A data mining approach to PubMed query refinement

Abstract

Finding disease relationships requires laborious examination of hundreds of possible candidate heterogeneous factors. Much of the related information is currently contained in biological and medical journals, making biomedical text mining a central bioinformatic problem. More than 14 million abstracts of such papers are contained in the Medline collection and are available online. In this paper we present a data mining engine, namely MeSH Terms Associator (MTA), that has been employed in a distributed architecture to refine a generic PubMed query by means of discovery of concept relations in the form of association rules. However, the number of discovered association rules is usually high and the interest of most of them does not fulfil user expectations. In addition, the presentation of thousands of rules can discourage users from interpreting them. To overcome this problem we investigate the application of some filtering techniques. Experimental results on datasets corresponding to real-world biomedical queries are discussed and future directions are drawn.

An intelligent human-expert forum system based on fuzzy information retrieval technique

“…Moreover, the parsed term set eliminates stop words that do not belong in the query term set. Based on fuzzy query term expansion, the remaining query term set would expand the term members itself, and becomes an expanded query set (Berardi et al, 2004;Billerbeck, Scholer, Williams, & Zobel, 2003;Chen, Yu, Furuse, & Ohbo, 2001;Li & Agrawal, 2000). Finally, the search engine employs the expanded query set and fuzzy query technology to discover relevant discussions in the discussion repository.…”