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Top-k Relevant Term Suggestion Approach for Relational Keyword Search

Top-k Relevant Term Suggestion Approach for Relational Keyword Search

Xiangfu Meng, Xiaoyan Zhang, Chongchun Bi
Copyright: © 2016 |Pages: 24
ISBN13: 9781466687677|ISBN10: 1466687673|EISBN13: 9781466687684
DOI: 10.4018/978-1-4666-8767-7.ch001
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MLA

Meng, Xiangfu, et al. "Top-k Relevant Term Suggestion Approach for Relational Keyword Search." Handbook of Research on Innovative Database Query Processing Techniques, edited by Li Yan, IGI Global, 2016, pp. 1-24. https://doi.org/10.4018/978-1-4666-8767-7.ch001

APA

Meng, X., Zhang, X., & Bi, C. (2016). Top-k Relevant Term Suggestion Approach for Relational Keyword Search. In L. Yan (Ed.), Handbook of Research on Innovative Database Query Processing Techniques (pp. 1-24). IGI Global. https://doi.org/10.4018/978-1-4666-8767-7.ch001

Chicago

Meng, Xiangfu, Xiaoyan Zhang, and Chongchun Bi. "Top-k Relevant Term Suggestion Approach for Relational Keyword Search." In Handbook of Research on Innovative Database Query Processing Techniques, edited by Li Yan, 1-24. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-8767-7.ch001

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Abstract

This chapter proposes a novel approach, which can provide a list of keywords that both semantically related to the application domain and the given keywords by analyzing the correlations between query keywords and database terms. The database term is first modeled as and suppose each query keyword can map into a database term. Then, a coupling relationship measuring method is proposed to measure both term intra- and inter-couplings, which can reflect the explicit and implicit relationships between terms in the database. Based on the coupling relationships between terms, for a given keyword query, an order of all terms in database is created for each query keyword and then the threshold algorithm (TA) is leveraged to expeditiously generate top-k ranked semantically related terms. The experiments demonstrate that our term coupling relationship measuring method can efficiently capture the semantic correlations between query keywords and terms in database.

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