Ontology Enhanced Clustering Based Summarization of Medical Documents
A. A. Kogilavani, P. Balasubramanie · 2009
Abstract—The growing amount of data, the short of structured information and the information diversity have made information and knowledge management a real challenge. Even though larger quantities of data are merely available, easier access to the required information at the right time and in the most appropriate form is still difficult. Particularly the medical domain suffers typically from the problem of information overload since it is essential for physicians and researchers in medicine and biology to have quick and efficient access to up-to-date information according to their interests and requirements. Methodologies are needed to support users whose knowledge of medical vocabularies is inadequate to find the desired information and for medical experts who search for information outside their field of expertise. In order to effectively utilize the vast amount of biomedical information and to provide a solution to information overload problem, the proposed system combines both document clustering and text summarization technique. In the proposed system the user query is revised by mapping query with synonyms and semantically related concepts using MeSH ontology knowledge source. Based on the revised query medical documents are retrieved from trustworthy online sources and those documents are clustered to generate cluster wise summary. Index Terms—query expansion, text summarization, document clustering, feature extraction, summary generation I.