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Scientific Programming
Volume 2017, Article ID 7613026, 12 pages
Research Article

Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases

College of Computer Science and Technology, Harbin Engineering University, Harbin, China

Correspondence should be addressed to Nianbin Wang; nc.ude.uebrh@nibnaingnaw

Received 15 January 2017; Revised 17 April 2017; Accepted 3 May 2017; Published 29 May 2017

Academic Editor: Michele Risi

Copyright © 2017 Yingqi Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Due to the ambiguity and impreciseness of keyword query in relational databases, the research on keyword query expansion has attracted wide attention. Existing query expansion methods expose users’ query intention to a certain extent, but most of them cannot balance the precision and recall. To address this problem, a novel two-step query expansion approach is proposed based on query recommendation and query interpretation. First, a probabilistic recommendation algorithm is put forward by constructing a term similarity matrix and Viterbi model. Second, by using the translation algorithm of triples and construction algorithm of query subgraphs, query keywords are translated to query subgraphs with structural and semantic information. Finally, experimental results on a real-world dataset demonstrate the effectiveness and rationality of the proposed method.