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Query relaxation for entity-relationship search

Elbassuoni, Shady and Ramanath, Maya and Weikum, Gerhard

MPI-I-2010-5-008. December 2010, 40 pages. | Status: available - back from printing | Next --> Entry | Previous <-- Entry

Abstract in LaTeX format:
Entity-relationship-structured data is becoming more important on the
Web. For example, large knowledge bases have been automatically constructed
by information extraction from Wikipedia and other Web sources.
Entities and relationships can be represented by subject-property-object
triples in the RDF model, and can then be precisely searched by structured
query languages like SPARQL. Because of their Boolean-match semantics,
such queries often return too few or even no results. To improve recall, it
is thus desirable to support users by automatically relaxing or reformulating
queries in such a way that the intention of the original user query is preserved
while returning a sufficient number of ranked results.
In this paper we describe comprehensive methods to relax SPARQL-like
triple-pattern queries, possibly augmented with keywords, in a fully automated
manner. Our framework produces a set of relaxations by means of
statistical language models for structured RDF data and queries. The query
processing algorithms merge the results of different relaxations into a unified
result list, with ranking based again on language models. Our experimental
evaluation, with two different datasets about movies and books, shows the
effectiveness of the automatically generated relaxations and the improved
quality of query results based on assessments collected on the Amazon Mechanical
Turk platform.
References to related material:

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  AUTHOR = {Elbassuoni, Shady and Ramanath, Maya and Weikum, Gerhard},
  TITLE = {Query relaxation for entity-relationship search},
  TYPE = {Research Report},
  INSTITUTION = {Max-Planck-Institut f{\"u}r Informatik},
  ADDRESS = {Stuhlsatzenhausweg 85, 66123 Saarbr{\"u}cken, Germany},
  NUMBER = {MPI-I-2010-5-008},
  MONTH = {December},
  YEAR = {2010},
  ISSN = {0946-011X},