Semantic retrieval and distribution of relevant medical knowledge
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ABSTRACT In the fast growing world of information, the amount of medical knowledge is growing at an exponential level. It has now become a very difficult task for a regular person to keep up with all the new discoveries and updates in this domain. This thesis describes an approach to semantically retrieve and distribute the medical data/information to the respective health records (people). This system comprises of sample health records and health publications from PubMed. The system performs a semantic matchmaking algorithm to find the relevant publications in PubMed for any particular health record (profile) using BioPortal Ontologies and UMLS. It then assigns a rank based on a semantic ranking algorithm and displays the results to the user. Our system empowers the users and enables them to discover hidden but relevant information. The result of the evaluation clearly proves that our system retrieves all the relevant information better than syntactic searches.