Abstract
Today pesticides, antimicrobials and other pest control products used in conventional agriculture are questioned and alternative solutions are searched out. Scientific literature and local knowledge describe a significant number of active plant-based products used as bio-pesticides. The Knomana (KNOwledge MANAgement on pesticide plants in Africa) project aims to gather data about these bio-pesticides and implement methods to support the exploration of knowledge by the potential users (farmers, advisers, researchers, retailers, etc.). Considering the needs expressed by the domain experts, Formal Concept Analysis (FCA) appears as a suitable approach, due do its inherent qualities for structuring and classifying data through conceptual structures that provide a relevant support for data exploration. The Knomana data model used during the data collection is an entity-relationship model including both binary and ternary relationships between entities of different categories. This leads us to investigate the use of Relational Concept Analysis (RCA), a variant of FCA on these data. We consider two different encodings of the initial data model into sets of object-attribute contexts (one for each entity category) and object-object contexts (relationships between entity categories) that can be used as an input for RCA. These two encodings are studied both quantitatively (by examining the produced conceptual structures size) and qualitatively, through a simple, yet real, scenario given by a domain expert facing a pest infestation.
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Aspergillus genus groups several species of microscopic fungi.
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References
Baader, F., Distel, F.: A finite basis for the set of \(\cal{EL}\)-implications holding in a finite model. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 46–61. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78137-0_4
Bazin, A., Carbonnel, J., Huchard, M., Kahn, G.: On-demand relational concept analysis. CoRR abs/1803.07847 (2018). http://arxiv.org/abs/1803.07847
Bazin, A., Carbonnel, J., Huchard, M., Kahn, G., Keip, P., Ouzerdine, A.: On-demand relational concept analysis. In: Cristea, D., et al. (eds.) ICFCA 2019, LNAI 11511, pp. 155–172. Springer, Cham (2019)
Dicky, H., Dony, C., Huchard, M., Libourel, T.: Ares, adding a class and restructuring inheritance hierarchy. In: Onzièmes Journées Bases de Données Avancées, Nancy, France (Informal Proceedings), pp. 25–42 (1995)
Ferré, S.: A proposal for extending formal concept analysis to knowledge graphs. In: Baixeries, J., Sacarea, C., Ojeda-Aciego, M. (eds.) ICFCA 2015. LNCS (LNAI), vol. 9113, pp. 271–286. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19545-2_17
Ferré, S., Cellier, P.: How hierarchies of concept graphs can facilitate the interpretation of RCA lattices? In: 14th International Conference CLA 2018, Olomouc, Czech Republic, pp. 69–80 (2018)
Ferré, S., Ridoux, O., Sigonneau, B.: Arbitrary relations in formal concept analysis and logical information systems. In: Dau, F., Mugnier, M.-L., Stumme, G. (eds.) ICCS-ConceptStruct 2005. LNCS (LNAI), vol. 3596, pp. 166–180. Springer, Heidelberg (2005). https://doi.org/10.1007/11524564_11
Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: Delugach, H.S., Stumme, G. (eds.) ICCS-ConceptStruct 2001. LNCS (LNAI), vol. 2120, pp. 129–142. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44583-8_10
Ganter, B., Wille, R.: Formal Concept Analysis - Mathematical Foundations. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-642-59830-2
Guédi, A.O., Huchard, M., Miralles, A., Nebut, C.: Sizing the underlying factorization structure of a class model. In: 17th IEEE International Conference EDOC 2013, Vancouver, BC, Canada, pp. 167–172 (2013)
Hacene, M.R., Huchard, M., Napoli, A., Valtchev, P.: Relational concept analysis: mining concept lattices from multi-relational data. Ann. Math. Artif. Intell. 67(1), 81–108 (2013)
Kötters, J.: Concept lattices of a relational structure. In: Pfeiffer, H.D., Ignatov, D.I., Poelmans, J., Gadiraju, N. (eds.) ICCS-ConceptStruct 2013. LNCS (LNAI), vol. 7735, pp. 301–310. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35786-2_23
Kuznetsov, S.O., Makhalova, T.P.: On interestingness measures of formal concepts. CoRR abs/1611.02646 (2016). http://arxiv.org/abs/1611.02646
Lehmann, F., Wille, R.: A triadic approach to formal concept analysis. In: 3rd International Conference ICCS 1995, Santa Cruz, California, USA, pp. 32–43 (1995)
Liquière, M., Sallantin, J.: Structural machine learning with galois lattice and graphs. In: ICML, Madison, Wisconsin, pp. 305–313 (1998)
van der Merwe, D., Obiedkov, S., Kourie, D.: AddIntent: a new incremental algorithm for constructing concept lattices. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 372–385. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24651-0_31
Mimouni, N., Nazarenko, A., Salotti, S.: A conceptual approach for relational IR: application to legal collections. In: Baixeries, J., Sacarea, C., Ojeda-Aciego, M. (eds.) ICFCA 2015. LNCS (LNAI), vol. 9113, pp. 303–318. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19545-2_19
Miralles, A., Molla, G., Huchard, M., Nebut, C., Deruelle, L., Derras, M.: Class model normalization - outperforming formal concept analysis approaches with aoc-posets. In: 12th International Conference on CLA 2015, Clermont-Ferrand, France, pp. 111–122 (2015). http://ceur-ws.org/Vol-1466/paper09.pdf
Nica, C., Braud, A., Dolques, X., Huchard, M., Le Ber, F.: Extracting hierarchies of closed partially-ordered patterns using relational concept analysis. In: Haemmerlé, O., Stapleton, G., Faron Zucker, C. (eds.) ICCS 2016. LNCS (LNAI), vol. 9717, pp. 17–30. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40985-6_2
Ouzerdine, A., Braud, A., Dolques, X., Huchard, M., Le Ber, F.: Régler le processus d’exploration dans l’analyse relationnelle de concepts. le cas de données hydroécologiques. In: Actes de la 19e conférence sur l’extraction et la gestion de connaissances (EGC 2019). Nouvelles Technologies de l’Information (2019)
Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing iceberg concept lattices with Titanic. Data Knowl. Eng. 42(2), 189–222 (2002)
Voutsadakis, G.: Polyadic concept analysis. Order 19(3), 295–304 (2002). https://doi.org/10.1023/A:1021252203599
Acknowledgement
This work was supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004 and by INRA-CIRAD GloFoodS metaprogram (KNOMANA project).
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Keip, P. et al. (2019). Effects of Input Data Formalisation in Relational Concept Analysis for a Data Model with a Ternary Relation. In: Cristea, D., Le Ber, F., Sertkaya, B. (eds) Formal Concept Analysis. ICFCA 2019. Lecture Notes in Computer Science(), vol 11511. Springer, Cham. https://doi.org/10.1007/978-3-030-21462-3_13
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