Learning to Rank Results in Relational Keyword SearchesReport
Keyword search within databases has become a hot topic within the research community as databases store increasing amounts of information. Users require an effective method to retrieve information from these databases without learn- ing complex query languages (viz. SQL). Despite the recent research interest, performance and search effectiveness have not received equal attention, and scoring functions in par- ticular have become increasingly complex while providing only modest benefits with regards to the quality of search results. An analysis of the factors appearing in existing scor- ing functions suggests that some factors previously deemed critical to search effectiveness are at best loosely correlated with relevance. We consider a number of these different scoring factors and use machine learning to create a new scoring function that provides significantly better results than existing approaches. We simplify our scoring function by systematically removing the factors with the lowest weight and show that this version still outperforms the previous state-of-the-art in this area.
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Coffman, Joel, and Alfred Weaver. "Learning to Rank Results in Relational Keyword Searches." University of Virginia Dept. of Computer Science Tech Report (2011).
University of Virginia, Department of Computer Science