Analysis
(RealDataFCA’2021)

June 29, 2021

<https://icfca2021.sciencesconf.org/page/realdatalfca2021>

in conjunction with the 16th International Conference on
Formal Concept Analysis (ICFCA 2021)

Edited by

Agnès Braud *

Xavier Dolques *

Rokia Missaoui **

* ICube, Université de Strasbourg - CNRS, France

** LARIM, Université du Québec en Outaouais, Canada
Workshop organizers

Agnès Braud, ICube, Université de Strasbourg - CNRS, France

Xavier Dolques, ICube, Université de Strasbourg - CNRS, France

Rokia Missaoui, LARIM, Université du Québec en Outaouais, Canada

Program Committee

Jaume Baixeries, Universitat Politècnica de Catalunya, Spain

Sadok Ben Yahia, Tallinn University of Technology, Estonia

Karell Bertet, Laboratory L3I, University of La Rochelle, France

Agnès Braud, ICube, Université de Strasbourg - CNRS, France

Peggy Cellier, IRISA/INSA Rennes, France

Pablo Cordero, Universidad de Málaga, Spain

Miguel Couceiro, LORIA (CNRS - Inria - Université de Lorraine), Nancy, France

Diana Cristea, Babes-Bolyai University, Romania

Christophe Demko, Université de La Rochelle, France

Xavier Dolques, ICube, Université de Strasbourg - CNRS, France

Peter Eklund, Deakin University, Australia

Manuel Enciso, Universidad de Málaga, Spain

Sébastien Ferré, Université de Rennes 1, CNRS, IRISA, France

Jessie Galasso, Geodes, DIRO, Université de Montréal, Canada

Marianne Huchard, LIRMM, Université de Montpellier et CNRS, France

Mohamed Hamza Ibrahim, Zagazig university, Egypt

Dmitry Ignatov, National Research University Higher School of Economics, Moscow, Russia

Mehdi Kaytoue, Infologic R&D, France

Léonard Kwuida, Bern University of Applied Sciences, Switzerland

Florence Le Ber, ICube, Université de Strasbourg/ENGEES - CNRS, France

Engelbert Mephu Nguifo, LIMOS, Blaise Pascal University - CNRS, France

Rokia Missaoui, LARIM, Université du Québec en Outaouais, Canada

Emilio Muñoz-Velasco, Universidad de Málaga, Spain

Amedeo Napoli, LORIA (CNRS - Inria - Université de Lorraine), Nancy, France

Sergei Obiedkov, National Research University Higher School of Economics, Moscow, Russia

Manuel Ojeda-Aciego, Universidad de Málaga, Spain

Uta Priss, Ostfalia University, Germany

Christian Sacarea, Babes-Bolyai University, Romania

Henry Soldano, LIPN, Université Paris 13, France

Francisco José Valverde-Albacete, Universidad Carlos III de Madrid, Spain
CONTENTS

Preface 4

How to provide light to COVID data by means of FCA 5

Domingo López-Rodríguez, Pablo Cordero, Manuel Enciso and Ángel Mora

Analyzing water monitoring data with RCA-based approaches 13

Xavier Dolques, Agnès Braud, Corinne Grac and Florence Le Ber

Explicit versus Tacit Knowledge in Duquenne-Guigues Basis of Implications: Preliminary Results 20

Johanna Saoud, Alain Gutierrez, Marianne Huchard, Pascal Marnotte, Pierre Silvie and Pierre Martin

Analyzing the composition of remedies in ancient pharmacopeias with FCA 28

Agnès Braud, Xavier Dolques, Pierre Fechter, Nicolas Lachiche, Florence Le Ber and Véronique Pitchon

Mining the Groceries Database using Triadic Concept Analysis 36

Pedro H. B. Ruas, Rokia Missaoui, Mark A. J. Song and Léonard Kwuida

Leveraging Formal Concept Analysis to Improve n-fold validation in Multilabel Classification 44

Francisco J. Valverde-Albacete and Carmen Peláez-Moreno
Explicit versus Tacit Knowledge in
Duquenne-Guigues Basis of Implications:
Preliminary Results

