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HyKSS: Hybrid Keyword and Semantic Search

  • Original Article
  • Published:
Journal on Data Semantics

Abstract

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.

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Notes

  1. In [2], we used HyKSS as our query system to explore cross-language query and search. Here, we focus on HyKSS itself, describing all its features and providing an in-depth statistical evaluation of its performance relative to alternative systems. Except for the necessary background about extraction ontologies and some parts of the running example and its figures, which serve to illustrate the ideas in HyKSS, this paper is new, describing HyKSS and its features in depth and providing a statistical evaluation substantiating our claimed contributions.

  2. http://www.craigslist.org.

  3. http://www.wikipedia.org.

  4. http://lucene.apache.org.

  5. http://deg.byu.edu (in the OntologyWorkbench tool).

  6. See the HyKSS demo at http://deg.byu.edu/demos.

  7. To see a sample of a generated form, enter some query like the running example query to provide context and then click on Advanced Search in the HyKSS demo (http://deg.byu.edu/demos).

  8. In general, logic form identification in natural language is difficult (e.g., see [8]). In our query here, for example, note that “and” denotes disjunction rather than its more common conjunctive denotation.

  9. http://www.openrdf.org.

  10. See http://lucene.apache.org/core/3_6_1/scoring.html for details.

  11. http://www.deg.byu.edu/demos/askontos.

  12. http://provo.craigslist.org.

  13. http://saltlakecity.craigslist.org.

  14. The document sets may have contained duplicate or similar advertisements due to cross posting and re-posting tendencies of craigslist users. We did not check for this, but rather just took the ads as posted.

  15. http://www.wikipedia.org.

  16. http://en.wikipedia.org/wiki/List_of_mountains.

  17. http://en.wikipedia.org/wiki/List_of_roller_coaster_rankings.

  18. We established the keyword and semantic weights as those that maximized mean average precision over the 50 validation documents. To find the keyword weight \(k\), we executed the 100 queries in the training query set using 101 weight combinations (0.0–1.0) and chose the best. The semantic weight \(s\) became \(1\)-k.

  19. See [13] for a full discussion of the principles of ranking in information retrieval.

  20. We began our analysis using a split plot design, but when we examined the interactions among our factors and found that the interactions were not major sources of response variability, we simplified to a block statistical model.

  21. In our experiment, the keyword results in the representative case turned out to be literally constant, but in general they need not be, and indeed the Keyword—Pre-processing approach (\(K_p\)) yielded results that were not quite constant.

  22. http://www.bing.com.

  23. January, 2012.

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Authors and Affiliations

  1. Department of Computer Science, Brigham Young University, Provo, UT, 84602, USA

    Andrew J. Zitzelberger & David W. Embley

  2. Information Systems Department, Brigham Young University, Provo, UT, 84602, USA

    Stephen W. Liddle

  3. Department of Statistics, Brigham Young University, Provo, UT, 84602, USA

    Del T. Scott

Authors
  1. Andrew J. Zitzelberger
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  2. David W. Embley
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  3. Stephen W. Liddle
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  4. Del T. Scott
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Correspondence to David W. Embley.

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Zitzelberger, A.J., Embley, D.W., Liddle, S.W. et al. HyKSS: Hybrid Keyword and Semantic Search. J Data Semant 4, 213–229 (2015). https://doi.org/10.1007/s13740-014-0046-4

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  • DOI: https://doi.org/10.1007/s13740-014-0046-4

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