CoRE: A context-aware relation extraction method for relation completion

Zhixu Li, Mohamed Abdel Fattah Sharaf, Laurianne Sitbon, Xiaoyong Du, Xiaofang Zhou

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


We 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.
Original languageEnglish (US)
Pages (from-to)836-849
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number4
StatePublished - Apr 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This 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].

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems
  • Computer Science Applications


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