@incollection{fdi:010077513,
  title = {{E}ffects of input data formalisation in relational concept analysis for a data model with a ternary relation},
  author = {{K}eip, {P}. and {G}utierrez, {A}. and {H}uchard, {M}. and {L}e {B}er, {F}. and {S}arter, {S}. and {S}ilvie, {P}ierre and {M}artin, {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}oday pesticides, antimicrobials and other pest control products used in conventional agriculture are questioned and alternative solutions are searched out. {S}cientific literature and local knowledge describe a significant number of active plant-based products used as bio-pesticides. {T}he {K}nomana ({KNO}wledge {MANA}gement on pesticide plants in {A}frica) 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.). {C}onsidering the needs expressed by the domain experts, {F}ormal {C}oncept {A}nalysis ({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. {T}he {K}nomana data model used during the data collection is an entity-relationship model including both binary and ternary relationships between entities of different categories. {T}his leads us to investigate the use of {R}elational {C}oncept {A}nalysis ({RCA}), a variant of {FCA} on these data. {W}e 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}. {T}hese 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.},
  keywords = {{AFRIQUE}},
  booktitle = {{F}ormal concept analysis},
  numero = {11511},
  pages = {191--207},
  address = {{C}ham},
  publisher = {{S}pringer},
  series = {{L}ecture {N}otes in {C}omputer {S}cience},
  year = {2019},
  DOI = {10.1007/978-3-030-21462-3_13},
  ISBN = {978-3-03-021461-6},
  URL = {https://www.documentation.ird.fr/hor/fdi:010077513},
}