The VLDB Journal

, Volume 17, Issue 1, pp 81–115 | Cite as

TopX: efficient and versatile top-k query processing for semistructured data

  • Martin Theobald
  • Holger Bast
  • Debapriyo Majumdar
  • Ralf Schenkel
  • Gerhard Weikum
Open Access
Special Issue Paper

Abstract

Recent IR extensions to XML query languages such as Xpath 1.0 Full-Text or the NEXI query language of the INEX benchmark series reflect the emerging interest in IR-style ranked retrieval over semistructured data. TopX is a top-k retrieval engine for text and semistructured data. It terminates query execution as soon as it can safely determine the k top-ranked result elements according to a monotonic score aggregation function with respect to a multidimensional query. It efficiently supports vague search on both content- and structure-oriented query conditions for dynamic query relaxation with controllable influence on the result ranking. The main contributions of this paper unfold into four main points: (1) fully implemented models and algorithms for ranked XML retrieval with XPath Full-Text functionality, (2) efficient and effective top-k query processing for semistructured data, (3) support for integrating thesauri and ontologies with statistically quantified relationships among concepts, leveraged for word-sense disambiguation and query expansion, and (4) a comprehensive description of the TopX system, with performance experiments on large-scale corpora like TREC Terabyte and INEX Wikipedia.

Keywords

Efficient XML full-text search Content- and structure-aware ranking Top-k query processing Cost-based index access scheduling Probabilistic candidate pruning Dynamic query expansion DB&IR integration 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Martin Theobald
    • 1
  • Holger Bast
    • 1
  • Debapriyo Majumdar
    • 1
  • Ralf Schenkel
    • 1
  • Gerhard Weikum
    • 1
  1. 1.Max-Planck Institute for InformaticsSaarbrueckenGermany

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