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Motivation: With proliferation of new discoveries in biomedical research areas as well as dramatic increase in volume of publications, it is imperative to design efficient search strategies and develop effective search engines to look up relevant documents. Results: This paper proposes a novel ontology graph based scheme for query expansion. In this scheme, a Personalized PageRank algorithm is first used on an ontology graph derived from multiple diverse biomedical ontologies to perform user query expansion. Then, a weighted edge semantic similarity measure is used to filter out the less relevant terms in the expanded query term set, further improving the relevance of information retrieval. Extensive experimental results show that this new search scheme outperforms the popular Lucene approach by 22% while other existing query expansion approaches are unable to beat the free-text based Lucene strategy. Furthermore, a graph-based biomedical search engine, G-Bean (Graph-based biomedical search engine), is implemented based on this new scheme. Not only can G-Bean rank the initial search results based on the relevance to the user query, but also it discovers user’s true search intention and conducts a new query based on the articles that he/she has already interest in to retrieve additional relevant documents. G-Bean provides a more accurate and easier to use Web interface for searching the MEDLINE database, compared the most popular biomedical search engine PubMed.

Availability: The search engine, G-Bean, is available at: http://bioinformatics. clemson. edu/G-Bean/index. php/Supplementary information: http://bioir. cs. clemson. edu: 8080/BioIRWeb/supplement. jsp Contact: jzwang@ clemson. edu