STI18LDN-Research on Topic Recognition Based on Multilayer Relation Fusion.pdf (586.72 kB)
0/0

Research on Topic Recognition Based on Multilayer Relation Fusion

Download (586.72 kB)
poster
posted on 07.09.2018 by haiyun xu, Rui Luo, Chunjiang Liu, Kun Dong

In this poster, 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.

Categories

Keyword(s)

Funding

National Natural Science Foundation of China (71704170) ;China Postdoctoral Science Foundation Funded Project (2016M590124); Youth Innovation Promotion Association, CAS ( 2016159)

History

Licence

Exports

Categories

Keyword(s)

Licence

Exports