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.
翻译:知识接近是指任何两个实体以体现知识库某些方面的结构形式建立联系的力度。在这项工作中,我们使用一个知识图(结构表),即使用专利元数据(包括引文、发明者、受让人和域分类)建造的PatNet(称为PatNet),利用PatNet(称为PatNet)培训各种图嵌入模型,以获得实体和关系的嵌入。在相应(或转变的)实体嵌入之间具有相似性,表明这些实体之间的知识接近性。我们比较了嵌入模型在预测目标实体和解释发明者和受让人领域扩展概况方面的性能。我们然后将最受青睐的模型嵌入式用于关联的同质(例如专利专利-专利)和异性(例如发明者-指派人)实体的配对。