A query refinement framework for xml keyword search

Bao, Z, Yu, Y, Shen, J and Fu, Z 2017, 'A query refinement framework for xml keyword search', World Wide Web, vol. 20, no. 6, pp. 1469-1505.

Document type: Journal Article
Collection: Journal Articles

Title A query refinement framework for xml keyword search
Author(s) Bao, Z
Yu, Y
Shen, J
Fu, Z
Year 2017
Journal name World Wide Web
Volume number 20
Issue number 6
Start page 1469
End page 1505
Total pages 37
Publisher Springer New York LLC
Abstract Existing work of XML keyword search focus on how to find relevant and meaningful data fragments for a query, assuming each keyword is intended as part of it. However, in XML keyword search, user queries usually contain irrelevant or mismatched terms, typos etc, which may easily lead to empty or meaningless results. In this paper, we introduce the problem of content-aware XML keyword query refinement, where the search engine should judiciously decide whether a user query Q needs to be refined during the processing of Q, and find a list of promising refined query candidates which guarantee to have meaningful matching results over the XML data, without any user interaction or a second try. To achieve this goal, we build a novel content-aware XML keyword query refinement framework consisting of two core parts: (1) we build a query ranking model to evaluate the quality of a refined query RQ, which captures the morphological/semantical similarity between Q and RQ and the dependency of keywords of RQ over the XML data; (2) we integrate the exploration of RQ candidates and the generation of their matching results as a single problem, which is fulfilled within a one-time scan of the related keyword inverted lists optimally. Finally, an extensive empirical study verifies the efficiency and effectiveness of our framework.
Subject Database Management
Global Information Systems
Information Retrieval and Web Search
Keyword(s) Keyword search
Query refinement
DOI - identifier 10.1007/s11280-017-0447-z
ISSN 1386-145X
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Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
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