Johanna Saoud¹, Alain Gutierrez¹, Marianne Huchard¹, Pascal Marnotte²,
Pierre Silvie²,³, and Pierre Martin²

¹ LIRMM, Univ Montpellier, CNRS, Montpellier, France
johanna.saoud@etu.umontpellier.fr,
{marianne.huchard,alain.gutierrez}@lirmm.fr

² CIRAD, UPR AIDA, F-34398 Montpellier, France
AIDA, Univ Montpellier, CIRAD, Montpellier, France
{pascal.marnotte,pierre.silvie,pierre.martin}@cirad.fr

³ PHIM Plant Health Institute, Montpellier University,
IRD, CIRAD, INRAE, Institut Agro, Montpellier, France

**Abstract.** Formal Concept Analysis (FCA) comes with a range of relevant techniques for knowledge analysis, such as conceptual structures or implications. The Duquenne-Guigues basis of implications provides a cardinality minimal set of non-redundant implications. The concern of a domain expert is to discover new knowledge within this implication set. The objective of this prospective paper is to collect and discuss the different patterns of implications extracted from a dataset on plants used in medical care or consumed as food. We identify 16 patterns combining 3 types of knowledge elements (KE). The patterns highlight redundant KEs, or KEs of little interest, in particular, those corresponding to plant taxonomy, as it is familiar knowledge for the experts. Removing these KEs from the implications would make them tacit. We suggest a post-process for cleaning up the implications before reporting them to the experts. In addition, we discuss the different patterns and how an implication classification based on patterns could help the experts.

**Keywords:** Formal Concept Analysis · Duquenne-Guigues basis · Implication Rules · Life Sciences Knowledge Base · One Health

# 1 Introduction

Formal Concept Analysis (FCA) is a mathematical framework based on lattice theory which aims to formalize the notion of concept [6]. It gives foundations for a large range of methods for knowledge processing and knowledge discovery [12]. These methods include the construction of formal concepts and their ordering in a concept lattice or in restricted sub-structures of the lattice. For a domain expert (e.g. pathologist, entomologist), navigating through a complex lattice to extract knowledge may remain a challenge. An alternative view on knowledge is

RealDataFCA'2021: Analyzing Real Data with Formal Concept Analysis, June 29,
2021, Strasbourg, France

<figure><img src="image_2.png" /></figure>

Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
building the Duquenne-Guigues basis (DGB) of implications [8]. An interest of this implication basis is its formulation of pieces of knowledge using a compact and comprehensive formalism. DGB indeed provides a cardinality-minimal set of non-redundant implications.

Through DGB building, expert concern is to discover new knowledge within the implication set. Diverse situations can occur. For instance, if an implication is too obvious for the expert, e.g. because it exclusively describes a domain taxonomy, then it presents a too limited interest to be kept in the implication set. This implication therefore becomes a tacit knowledge, while the others remain explicit. In more complex situations, the implication may contain both obvious knowledge elements (KE) and useful KE. In such cases, the obvious part of the implication may become tacit, when the other part may remain explicit.

The objective of this paper is to observe and discuss different patterns of implications extracted from a dataset on plants used in medical care or consumed as food. With these patterns, we aim to identify obvious KE included in the implications, and how implications can be simplified. The patterns may also provide an opportunity to classify the implications into coherent sets. Section 2 introduces the background and the dataset. Section 3 presents and discusses the preliminary results. Section 4 concludes and draws future work.

## 2 Background and Dataset

Background FCA elaborates knowledge, including formal concepts or attribute implications, on top of a formal context (FC) $\mathcal{K} = (G, M, I)$ where $G$ is an object set, $M$ is an attribute set and $I \subseteq G \times M$. An implication, denoted by $A \implies B$, is an attribute set pair $(A, B)$, $A, B \subseteq M$ such that all objects owning the attributes of $A$ (premise) also own the ones of $B$ (conclusion): $\{g|\forall m_a \in A, (g, m_a) \in I\} \subseteq \{g|\forall m_b \in B, (g, m_b) \in I\}$. There are several types of implication bases [1]. Here we consider the Duquenne-Guigues basis (DGB) of implications [8], which is a cardinality minimal set of non-redundant implications, from which all implications can be produced. An implication is held (or supported) by a number of objects, that we call the implication scope (S). The support is the proportion of such supporting objects. Let $Imp = A \implies B$, $S(Imp) = |\{g|\forall m \in A, (g, m) \in I\}|$. $Support(Imp) = S(Imp)/|G|$. For this work, the DGB of implications is built on a FC using Cogui software platform$^4$, which includes a Java implementation of LinCbO

