A multi-entity reinforced main path analysis: Heterogeneous network embedding considering knowledge proximity

• Published in: Journal of informetrics Vol. 18; no. 4; p. 101593
• Main Authors: Yan, Zhaoping, Fan, Kaiyu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-11-2024
• Subjects: Knowledge graph embedding; Knowledge proximity; Main path analysis; Topic relevance; TransE; Knowledge graph embedding; Main path analysis; TransE; Knowledge proximity; Topic relevance
• ISSN: 1751-1577
• DOI: 10.1016/j.joi.2024.101593

•This paper combines the knowledge graph embedding with main path analysis.•A heterogeneous network consisting of authors, papers, journals and keywords is constructed.•The multi-entity reinforced main path analysis has stronger knowledge proximity.•Sentence BERT are adopted to measure the performance of the traditional and adjusted main path.
Main path analysis (MPA) is an important approach in detecting the trajectory of knowledge diffusion in a specific research domain. Previous studies always focus on citation-based relationships, overlooking other structural forms in citation network. This study introduces a multi-entity reinforced MPA model by constructing a knowledge graph from paper metadata, including citations, authors, journals, and keywords. We construct heterogeneous network to reveal relationships among various entities. Different knowledge graph embedding models are employed to train the network, thereby obtaining entity and relation embeddings. The cosine similarity algorithm is adopted to measure the knowledge proximity between these embeddings. We take the Internet of Thing domain as an example to verify the performance of the multi-entity reinforced MPA through both quantitative and qualitative analysis. Our findings indicate that the adjusted MPA exhibits stronger topic relevance, demonstrating the effectiveness of the method in capturing complex knowledge relationships.