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Entity ranking based on Wikipedia for related entity finding
Abstract Entity ranking is a very important step foEntity ranking is a very important step for related entity finding (REF). Although researchers have done many works about "entity ranking based on Wikipedia for REF", there still exists some issues: the semi-automatic acquirement of target-type, the coarse-grained target-type, the binary judgment of entity-type relevancy and ignoring the effects of stop words in calculation of entity-relation relevancy. This paper designs a framework, which ranks entities through the calculation of a triple-combination (including entity relevancy, entity-type relevancy and entity-relation relevancy) and acquires the best combination-method through the comparisons of experimental results. A novel approach is proposed to calculate the entity-type relevancy. It can automatically acquire the fine-grained target-type and the discriminative rules of its hyponym Wikipedia-categories through inductive learning, and calculate entity-type relevancy through counting the number of categories which meet the discriminative rules. Also, this paper proposes a "cut stop words to rebuild relation" approach to calculate the entity-relation relevancy between candidate entity and source entity. Experiment results demonstrate that the proposed approaches can effectively improve the entity-ranking results and reduce the time consumed in calculating.d reduce the time consumed in calculating.
Abstractsub Entity ranking is a very important step foEntity ranking is a very important step for related entity finding (REF). Although researchers have done many works about "entity ranking based on Wikipedia for REF", there still exists some issues: the semi-automatic acquirement of target-type, the coarse-grained target-type, the binary judgment of entity-type relevancy and ignoring the effects of stop words in calculation of entity-relation relevancy. This paper designs a framework, which ranks entities through the calculation of a triple-combination (including entity relevancy, entity-type relevancy and entity-relation relevancy) and acquires the best combination-method through the comparisons of experimental results. A novel approach is proposed to calculate the entity-type relevancy. It can automatically acquire the fine-grained target-type and the discriminative rules of its hyponym Wikipedia-categories through inductive learning, and calculate entity-type relevancy through counting the number of categories which meet the discriminative rules. Also, this paper proposes a "cut stop words to rebuild relation" approach to calculate the entity-relation relevancy between candidate entity and source entity. Experiment results demonstrate that the proposed approaches can effectively improve the entity-ranking results and reduce the time consumed in calculating.d reduce the time consumed in calculating.
Bibtextype article  +
Doi 10.7544/issn1000-1239.2014.20130017  +
Has author Jinghua Zhang + , Qu Y. + , Shui Y. + , Tian S. +
Has extra keyword Hardware + , Entity ranking + , Entity-relation relevancy + , Entity-type relevancy + , Related entity findings + , Wikipedia + , Computer networks +
Has keyword Entity ranking + , Entity-relation relevancy + , Entity-type relevancy + , Related entity finding + , Wikipedia +
Issn 10001239  +
Issue 6  +
Language Chinese +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 1359–1372  +
Published in Jisuanji Yanjiu yu Fazhan/Computer Research and Development +
Title Entity ranking based on Wikipedia for related entity finding +
Type journal article  +
Volume 51  +
Year 2014 +
Creation dateThis property is a special property in this wiki. 6 November 2014 15:39:07  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 15:39:07  +
DateThis property is a special property in this wiki. 2014  +
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