HyKSS: Hybrid keyword and semantic search
Del Scott2011
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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. 1 Introduction Keyword search for documents on the web works well-often surprisingly well. Can semantic search, added to keyword search, make the search for relevant documents even better? Clearly, the answer should be yes, and researchers are pursuing this initiative (e.g., [1]). The real question, however, is not whether adding semantic search might help, but rather how can we, in a cost-effective way, identify the semantics both in documents in the search space and in the free-form queries users wish to ask. Keyword search has a number of limitations: (1) Polysemy: Ambiguous keywords may result in the retrieval of irrelevant documents. (2) Synonymy: Document publishers may use words that are synonymous with, but not identical to, terms in user queries causing relevant documents to be missed. (3) Constraint satisfaction: Keyword search is incapable of recognizing semantic constraints. If a query specifies "Hondas for under 12 grand", a keyword search will treat each word as a keyword (or stopword) despite the fact that many, if not most, relevant documents likely do not contain any of these words-not even "Hondas" since the plural is relatively rare in relevant documents. Semantic search can resolve polysemy by placing words in context, synonymy by allowing for alternatives, and constraint satisfaction by recognizing specified conditions. Thus, for example, semantic search can interpret the query "Hondas