The dataset and the taxonomic knowledge To conduct the evaluation, we use an excerpt of the Noctuidae dataset [13], which is itself part of the Knomana dataset [14]. This dataset draws particular attention of experts in the context of One Health initiative [11] for addressing the worrying worldwide invasion of Spodoptera frugiperda (Lepidoptera from the Noctuidae family) which was first detected in Africa in 2016 [7] and is continuously spreading. At the end of 2018, S. frugiperda was first found in Yunnan Province in China [16]. Furthermore, in

<footnote n="4">⁴ http://www.lirmm.fr/cogui/</footnote>
2018, it was first recorded in South Asia, namely India [9]. In January 2020, it was trapped in Australia's special biosecurity zone in the Torres Strait islands of Saibai and Erub, and confirmed on 3 February 2020, and on mainland Australia in Bamaga on 18 February 2020 [15].

<table><caption>**Table 1.** On the top, excerpt of [13] that describes organisms uses, and, on the bottom, the associated formal context (FC) after the nominal scaling of *Species*, *Genus*, *Family*, *food*, and *medical*. *S*, *G*, and *F* are short notation for Species, Genus, and Family.</caption><thead><tr><th rowspan="2">FC</th><th colspan="2">S_Acorus</th><th colspan="2">S_Cychorium</th><th colspan="2">S_Achillea</th><th colspan="2">G_Acorus</th><th colspan="2">G_Cychorium</th><th colspan="2">G_Achillea</th><th colspan="2">F_Acoraceae</th><th colspan="2">F_Asteraceae</th><th colspan="2">food</th><th colspan="2">medical</th></tr><tr><th>Calamus</th><th>Intybus</th><th>Collina</th><th>Intybus</th><th>Collina</th><th>Intybus</th><th>Collina</th><th>Intybus</th><th>Collina</th><th>Intybus</th><th>Collina</th><th>Intybus</th><th>Collina</th><th>Intybus</th><th>Collina</th><th>Intybus</th><th>Collina</th><th>Intybus</th><th>Collina</th><th>Intybus</th></tr></thead><tbody><tr><td>p1</td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>p2</td><td></td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>p3</td><td></td><td></td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td><td></td><td></td><td></td><td></td><td>X</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr></tbody></table>

This dataset indicates for each plant organism, out of the 600 in the dataset, its species, its genus, its family, and whether it is consumed as food and used in medical care. There is one-to-one mapping between organisms (objects) and *Species* values (cf. Tab. 1). Species, genera, and families respect a taxonomy which is a 3-level tree structure with this general shape: Species ≺ Genus ≺ Family; E.g. Species Acorus calamus ≺ Genus Acorus ≺ Family Acoraceae (see Fig. 1). This taxonomy is a familiar knowledge for the experts. The dataset comprises 600 species from 376 genera and from 98 families.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Example of taxonomy for plants. Family, genus, and species names are respectively presented on the top, in the middle, and at the bottom of the figure. An arrow represents a generalization relation.</figcaption></figure>

*Formal context built from the dataset* A FC describes a set of objects using a set of Boolean attributes. When the dataset contains a multi-valued attribute, conceptual scaling can be used to obtain Boolean attributes [6]. Various conversion methods are adopted, among which the nominal scaling for categorical attributes, such as the species name (e.g. *Achillea collina* or *Acorus calamus*), where each value is converted into a Boolean attribute [10]. Applied to this
work, the conversion of the dataset as a FC consisted in the nominal scaling of the attributes *Species*, *Genus*, *Family*, *food*, and *medical*. The taxonomy KE is expressed, in the FC ($G, M, I$), by the fact that for a plant organism $p \in G$ and a given species $s$ from genus $g$ and family $f$, with $s, g$ and $f \in M$, if $(p, s) \in I$, then $(p, g) \in I$. Similarly, if $(p, g) \in I$ then $(p, f) \in I$. In addition, the dual of the *food* (i.e. *no-food*) and *medical* (i.e. *no-medical*) attributes are added in the FC to explicit respectively the fact to be not consumed (*no-food*) or not used in medical care (*no-medical*). Note that, in [13], the attribute *medical* is encoded by *Medical_X*, *no-medical* by *Medical_*, *food* by *Food_X*, and *no-food* by *Food_*.

