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

TopX: efficient and versatile top- k query processing for semistructured data 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

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

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Publisher
Springer-Verlag
Copyright
Copyright © 2007 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-007-0072-z
Publisher site
See Article on Publisher Site

References

  • Region proximity in metric spaces and its use for approximate similarity search
    Amato, G.; Rabitti, F.; Savino, P.; Zezula, P.
  • Report on the DB/IR panel at SIGMOD 2005
    Amer-Yahia, S.; Case, P.; Rölleke, T.; Shanmugasundaram, J.; Weikum, G.
  • XML Search: languages, INEX and scoring
    Amer-Yahia, S.; Lalmas, M.

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