Relevance-based Retrieval on Hidden-Web Text Databases without Ranking Support

14 Pages Posted: 6 Oct 2009 Last revised: 16 Feb 2012

See all articles by Vagelis Hristidis

Vagelis Hristidis

affiliation not provided to SSRN

Yuheng Hu

University of Illinois at Chicago, College of Business Administration

Panagiotis G. Ipeirotis

New York University - Leonard N. Stern School of Business

Multiple version iconThere are 2 versions of this paper

Date Written: September 1, 2009

Abstract

Many online or local data sources provide powerful querying mechanisms but limited ranking capabilities. For instance, PubMed allows users to submit highly expressive Boolean keyword queries, but ranks the query results by date only. However, a user would typically prefer a ranking by relevance, measured by an information retrieval (IR) ranking function. A naive approach would be to submit a disjunctive query with all query keywords, retrieve all the returned matching documents, and then re-rank them. Unfortunately, such an operation would be very expensive due to the large number of results returned by disjunctive queries. In this paper we present algorithms that return the top results for a query, ranked according to an IR-style ranking function, while operating on top of a source with a Boolean query interface with no ranking capabilities (or a ranking capability of no interest to the end user). The algorithms generate a series of conjunctive queries that return only documents that are candidates for being highly ranked according to a relevance metric. Our approach can also be applied to other settings where the ranking is monotonic on a set of factors (query keywords in IR) and the source query interface is a Boolean expression of these factors. Our comprehensive experimental evaluation on the PubMed database and a TREC dataset show that we achieve order of magnitude improvement compared to the current baseline approaches

Keywords: Hidden-web databases, Keyword Search, Top-k ranking

Suggested Citation

Hristidis, Vagelis and Hu, Yuheng and Ipeirotis, Panagiotis G., Relevance-based Retrieval on Hidden-Web Text Databases without Ranking Support (September 1, 2009). IEEE Transactions on Knowledge and Data Engineering. Available at SSRN: https://ssrn.com/abstract=1483479

Vagelis Hristidis (Contact Author)

affiliation not provided to SSRN ( email )

No Address Available

Yuheng Hu

University of Illinois at Chicago, College of Business Administration ( email )

601 S Morgan St
Chicago, IL 60607
United States

HOME PAGE: http://yuhenghu.com

Panagiotis G. Ipeirotis

New York University - Leonard N. Stern School of Business ( email )

44 West Fourth Street
Ste 8-84
New York, NY 10012
United States
+1-212-998-0803 (Phone)

HOME PAGE: http://www.stern.nyu.edu/~panos

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