# 3 Results and Discussion

As noticed in [4], the implications from the DGB are not redundant one with the others. But they may contain redundant attributes in the premise and in the conclusion, due to the fact that pseudo-intents are used instead of minimal generators. Besides, we can expect that implications respect a limited number of patterns, and that some of these patterns have different meanings. A long-term objective of this work is to provide the experts with a minimal set of implications filtered and classified to assist them in the analysis. In this section, we observe patterns in implications of DGB. Then, we discuss how implications may be post-processed and classified making them more appropriate to the domain expert.

## 3.1 The implications from DGB

Due to the scaling of the attributes *Species*, *Genus*, and *Family*, the FC associated to the dataset has 1078 Boolean attributes, i.e. 600 attributes to inform on the species, 376 on the genus, 98 on the family, and 4 on the medical and food use. For the 600 plant organisms, Table 2 shows that 1168 implications were extracted from this FC. Most of the implications, i.e 1007, are held by one object, and thus are specific to a plant species. Among the 161 remaining ones, 9 implications are supported by more than 9 objects. The maximum scope, i.e. 35, corresponds to the implication informing that none of the 35 species from the Meliaceae Family, present in the dataset, is consumed (cf. ID 1 in Table 3).

<table><caption>**Table 2.** Number of implications per scope.</caption><tr><td>Scope</td><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td><td>6</td><td>7</td><td>8</td><td>10</td><td>11</td><td>16</td><td>18</td><td>29</td><td>35</td></tr><tr><td>#Implications</td><td>1007</td><td>76</td><td>37</td><td>18</td><td>12</td><td>3</td><td>1</td><td>5</td><td>3</td><td>2</td><td>1</td><td>1</td><td>1</td><td>1</td></tr></table>

These 1168 implications are formulated using 16 patterns, where a pattern corresponds to the pair (Premise, Conclusion) in which each of the declarative sentence is designed using the SGFP schema (Table 3). This schema is the ordered list of presence of the attributes *Species* (S), *Genus* (G), *Family* (F), *food* or *no-food*, *medical* or *no-medical* (p), in the declarative sentence. By grouping *food*,
no-food, medical and no-medical, our intent is to focus our analysis on the types of KEs, and not the KEs themselves.

Various combinations of S, G, F, and p can be observed in the premise or the conclusion. For instance, pattern 1 informs on the use of all plants from a family. Pattern 2 provides the taxonomic relation of a genus with a family. Some patterns are more extended, such as pattern 5 that states on the genus of plants from a family with a given use.

