Embedding knowledge graph of patent metadata to measure knowledge proximity

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)476-490
Journal / PublicationJournal of the Association for Information Science and Technology
Volume74
Issue number4
Online published16 Jan 2023
Publication statusPublished - Apr 2023
Externally publishedYes

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. © 2023 Association for Information Science and Technology.