Enabling multi-level relevance feedback on pubmed by integrating rank learning into DBMS

Background: Finding relevant articles from PubMed is challenging because it is hard to express the user’s specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. Results: RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed “tightly” integrates the RankSVM into R...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Hwanjo Yu, Taehoon Kim, Jinoh Oh, Ilhwan Ko, Sungchul Kim, Wook-Shin Han
• Keywords: CIKM 2009, Database, Keyword Queries, Multi-level Relevance Feedback, Relevant Articles