Knowledge-based Selection of Association Rules for Text Mining

Dietmar Janetzko Hacène Cherfi 1 Roman Kennke Amedeo Napoli 1 Yannick Toussaint 1
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : A reoccuring problem in mining association rules is the selection of interesting association rules within the overall, and possibly huge set of extracted rules. The majority of previous work in this area relies on statistical methods for quality estimation and se-lection of association rules. However, strictly bottom-up approaches are oblivious of knowledge though knowledge may be available (e.g., provided by ontologies), and rule extraction may take advantage of it. In this paper, we conceive of the problem of selecting association rules as a classification task. A framework of a binary probabilistic classifier is introduced that uses ontologies in order to estimate whether and to which degree a rule expresses a mere taxonomic relationship. In so doing, selection of association rules (selection by elimination) is carried out by identifying and discarding trivial association rules.
Mots-clés : fouille de textes probabilistic reasoning textmining association rules terminological model model enrichment quality measure règles d'association modèle terminologique enrichissement de modèle mesure de qualité raisonnement probabiliste
Type de document :
Communication dans un congrès
R. Lopez de Màntaras and L. Saitta. 16h European Conference on Artificial Intelligence - ECAI'04, 2004, Valencia, Spain, IOS Press, pp.485-489, 2004
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Dietmar Janetzko, Hacène Cherfi, Roman Kennke, Amedeo Napoli, Yannick Toussaint. Knowledge-based Selection of Association Rules for Text Mining. R. Lopez de Màntaras and L. Saitta. 16h European Conference on Artificial Intelligence - ECAI'04, 2004, Valencia, Spain, IOS Press, pp.485-489, 2004. 〈inria-00107787〉

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