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dc.contributor.authorLi, Zhixu
dc.contributor.authorSharaf, Mohamed Abdel Fattah
dc.contributor.authorSitbon, Laurianne
dc.contributor.authorDu, Xiaoyong
dc.contributor.authorZhou, Xiaofang
dc.date.accessioned2015-08-03T11:52:25Z
dc.date.available2015-08-03T11:52:25Z
dc.date.issued2014-04
dc.identifier.issn10414347
dc.identifier.doi10.1109/TKDE.2013.148
dc.identifier.urihttp://hdl.handle.net/10754/563475
dc.description.abstractWe identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation {\cal R}, RC attempts at linking entity pairs between two entity lists under the relation {\cal R}. To accomplish the RC goals, we propose to formulate search queries for each query entity \alpha based on some auxiliary information, so that to detect its target entity \beta from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC. © 1989-2012 IEEE.
dc.description.sponsorshipThis research was partially supported by National 863 High-tech Program (Grant No. 2012AA011001) and the Australian Research Council (Grant No. DP120102829). Part of this work has appeared as a short paper in CIKM '11 [18].
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectContext-aware relation extraction
dc.subjectRelation completion
dc.subjectRelation query expansion
dc.titleCoRE: A context-aware relation extraction method for relation completion
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE Transactions on Knowledge and Data Engineering
dc.contributor.institutionSchool of Information Technology and Electrical Engineering, University of Queensland, Building 78, St Lucia Campus, Brisbane, QLD 4072, Australia
dc.contributor.institutionSchool of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia
dc.contributor.institutionMOE China, School of Information, Renmin University of China, Beijing 100872, China
dc.contributor.institutionSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane QLD 4072, Australia
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, China
kaust.personLi, Zhixu


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