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Exploring Fuzzy Association Rules in Semantic Network Enrichment Improvement of the Semantic Indexing Process
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Author(s): Souheyl Mallat (Faculty of Science of Monastir, Tunisia), Emna Hkiri (LATICE Laboratory, Tunisia)and Mounir Zrigui (Faculty of Science of Monastir, Tunisia)
Copyright: 2018
Pages: 21
Source title:
Innovations, Developments, and Applications of Semantic Web and Information Systems
Source Author(s)/Editor(s): Miltiadis D. Lytras (American College of Greece, Greece), Naif Aljohani (King Abdulaziz University, Saudi Arabia), Ernesto Damiani (University of Milan, Italy)and Kwok Tai Chui (The Open University of Hong Kong, Hong Kong)
DOI: 10.4018/978-1-5225-5042-6.ch006
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Abstract
In the aim of natural language processing applications improvement, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource EuroWordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules modelOur proposed indexing approach can be applied to text documents in various languages. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing approach provides encouraging results.
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