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Manouba, Tunisia

Mustapha N.B.,Ecole Centrale Paris | Mustapha N.B.,Laboratory RIADI | Aufaure M.-A.,Ecole Centrale Paris | Zghal H.B.,Laboratory RIADI | Ghezala H.B.,Laboratory RIADI
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

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. © 2011 Springer-Verlag. Source


Ben Mustapha N.,Ecole Centrale Paris | Ben Mustapha N.,Laboratory RIADI | Aufaure M.-A.,Ecole Centrale Paris | Baazaoui Zghal H.,Laboratory RIADI | Ben Ghezala H.,Laboratory RIADI
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

With the growth rate of information repositories, most of the current research effort are focusing on improving the accuracy in searching and managing information (especially text data), because of lacking of adaptive knowledge representation to the information content of these systems. Besides, domain knowledge is evolving and consequently, ontologies should be automatically built and extended. Thus, introducing modularity paradigm in ontology engineering is now important to tackle scalability problems. In this paper, we address the problem of representing modular ontologies at an abstract level that can improve the traditional information system with higher efficiency, in the context of previous work aiming at integrating ontology learning in traditional Information Retrieval systems on the web. The contribution consists in organizing ontology elements into semantic three-layered ontology warehouse (topic classification, domain knowledge representation, and module representation). The proposed model has been applied for textual content semantic search and relevance improvement has been observed. © 2012 Springer-Verlag. Source

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