Utilizing Conceptual Indexing to Enhance the Effectiveness of Vector Space Model

This is the authors' abstract. We don't add key points for this paper.

One of the main purposes of the semantic Web is to improve the retrieval performance of search systems.Unlike keyword based search systems, the semantic search systems aim to discover pages related to the query's concepts rather than merely collect ing all pages instantiating its keywords.To that end, the concepts must be defined to be used as a semantic index instead of the traditional lexical one.In fact, The Arabic language is still far fro m being semantically searchable.Therefore, this paper proposed a model that exp loits the Universal Word Net ontology for producing an Arabic Concepts-Space to be used as the index of Semantic Vector Space Model.The Vector Space Model is one of the most common in formation ret rieval models due to its capability of expressing the documents' structure.However, like all key word-based search systems, its sensitivity to the query's keywords reduces its retrieval effectiveness.The proposed model allo ws the VSM to represent Arabic documents by their topic, and thus classify them semantically.Th is , consequently, enhances the retrieval effectiveness of the search system.