Search (1 results, page 1 of 1)

  • × author_ss:"Li, G."
  • × author_ss:"Luo, J."
  • × year_i:[2020 TO 2030}
  1. Li, G.; Siddharth, L.; Luo, J.: Embedding knowledge graph of patent metadata to measure knowledge proximity (2023) 0.02 
    0.022779368 = product of:
  0.045558736 = sum of:
    0.045558736 = sum of:
      0.008118451 = weight(_text_:a in 920) [ClassicSimilarity], result of:
        0.008118451 = score(doc=920,freq=8.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.15287387 = fieldWeight in 920, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046875 = fieldNorm(doc=920)
      0.037440285 = weight(_text_:22 in 920) [ClassicSimilarity], result of:
        0.037440285 = score(doc=920,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = queryNorm
          0.23214069 = fieldWeight in 920, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=920)
  0.5 = coord(1/2)
    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.
    Date
    22. 3.2023 12:06:55
    Type
    a