Ontology-based relevance analysis for automatic reference tracking
International Journal of Computer Applications in Technology
Automatic reference tracking with on-demand relevance filtering based on user's interest
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Evaluation of a hybrid approach of personalized web information retrieval using the FIRE data set
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Personalised search-a hybrid approach for web information retrieval and its evaluation
International Journal of Knowledge and Web Intelligence
Web query disambiguation using PageRank
Journal of the American Society for Information Science and Technology
Automatic Topic Ontology Construction Using Semantic Relations from WordNet and Wikipedia
International Journal of Intelligent Information Technologies
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It is well known that ontologies will become a key piece, as they allow making the semantics of Semantic Web content explicit. In spite of the big advantages that the Semantic Web promises, there are still several problems to solve. Those concerning ontologies include their availability, development, and evolution. In the area of information retrieval, the dimension of document vectors plays an important role. Firstly, with higher index dimensions the indexing structures suffer from the "curse of dimensionality" and their efficiency rapidly decreases. Secondly, we may not use exact words when looking for a document, thus we miss some relevant documents. LSI is a numerical method, which discovers latent semantics in documents by creating concepts from existing terms. In this paper we present a basic method of mapping LSI concepts on given ontology (Word- Net), used both for retrieval recall improvement and dimension reduction.We offer experimental results for this method on a subset of TREC collection, consisting of Los Angeles Times articles.