Keywords
hybrid search, extraction ontologies, dynamic ranking
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.
Original Publication Citation
"HyKSS: Hybrid Keyword and Semantic Search", Journal on Data Semantics, Springer
BYU ScholarsArchive Citation
Zitzelberger, Andrew J.; Embley, David W.; Liddle, Stephen W.; and Scott, Del T., "HyKSS: Hybrid Keyword and Semantic Search" (2014). Faculty Publications. 9499.
https://scholarsarchive.byu.edu/facpub/9499
Document Type
Peer-Reviewed Article
Publication Date
2014
Publisher
Journal on Data Semantics
Language
English
College
Marriott School of Business
Department
Information Systems Management
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