<table><caption>**Table 3.** Implication patterns from DGB. The premise and the conclusion are designed using the SGFP schema. KU, KT, and KD are respectively short notations for Knowledge on plant Use, Knowledge on plant Taxonomy, and Knowledge on the Dataset.</caption><thead><tr><th>ID</th><th>Premise</th><th>Conclusion</th><th>knowledge elements</th><th>Example of implication</th><th>#implications</th><th>Max scope</th></tr></thead><tbody><tr><td>1</td><td>F</td><td>p</td><td>KU</td><td>F_Meliaceae ⇒ no-food</td><td>35</td><td>35</td></tr><tr><td>2</td><td>G</td><td>F</td><td>KT</td><td>G_Salvia ⇒ F_Lamiaceae</td><td>12</td><td>18</td></tr><tr><td>3</td><td>Fp</td><td>p</td><td>KU</td><td>no-food,F_Annonaceae ⇒ no-medical</td><td>10</td><td>16</td></tr><tr><td>4</td><td>G</td><td>Fp</td><td>KU, KT</td><td>G_Trichilia ⇒ no-food,F_Meliaceae</td><td>84</td><td>10</td></tr><tr><td>5</td><td>Fp</td><td>G</td><td>KU, KD</td><td>food,medical,F_Rutaceae ⇒ G_Citrus</td><td>6</td><td>5</td></tr><tr><td>6</td><td>F</td><td>G</td><td>KD</td><td>F_Piperaceae ⇒ G_Piper</td><td>1</td><td>5</td></tr><tr><td>7</td><td>GFp</td><td>p</td><td>KU, KT</td><td>medical,F_Asteraceae,G_Artemisia ⇒ no-food</td><td>7</td><td>4</td></tr><tr><td>8</td><td>Fp</td><td>Gp</td><td>KU, KD</td><td>medical,F_Annonaceae ⇒ food,G_Annona</td><td>1</td><td>3</td></tr><tr><td>9</td><td>F</td><td>Gp</td><td>KU, KD</td><td>F_Lythraceae ⇒ no-food,no-medical,G_Lythrum</td><td>5</td><td>2</td></tr><tr><td>10</td><td>S</td><td>GFp</td><td>KU, KT</td><td>S_ZygophyllumAlbum ⇒ no-food,no-medical,G_Zygophyllum,F_Zygophyllaceae</td><td>600</td><td>1</td></tr><tr><td>11</td><td>G</td><td>SFp</td><td>KU, KT, KD</td><td>G_Zygophyllum ⇒ no-food,no-medical,S_ZygophyllumAlbum,F_Zygophyllaceae</td><td>280</td><td>1</td></tr><tr><td>12</td><td>Fp</td><td>SG</td><td>KU, KT, KD</td><td>medical,no-food,F_Zingiberaceae ⇒ S_HedychiumCoronarium,G_Hedychium</td><td>29</td><td>1</td></tr><tr><td>13</td><td>GFp</td><td>S</td><td>KU, KT, KD</td><td>medical,no-food,G_Cinnamomum,F_Lauraceae ⇒ S_CinnamomumCassia</td><td>38</td><td>1</td></tr><tr><td>14</td><td>F</td><td>SGp</td><td>KU, KT, KD</td><td>F_Zygophyllaceae ⇒ no-food,no-medical,S_ZygophyllumAlbum,G_Zygophyllum</td><td>42</td><td>1</td></tr><tr><td>15</td><td>Fp</td><td>SGp</td><td>KU, KT, KD</td><td>food,F_Lauraceae ⇒ medical,G_Cinnamomum,S_CinnamomumVerum</td><td>1</td><td>1</td></tr><tr><td>16</td><td>GFp</td><td>Sp</td><td>KU, KT, KD</td><td>medical,F_Solanaceae,G_Solanum ⇒ food,S_SolanumLycopersicum</td><td>15</td><td>1</td></tr></tbody></table>

## 3.2 Observing patterns and implications

Three types of KEs were identified in the implications. KU type informs on the relationship of a plant, at any taxonomic level, with a use as food or medical care. The second KE type is the Taxonomic relationship type (KT), such as giving the family in the conclusion when the species is indicated in the premise. The third type (KD) corresponds to a KE resulting from the content of the Dataset, which represents a limit in this work. For instance, taxonomic referential web sites list 5 genera from the Piperaceae family <fnref n="5" />. As only one is present in the dataset (i.e. Piper), inferring that “a plant from the Piperaceae family is of the genus Piper” is wrong in the real life, but is true in this work as it results from a side effect of the dataset.

Except for patterns 2 and 6, all the implication patterns include KU (Table 3), suggesting at first sight that the latter are useful implications for the expert. But attention should be paid on implications combining KU, KT, or KD, and respected by implications with a scope value of 1, meaning that each one is associated to a single plant organism. Pattern 10 (scope 600) only reports information from the context as these rules only describe an organism by its

<footnote n="5">⁵ E.g. http://www.plantsoftheworldonline.org/ lists 5 plant genera.</footnote>
attributes. Similarly, patterns 11 and 14 indicate that a given genus or fam-
ily has only one species in the dataset, the other attributes being those of the
species, and these patterns could be considered as containing only KD. Most
of the patterns include KT. Including the taxonomy was crucial in this work
for FC processing in order to discover knowledge at a higher generic level, but
corresponds to a redundancy in the implications.

