Matches in ScholarlyData for { ?s ?p In this paper, we propose the use of a minimal generic basis of association rules between terms (AR), in order to automatically enrich an existing domain ontology. For this purpose, three distance measures are defined to link the candidate terms identified from AR, to the initial concepts in the ontology. The final result is a proxemic conceptual network which contains additional implicit knowledge. Therefore, to evaluate our ontology enrichment approach, we propose a novel document indexing approach based on this proxemic network. The experiments carried out on the OHSUMED document collection of the TREC 9 filtring track and MeSH ontology, showed that our conceptual indexing approach could considerably enhance information retrieval effectiveness.. }
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- 125 abstract "In this paper, we propose the use of a minimal generic basis of association rules between terms (AR), in order to automatically enrich an existing domain ontology. For this purpose, three distance measures are defined to link the candidate terms identified from AR, to the initial concepts in the ontology. The final result is a proxemic conceptual network which contains additional implicit knowledge. Therefore, to evaluate our ontology enrichment approach, we propose a novel document indexing approach based on this proxemic network. The experiments carried out on the OHSUMED document collection of the TREC 9 filtring track and MeSH ontology, showed that our conceptual indexing approach could considerably enhance information retrieval effectiveness.".