An interactive clustering-based approach to integrating source query interfaces on the deep Web

  • Authors:

  • Wensheng Wu;Clement Yu;AnHai Doan;Weiyi Meng

  • Affiliations:

  • University of Illinois at Urbana-Champaign;University of Illinois at Chicago;University of Illinois at Urbana-Champaign;SUNY at Binghamton

  • Venue:
  • SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
  • Year:
  • 2004

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Abstract

An increasing number of data sources now become available on the Web, but often their contents are only accessible through query interfaces. For a domain of interest, there often exist many such sources with varied coverage or querying capabilities. As an important step to the integration of these sources, we consider the integration of their query interfaces. More specifically, we focus on the crucial step of the integration: accurately matching the interfaces. While the integration of query interfaces has received more attentions recently, current approaches are not sufficiently general: (a) they all model interfaces with flat schemas; (b) most of them only consider 1:1 mappings of fields over the interfaces; (c) they all perform the integration in a blackbox-like fashion and the whole process has to be restarted from scratch if anything goes wrong; and (d) they often require laborious parameter tuning. In this paper, we propose an interactive, clustering-based approach to matching query interfaces. The hierarchical nature of interfaces is captured with ordered trees. Varied types of complex mappings of fields are examined and several approaches are proposed to effectively identify these mappings. We put the human integrator back in the loop and propose several novel approaches to the interactive learning of parameters and the resolution of uncertain mappings. Extensive experiments are conducted and results show that our approach is highly effective.