Embedding knowledge graph of patent metadata to measure knowledge proximity. (16th January 2023)

• Record Type: Journal Article
• Authors: Li, Guangtong; Siddharth, L.; Luo, Jianxi
• Is Part Of: Journal of the Association for Information Science and Technology. Volume 74:Number 4(2023)
• Page Start: 476
• Page End: 490
• Publication Date: 2023-01-16
• Subjects: Information science -- Periodicals; Information technology -- Periodicals; 020.5
• DOI: 10.1002/asi.24736
• Languages: English
• ISSNs: 2330-1635

Abstract

Abstract: Knowledge proximity refers to the strength of association between any two entities in a structural form that embodies certain aspects of a knowledge base. In this work, we operationalize knowledge proximity within the context of the US Patent Database (knowledge base) using a knowledge graph (structural form) named "PatNet" built using patent metadata, including citations, inventors, assignees, and domain classifications. We train various graph embedding models using PatNet to obtain the embeddings of entities and relations. The cosine similarity between the corresponding (or transformed) embeddings of entities denotes the knowledge proximity between these. We compare the embedding models in terms of their performances in predicting target entities and explaining domain expansion profiles of inventors and assignees. We then apply the embeddings of the best‐preferred model to associate homogeneous (e.g., patent–patent) and heterogeneous (e.g., inventor–assignee) pairs of entities.