This preliminary analysis gives directions for pattern and implication post-
processing and classification. Some may be specific to some characteristics of
Knomana, such as the fact that attribute species is an object identifier, and
some could be generalized to all datasets.

The post-processing may have different forms for redundant or evident in-
formation: either removing it, or simply separating it from the rest, so that it
remains written but not distracting. KU, KD, KT can be highlighted in different
ways to distinguish them. Highlighting the different reasons under redundancy
or evident information (e.g. KT information versus logical redundancy due to the
fact that minimal generators are not used) would be useful. We could consider
removing redundancy only in premise [5] or only in conclusion, or in both.

Patterns also have to be analyzed. For example, pattern 4 (G -> Fp) could
be simplified as G -> p, as F is tacit given G. This would change the pattern
classification (initially KU KT), as it is now reduced to KU.

As regard with implications, a post-process could remove KT from the im-
plications, corresponding to a tacit knowledge as experts are familiar with the
taxonomy. As the redundancy due to KT may appear in the premise (e.g. *the
species and its genus*), in the conclusion, or in both (e.g. *a species implies a
family*), the post-process has to consider the implication in its entirety. This
approach differs with [5] where authors consider exclusively the left-minimal
premises for technical purpose (i.e. a fast computation of attribute closure and
a minimal left hand side in the implication). In addition, a filtering could lead
to remove pattern 2 implications that only express tacit knowledge.

KUs are the explicit KEs investigated by the expert and thus have to be put
forward. KDs present a particular situation in the implications as they result
from a lack of knowledge in the dataset. Pattern 6 has to be considered carefully
as it contains only KD. Thus, the experts have to be alerted of KD presence to
consider this aspect in the dataset analysis.

A classification of implications based on patterns seems to us relevant for
presenting them to the expert by groups having a coherent meaning: E.g. im-
plications providing information on the diversity of some plant families in the
dataset or pure information on the One Health Approach. Depending of the
number of rules in some categories, a classification may have a significant im-
pact for the expert, e.g. discarding or at least separating rules from pattern 10
distinguishes 600 rules that only recall initial data. We also guess that if a post-
processing is made, the way it is made has to be notified to the expert and it
should be indicated if this is reversible operation.

Finally, literature on association rules also faced the issues of extension of
non-redundant rules [17] and redundancy removal introduced by using a taxon-
omy in concept-based rules building [3]. Classifying association rules in a lattice
has been addressed in [2] in the context of fault localization, where rules are
described by elements of their premises, that can be inspiring in our case, using
patterns as an implication description.

4 Conclusion

This paper identifies 3 types of knowledge elements and 16 patterns that con-
stitute the implications from the Duquenne-Guigues basis on a formal context.
Each implication needs a specific consideration before being presented to the
expert. For instance, a post-process can be conducted to remove tacit knowledge
elements from implications, which may drive to delete some of them.

In a future work, we will study how the different patterns can be used to
display implications to experts by categories, that may help them to focus on
different aspects of the dataset. For instance, the user may focus on knowledge el-
ements related to some plant families in the dataset, or pure knowledge elements
on the plant uses at the family level.

This work is a preliminary study to the analysis of the Duquenne-Guigues
basis of implications resulting from a Relational Context Family of the Knomana
knowledge base. The general objective is to contribute in the decision support
process to identify plants that could be used by farmers to control pest. These
pesticidal plants will be an alternative to pesticide and antibiotics, considering
the One Health approach. Using this approach, one must be aware of the multi-
uses of these plants to prevent the intentional effects on the animals, the humans,
and their environment.

In a more general perspective, it would be relevant to examine how the
forms of post-processing, filtering and classification of patterns and rules can
be generalized in order to be able to apply these approaches to other datasets,
more particularly when several taxonomic relations are involved.

**Acknowledgments.** We warmly thank the reviewers for their comments. Part of the discussion has been notably improved thanks to their relevant remarks. This work was supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004.

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