@incollection{fdi:010084434,
  title = {{C}ombining implications and conceptual analysis to learn from a pesticidal plant knowledge base},
  author = {{M}ahrach, {L}. and {G}utierrez, {A}. and {H}uchard, {M}. and {K}eip, {P}. and {M}arnotte, {P}. and {S}ilvie, {P}ierre and {M}artin, {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}upporting organic farming aims to find alternative solutions to synthetic pesticides and antibiotics, using local plants, to protect crops. {M}oreover, in the {O}ne {H}ealth approach ({OHA}), a pesticidal plant should not be harmful to humans, meaning it cannot be toxic if the crop is consumed or should have a limited and conscious use if it is used for medical care. {K}nowledge on plant use presented in the scientific literature was compiled in a knowledge base ({KB}). {T}he challenge is to develop a {KB} exploration method that informs experts (including farmers) about protection systems properties that respect {OHA}. {I}n this paper, we present a method that extracts the {D}uquenne-{G}uigues basis of implications from knowledge structured using {R}elational {C}oncept {A}nalysis ({RCA}). {W}e evaluate the impact of three data representations on the implications and their readability. {T}he experimentation is conducted on 562 plant species used to protect 15 crops against 29 pest species of the {N}octuidae family. {R}esults show that consistently splitting data into several tables fosters less redundant and more focused implications.},
  keywords = {},
  booktitle = {{G}raph-based representation and reasoning},
  numero = {12879},
  pages = {57--72},
  address = {{C}ham},
  publisher = {{S}pringer},
  series = {{L}ecture {N}otes in {C}omputer {S}cience},
  year = {2021},
  DOI = {10.1007/978-3-030-86982-3_5},
  ISBN = {978-3-030-86981-6},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084434},
}