A semantic main path analysis method to identify multiple developmental trajectories

Liang Chen

doi:10.1016/j.joi.2022.101281

A semantic main path analysis method to identify multiple developmental trajectories

2022-05-01

13

PRE

AI

DOI：10.1016/j.joi.2022.101281

Original

Original request for help

Save

Abstract

En 中文

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 cherrypick 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 subfields, and improving the topical coherence of candidate paths as well.

Keywords:

Main path analysis

Developmental trajectory

Patent mining

Topic coherence

Lithium-ion battery

A semantic main path analysis method to identify multiple developmental trajectories

Abstract

Journal

Organization