Contextual Ontology Module Learning from Web Snippets and Past User Queries

Nesrine Ben Mustapha (1) , Marie-Aude Aufaure (1) , Hajer Baazaoui (2) , Henda Ben Ghezala (2)

1 MAS - Mathématiques Appliquées aux Systèmes - EA 4037
2 RIADI - Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba]

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, some arising approaches of ontology module extraction aim at solving the problem of identifying ontology fragment candidates that are relevant for the application. In these approaches, the main problem 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.

Domaines: Autre [cs.OH]

Nesrine Ben Mustapha, Marie-Aude Aufaure, Hajer Baazaoui, Henda Ben Ghezala. Contextual Ontology Module Learning from Web Snippets and Past User Queries. 15th International Conference, KES 2011, Sep 2011, Kaiserslautern, Germany. pp.538-547, ⟨10.1007/978-3-642-23863-5_55⟩. ⟨hal-00831658⟩