A semantic main path analysis method to identify multiple developmental trajectories. Issue 2 (May 2022)

• Record Type: Journal Article
• Authors: Chen, Liang; Xu, Shuo; Zhu, Lijun; Zhang, Jing; Xu, Haiyun; Yang, Guancan
• Is Part Of: Journal of informetrics. Volume 16:Issue 2(2022)
• Publication Date: 2022-05
• Subjects: Main path analysis -- Developmental trajectory -- Patent mining -- Topic coherence -- Lithium-ion battery; Library statistics -- Periodicals; Information science -- Statistical methods -- Periodicals; Bibliometrics -- Periodicals; Bibliothèques -- Statistiques -- Périodiques; Sciences de l'information -- Méthodes statistiques -- Périodiques; Bibliométrie -- Périodiques; 020.727
• DOI: 10.1016/j.joi.2022.101281
• Languages: English
• ISSNs: 1751-1577

Abstract

Highlights: A semantic main path analysis approach is put forward to identify simultaneously multiple developmental trajectories in a target field. To improve topical coherence of documents along a same trajectory, the conventional link weights are armed with the semantic information based ones. After all paths are enumerated effectively with a dynamic programming based search algorithm, a density-based clustering method is used to divide them into several groups. The source codes in Python language can be freely accessed at the GitHub and PyPi with detailed API documentation, thus to promote the related studies. 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.