Xianchuan Wang, Xiao Gao, Zhenyuan Fu, Xiuming Chen, Xianchao Wang
{"title":"Design and Implementation of Event Knowledge Graph Construction Platform Based on Neo4j","authors":"Xianchuan Wang, Xiao Gao, Zhenyuan Fu, Xiuming Chen, Xianchao Wang","doi":"10.1109/ICDCECE57866.2023.10150587","DOIUrl":null,"url":null,"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.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCECE57866.2023.10150587","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.