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dc.contributor.authorHuang, Hai
dc.contributor.authorChen, Zonghai
dc.contributor.authorLiu, Chengfei
dc.contributor.authorHuang, He
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2017-01-02T08:42:35Z
dc.date.available2017-01-02T08:42:35Z
dc.date.issued2016-09-09
dc.identifier.citationHuang 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.
dc.identifier.issn1386-145X
dc.identifier.issn1573-1413
dc.identifier.doi10.1007/s11280-016-0413-1
dc.identifier.urihttp://hdl.handle.net/10754/622171
dc.description.abstractThis 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
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007%2Fs11280-016-0413-1
dc.subjectQuery reformulation and keyword recommendation
dc.subjectQuery suggestion
dc.titleAn effective suggestion method for keyword search of databases
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalWorld Wide Web
dc.contributor.institutionDepartment of Automation, University of Science and Technology of China, Hefei, China
dc.contributor.institutionFaculty of ICT, Swinburne University of Technology, Melbourne Vic, Australia
dc.contributor.institutionInstitute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China
kaust.personZhang, Xiangliang
dc.date.published-online2016-09-09
dc.date.published-print2017-07


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