A semantic main path analysis method to identify multiple developmental trajectories

Liang Chen, Shuo Xu, Lijun Zhu, Jing Zhang, Haiyun Xu and Guancan Yang

Journal of Informetrics, 2022, vol. 16, issue 2

Abstract: Main Path Analysis (MPA) is widely used to trace the developmental trajectory of a technological field through a citation network. The citation-based traversal weight is usually utilized to cherry-pick the most significant path. However, the theme of documents along a main path may not be so coherent, and it is very possible to miss the main paths of significant sub-fields overall in a domain. Furthermore, the global path search algorithm in conventional MPA also suffers from high space complexity due to the exhaustive strategy. To address these limitations, a new method, named as semantic MPA (sMPA), is proposed by leveraging semantic information in two steps of candidate path generation and main path selection. In the meanwhile, the resulting source code can be freely accessed. To demonstrate the advantages of our method, extensive experiments are conducted on a patent dataset pertaining to lithium-ion battery in electric vehicle. Experimental results show that our sMPA is capable of discovering more knowledge flows from important sub-fields, and improving the topical coherence of candidate paths as well.

Keywords: Main path analysis; Developmental trajectory; Patent mining; Topic coherence; Lithium-ion battery (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:16:y:2022:i:2:s1751157722000335

DOI: 10.1016/j.joi.2022.101281

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