An effective suggestion method for keyword search of databases

Type
Article

Authors
Huang, Hai
Chen, Zonghai
Liu, Chengfei
Huang, He
Zhang, Xiangliang

KAUST Department
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Online Publication Date
2016-09-09

Print Publication Date
2017-07

Date
2016-09-09

Abstract
This paper solves the problem of providing high-quality suggestions for user keyword queries over databases. With the assumption that the returned suggestions are independent, existing query suggestion methods over databases score candidate suggestions individually and return the top-k best of them. However, the top-k suggestions have high redundancy with respect to the topics. To provide informative suggestions, the returned k suggestions are expected to be diverse, i.e., maximizing the relevance to the user query and the diversity with respect to topics that the user might be interested in simultaneously. In this paper, an objective function considering both factors is defined for evaluating a suggestion set. We show that maximizing the objective function is a submodular function maximization problem subject to n matroid constraints, which is an NP-hard problem. An greedy approximate algorithm with an approximation ratio O((Formula presented.)) is also proposed. Experimental results show that our suggestion outperforms other methods on providing relevant and diverse suggestions. © 2016 Springer Science+Business Media New York

Citation
Huang H, Chen Z, Liu C, Huang H, Zhang X (2016) An effective suggestion method for keyword search of databases. World Wide Web. Available: http://dx.doi.org/10.1007/s11280-016-0413-1.

Publisher
Springer Nature

Journal
World Wide Web

DOI
10.1007/s11280-016-0413-1

Additional Links
http://link.springer.com/article/10.1007%2Fs11280-016-0413-1

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