Embedding Knowledge Graph of Patent Metadata to Measure Knowledge Proximity

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. Using several graph embedding models (e.g., TransE, RESCAL), we obtain the embeddings of entities and relations that constitute PatNet. The cosine similarity between the corresponding (or transformed) embeddings entities denotes the knowledge proximity between these. We evaluate the plausibility of these embeddings across different models in predicting target entities. We also evaluate the meaningfulness of knowledge proximity to explain the 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.