Exploring path query results through relevance feedback

Huiping Cao, Yan Qi, Kasim Candan, Maria Luisa Sapino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Feedback driven data exploration schemes have been implemented for non-structured data (such as text) and document-centric XML collections where formulating precise queries is often impossible. In this paper, we study the problem of enabling exploratory access, through ranking, to data-centric XML. Given a path query and a set of results identified by the system to this query over the data, we consider feedback which captures the user's preference for some features over the others. The feedback can be "positive" or "negative". To deal with feedback, we develop a probabilistic feature significance measure and describe how to use this for ranking results in the presence of dependencies between the path features. We bring together these techniques in AXP, a system for adaptive and exploratory path retrieval. The experimental results show the effectiveness of the proposed techniques.

Original languageEnglish (US)
Title of host publicationACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Pages1959-1962
Number of pages4
DOIs
StatePublished - Dec 1 2009
EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: Nov 2 2009Nov 6 2009

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

ConferenceACM 18th International Conference on Information and Knowledge Management, CIKM 2009
CountryChina
CityHong Kong
Period11/2/0911/6/09

Keywords

  • Data-centric XML
  • Feature cover
  • Inter-dependent structural feature
  • Relevance feedback

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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