Relevance-Based Retrieval on Hidden-Web Text Databases Without Ranking Support

Abstract

Many online or local data sources provide powerful querying mechanismsbut limited ranking capabilities. For instance, PubMed allows users tosubmit highly expressive Boolean keyword queries, but ranks the queryresults by date only. However, a user would typically prefer a rankingby relevance, measured by an Information Retrieval (IR) rankingfunction. The naive approach would be to submit a disjunctive query withall query keywords, retrieve the returned documents, and then re-rankthem. Unfortunately, such an operation would be very expensive due tothe large number of results returned by disjunctive queries. In thispaper we present algorithms that return the top results for a query,ranked according to an IR-style ranking function, while operating on topof a source with a Boolean query interface with no ranking capabilities(or a ranking capability of no interest to the end user). The algorithmsgenerate a series of conjunctive queries that return only documents thatare candidates for being highly ranked according to a relevance metric.Our approach can also be applied to other settings where the ranking ismonotonic on a set of factors (query keywords in IR) and the sourcequery interface is a Boolean expression of these factors. Ourcomprehensive experimental evaluation on the PubMed database and a TRECdataset show that we achieve order of magnitude improvement compared tothe current baseline approaches.