Co-word analysis method based on meta-path of subject knowledge network

Xiang Zhu and Yunqiu Zhang ()
Additional contact information
Xiang Zhu: Jilin University
Yunqiu Zhang: Jilin University

Scientometrics, 2020, vol. 123, issue 2, No 9, 753-766

Abstract: Abstract We propose a method of co-word analysis based on the subject knowledge network meta-path to overcome limitations with the current co-word analysis method. First, we construct a subject knowledge network to find the word-to-word meta-path. Second, we use the HeteSim algorithm to calculate the semantic relevance between words based on each meta-path. Then, through matrix operations, standardization, and matrix fusion, we construct a word-to-word semantic relevance matrix (WSRM). We conduct an empirical evaluation to test the proposed method. The results indicate that the WSRM formed by this method is superior to the word-to-word similarity matrix used in traditional co-word analysis in terms of both macro-evaluation indicators (viz., network density, network centralization, network average degree, and cohesive subgroups) and micro-evaluation indicators (viz., core-periphery class, point centrality, and cluster analysis). The method overcomes limitations to the traditional co-word analysis method, and combines multiple semantic relations between words, to reflect the relationship between words more realistically.

Keywords: Co-word analysis; Subject knowledge network; Meta-path; Semantic relevance; Word-to-word semantic relevance matrix (WSRM) (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11192-020-03400-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:123:y:2020:i:2:d:10.1007_s11192-020-03400-0

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-020-03400-0

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().