RefMed

Abstract

Finding related articles from the PubMed (a large biomedical literature repository) is challenging because it is hard to express the user's specific relevance in the given query interface and a keyword query typically retrieves many results. Biomedical researchers spend a critical amount of time (e.g., often more than several days) in the literature search process. This paper proposes RefMed, a novel search system for PubMed, which supports relevance ranking by enabling relevance feedback on PubMed. RefMed first returns initial result documents for a user's keyword query as in PubMed. The user then makes relevance judgments on some of the resultant documents while browsing them. Once the user pushes the feedback, the system induces a relevance function using RankSVM and ranks the results according to the function. To realize the ad-hoc relevance retrieval on PubMed, RefMed tightly integrates RankSVM within RDBMS and runs the rank learning and process on the fly with a response time of a few minutes.Our qualitative experiments with biomedical researchers show that RefMed substantially reduces the amount of effort required to search related PubMed articles. RefMed is accessible at http://dm.postech.ac.kr/refmed.