| Abstract
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Entity ranking is a very important step fo … Entity 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.
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| Abstractsub
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Entity ranking is a very important step fo … Entity 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.
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| Bibtextype
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article +
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| Doi
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10.7544/issn1000-1239.2014.20130017 +
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| Has author
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Jinghua Zhang +
, Qu Y. +
, Shui Y. +
, Tian S. +
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| Has extra keyword
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Hardware +
, Entity ranking +
, Entity-relation relevancy +
, Entity-type relevancy +
, Related entity findings +
, Wikipedia +
, Computer networks +
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| Has keyword
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Entity ranking +
, Entity-relation relevancy +
, Entity-type relevancy +
, Related entity finding +
, Wikipedia +
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| Issn
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10001239 +
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| Issue
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6 +
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| Language
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Chinese +
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| Number of citations by publication
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0 +
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| Number of references by publication
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0 +
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| Pages
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1359–1372 +
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| Published in
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Jisuanji Yanjiu yu Fazhan/Computer Research and Development +
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| Title
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Entity ranking based on Wikipedia for related entity finding +
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| Type
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journal article +
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| Volume
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51 +
|
| Year
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2014 +
|
| Creation dateThis property is a special property in this wiki.
|
6 November 2014 15:39:07 +
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| 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 +
|
