Contextual ontology module learning from web snippets and past user queries

Authors:
Nesrine Ben Mustapha;Marie-Aude Aufaure;Hajer Baazaoui Zghal;Henda Ben Ghezala
Affiliations:
Ecole Centrale Paris, MAS Laboratory, Business Intelligence Team, France and Laboratory RIADI, ENSI, La Manouba, Tunisia;Ecole Centrale Paris, MAS Laboratory, Business Intelligence Team, France;Laboratory RIADI, ENSI, La Manouba, Tunisia;Laboratory RIADI, ENSI, La Manouba, Tunisia
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Year:
2011

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Abstract

In this paper, we focus on modularization aspects for query reformulation in ontology-based question answering on the Web. The main objective is to automatically learn ontology modules that cover search terms of the user. Indeed, the main problem is that current approaches of ontology modularization consider only the input existant ontologies, instead of underlying semantics found in texts. This work proposes an approach of contextual ontology module learning covering particular search terms by analyzing past user queries and snippets provided by search engines. The obtained contextual modules will be used for query reformulation. The proposal has been evaluated on the ground of semantic cotopy measure of discovered ontology modules, relevance of search results.