Journal on Data Semantics

, Volume 4, Issue 4, pp 213–229 | Cite as

HyKSS: Hybrid Keyword and Semantic Search

  • Andrew J. Zitzelberger
  • David W. Embley
  • Stephen W. Liddle
  • Del T. Scott
Original Article


Keyword search suffers from a number of issues: ambiguity, synonymy, and an inability to handle semantic constraints. Semantic search helps resolve these issues but is limited by the quality of annotations which are likely to be incomplete or imprecise. Hybrid search, a search technique that combines the merits of both keyword and semantic search, appears to be a promising solution. In this paper we describe and evaluate HyKSS, a hybrid search system driven by extraction ontologies for both annotation creation and query interpretation. For displaying results, HyKSS uses a dynamic ranking algorithm. We show that over data sets of short topical documents, the HyKSS ranking algorithm outperforms both keyword and semantic search in isolation, as well as a number of other non-HyKSS hybrid approaches to ranking.


Data Frame Mean Average Precision Keyword Query SPARQL Query Semantic Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Andrew J. Zitzelberger
    • 1
  • David W. Embley
    • 1
  • Stephen W. Liddle
    • 2
  • Del T. Scott
    • 3
  1. 1.Department of Computer ScienceBrigham Young UniversityProvoUSA
  2. 2.Information Systems DepartmentBrigham Young UniversityProvoUSA
  3. 3.Department of StatisticsBrigham Young UniversityProvoUSA

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