A PubMed Meta Search Engine Based on Biomedical Entity Mining

Abstract

Biomedical knowledge stored in the web is increasing significantly as most of the biomedical research papers are published online. Biomedical entity extraction is a crucial procedure for efficient text analysis and retrieval. PubMed is a very popular indexing engine, concerning life sciences and biomedical research. Being a free database, it accesses primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. In this work, we propose a metasearch engine over PubMed, which classifies PubMed results according to their specific topic and the extracted Biomedical entities. This method helps researchers to browse and search in the retrieved results. In order to provide more accurate clustering results, we utilize the biomedical ontology, named MeSH as well as RxNorm which is a tool for supporting semantic interoperation between drug terminologies and pharmacy knowledge base systems. Finally, we embed the proposed methodology in an online system.