Research on Topic Recognition Based on Multivariate Relation Fusion

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Research on Topic Recognition Based on Multivariate Relation Fusion

Type: Article in monograph or in proceedings
Title: Research on Topic Recognition Based on Multivariate Relation Fusion
Author: Xu H.Dong K.Luo R.Wang C.
Journal Title: STI 2018 Conference Proceedings
Start Page: 378
End Page: 384
Publisher: Centre for Science and Technology Studies (CWTS)
Issue Date: 2018-09-11
Keywords: Scientometrics
Abstract: In this paper, we present a review of the current research status of multi-relational fusion and systematically summarize the multiple relationships among different measurement entities and entities in the scientific literature. Further, we propose a multi-relational extraction and relational fusion approach to thematic identification. We divide the relationships for topic recognition into three types—basic, enhancing, and supplement—that can be formed by integrating co-occurrence, citation, and co-authorship relationships. Finally, as an empirical analysis, we use the PathSelClus algorithm to realize topic clustering based on multivariate relation fusion. Empirical analysis confirms that multivariate relational fusion can effectively improve the effectiveness of topic clustering.
Handle: http://hdl.handle.net/1887/65357
 

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