Design and Implementation of Event Knowledge Graph Construction Platform Based on Neo4j

Xianchuan Wang, Xiao Gao, Zhenyuan Fu, Xiuming Chen, Xianchao Wang
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Abstract

The traditional knowledge graph research field focuses on static knowledge such as entities and entity relationships. Events are dynamic and have the characteristics of actions, participants, and time and space. It is a coarse-grained way of knowledge representation. This paper first uses Python's xml.dom module to parse the marked corpus text in the Chinese emergency corpus CEC to obtain event semantic information, and then realizes the complete mapping of event semantic information to Neo4j graph database to store knowledge graphs, and finally designs and implements events A knowledge graph platform, which can construct the event knowledge in the database into an event knowledge graph, and complete the basic functions of adding, deleting, modifying, and checking event nodes and event relationships. The research in this paper can provide favorable support for event-oriented knowledge processing applications.
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基于Neo4j的事件知识图谱构建平台的设计与实现
传统的知识图谱研究领域侧重于实体、实体关系等静态知识。事件是动态的,具有行动、参与者、时间和空间的特征。它是一种粗粒度的知识表示方式。本文首先使用Python的xml。dom模块解析中文应急语料库CEC中标记的语料库文本,获取事件语义信息,然后实现事件语义信息到Neo4j图形数据库的完整映射,存储知识图谱,最后设计并实现了事件知识图谱平台,该平台可以将数据库中的事件知识构建为事件知识图谱,并完成添加、删除、修改、检查事件节点和事件关系。本文的研究为面向事件的知识处理应用提供了良好的支持。
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