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- W4308244296 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." @default.
- W4308244296 created "2022-11-09" @default.
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- W4308244296 date "2022-11-03" @default.
- W4308244296 modified "2023-09-24" @default.
- W4308244296 title "Embedding Knowledge Graph of Patent Metadata to Measure Knowledge Proximity" @default.
- W4308244296 doi "https://doi.org/10.48550/arxiv.2211.01768" @default.
- W4308244296 hasPublicationYear "2022" @default.
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