基于Neo4j的语言学术语知识图谱构建研究

Graph Construction of Linguistic Term Knowledge Based on Neo4j

此研究以《语言学名词》为数据源,使用Neo4j图数据库,采用自顶向下的图谱构建模式,融合事件理论与事理知识图谱构建方法生成语言学术语知识图谱,直观展现语言学术语内部的五类属性值及术语节点之间的九类关系,提供了一种较为合理的学科知识图谱构建模式,对语言学术语知识图谱的部分特征进行了分析,并对语言学术语知识图谱研究进行了总结和展望。

Taking the Chinese Languistic Terms as data source and using the Neo4j graph database,we adopt a top-down graph construction model,and integrate event theory and event evolutionary graph construction methods to generate linguistic terminology knowledge graphs.We hope to visualize the five types of attribute values within linguistic terms and nine types of relationships among term nodes,and provide a more reasonable disciplinary knowledge graph construction model.We also analyze some features of the linguistic terminology knowledge graph,and summarize and outlook the research on linguistic terminology knowledge graph.