论文标题
嵌入专利元数据的知识图以衡量知识接近性
Embedding Knowledge Graph of Patent Metadata to Measure Knowledge Proximity
论文作者
论文摘要
知识接近是指任何两个实体之间以结构形式体现知识基础某些方面的结合力量。在这项工作中，我们使用使用专利元数据构建的名为PATNET的知识图（结构形式）在美国专利数据库（知识库）的背景下运行知识接近，包括引用，发明家，受让人和域分类。我们使用PATNET训练各种图形嵌入模型，以获取实体和关系的嵌入。实体的相应（或转化）嵌入之间的余弦相似性表示这些知识接近。我们根据其在预测目标实体和解释发明人和受让人的域扩展概况方面的性能来比较嵌入模型。然后，我们将最佳偏爱模型的嵌入方式应用于均质（例如，专利权）和异质（例如，发明家 - 分配者）对实体的嵌入。
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.
