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On Topic Categorization of PubMed Query Results

Abstract : Nowadays, people frequently use search engines in order to find the information they need on the Web. Especially Web search constitutes a basic tool used by million researchers in their everyday work. A very popular indexing engine, concerning life sciences and biomedical research is PubMed. PubMed is a free database accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. The present search engines usually return search results in a global ranking making it difficult to the users to browse in different topics or subtopics that they query. Because of this mixing of results belonging to different topics, the average users spend a lot of time to find Web pages, best matching their query. In this paper, we propose a novel system to address this problem. We present and evaluate a methodology that exploits semantic text clustering techniques in order to group biomedical document collections in homogeneous topics. In order to provide more accurate clustering results, we utilize various biomedical ontologies, like MeSH and GeneOntology. Finally, we embed the proposed methodology in an online system that post-processes the PubMed online database in order to provide to users the retrieved results according to well formed topics.
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Andreas Kanavos, Christos Makris, Evangelos Theodoridis. On Topic Categorization of PubMed Query Results. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.556-565, ⟨10.1007/978-3-642-33412-2_57⟩. ⟨hal-01523043⟩

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