A semantic main path analysis method to identify multiple developmental trajectories

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.

Automated summary

Main Path Analysis (MPA) is a method widely used to track the developmental trajectory of a technological field. However, traditional methods have limitations. To address these limitations, a new method called semantic MPA (sMPA) is proposed, which leverages semantic information to generate candidate paths and select main paths. Experimental results demonstrate that this method is capable of discovering more knowledge flows from important subfields and improving the coherence of paths.