Integrating association mining into relevance feedback for biomedical literature search

Finding highly relevant articles from biomedical databases is challenging because it is often very difficult to accurately express a user's underlying intention through keywords, and a keyword-based query normally returns a long list of hits. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback. In this system, we developed a weighted interest measure and an association mining algorithm to find the strength of association between the query and each concept in the article(s) selected by the user as feedback. The top κ concepts were utilized to form a k-profile used for the next-round search. BiomedSearch relies on Unified Medical Language System (UMLS) knowledge sources to map text files to standard biomedical concepts. It was designed to support queries with any levels of complexity. The prototype system was preliminarily evaluated using three topics and related Genomics data from TREC (Text Retrieval Conference) 2006 Genomics Track. Initial results indicated that BiomedSearch could effectively utilize users' relevance feedback to improve search performance.