Enhancing search result clustering with semantic indexing

Semantic search results clustering is one of the most wanted functionalities of many information retrieval systems including general web search engines as well as domain specific article portals or digital libraries. It may advice the users to describe the need for information in a more precise way. In this paper, we discuss a framework of document description extension which utilizes domain knowledge and semantic similarity. Our idea is based on application of Tolerance Rough Set Model, semantic information extracted from source text and domain ontology to approximate concepts associated with documents and to enrich the vector representation. Some document representation models including document meta-data, citations and semantic information build using MeSH ontology. We compare those models in a search result clustering problem over the freely accessed biomedical research articles from Pubmed Cetral (PMC) portal. The experimental results are showing the advantages of the proposed models.

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