Les bases de connaissances du web sémantique sont générées à partir de plateformes collaboratives ou d’intégration de sources diverses. Cela entraîne évidemment des manques d’information et des erreurs ou incohérences. De plus, dans les programmes d’extraction de connaissances à partir de ces sources il est erroné de considérer que l’absence d’une information dans la base de connaissances équivaut à son inexistence, il faut donc munir la source interrogée d’informations complémentaires permettant de déterminer quand une relation interrogée peut être considérée comme complète. Le volume important de certaines bases nous permet d’utiliser l’inégalité de Hoeffding pour en extraire des règles de cardinalité significatives. Les expérimentations menées sur DBpedia et sur une base de connaissances numismatiques démontrent la faisabilité de l’approche et la pertinence des contraintes extraites.
Big semantic web knowledge bases (KB) are generated from collaborative platforms or by integration of various sources. This naturally induces lack of information, and inconsistencies. Moreover, missing data must not be considered as non existing data. Applications that query these KB’s content need complementary information to decide whether the queried data is complete. Based on KB’s volume, it is possible to discover such kind of information. We present an algorithm for extracting significant maximum cardinality rules from a knowledge base. We use Hoeffding’s inequality to define the likelihood for a constraint to be significant. Experiments conducted on DBpedia and on a numismatic knowledge base resulting from an integration process demonstrate the feasibility of the approach and the relevance of the discovered contextual constraints.
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Keywords: Cardinality Mining, Contextual Constraints, Knowledge Base.
Arnaud Giacometti 1 ; Béatrice Markhoff 1 ; Arnaud Soulet 1
CC-BY 4.0
@article{ROIA_2022__3_3-4_223_0,
author = {Arnaud Giacometti and B\'eatrice Markhoff and Arnaud Soulet},
title = {D\'ecouverte de cardinalit\'es maximales significatives dans des bases de connaissances},
journal = {Revue Ouverte d'Intelligence Artificielle},
pages = {223--251},
publisher = {Association pour la diffusion de la recherche francophone en intelligence artificielle},
volume = {3},
number = {3-4},
year = {2022},
doi = {10.5802/roia.30},
language = {fr},
url = {https://roia.centre-mersenne.org/articles/10.5802/roia.30/}
}
TY - JOUR AU - Arnaud Giacometti AU - Béatrice Markhoff AU - Arnaud Soulet TI - Découverte de cardinalités maximales significatives dans des bases de connaissances JO - Revue Ouverte d'Intelligence Artificielle PY - 2022 SP - 223 EP - 251 VL - 3 IS - 3-4 PB - Association pour la diffusion de la recherche francophone en intelligence artificielle UR - https://roia.centre-mersenne.org/articles/10.5802/roia.30/ DO - 10.5802/roia.30 LA - fr ID - ROIA_2022__3_3-4_223_0 ER -
%0 Journal Article %A Arnaud Giacometti %A Béatrice Markhoff %A Arnaud Soulet %T Découverte de cardinalités maximales significatives dans des bases de connaissances %J Revue Ouverte d'Intelligence Artificielle %D 2022 %P 223-251 %V 3 %N 3-4 %I Association pour la diffusion de la recherche francophone en intelligence artificielle %U https://roia.centre-mersenne.org/articles/10.5802/roia.30/ %R 10.5802/roia.30 %G fr %F ROIA_2022__3_3-4_223_0
Arnaud Giacometti; Béatrice Markhoff; Arnaud Soulet. Découverte de cardinalités maximales significatives dans des bases de connaissances. Revue Ouverte d'Intelligence Artificielle, Volume 3 (2022) no. 3-4, pp. 223-251. doi : 10.5802/roia.30. https://roia.centre-mersenne.org/articles/10.5802/roia.30/
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