Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (3): 15-33.DOI: 10.3778/j.issn.1002-8331.2106-0351

• Research Hotspots and Reviews • Previous Articles     Next Articles

Summary of Application and Prospect Analysis of Knowledge Graphs in Marine Field

XIONG Zhongmin, MA Haiyu, LI Shuai, ZHANG Na   

  1. 1.College of Information Technology, Shanghai Ocean University, Shanghai 201306, China 
    2.School of Tourism and Urban and Rural Planning, Chengdu University of Technology, Chengdu 610059, China
  • Online:2022-02-01 Published:2022-01-28



  1. 1.上海海洋大学 信息学院,上海 201306
    2.成都理工大学 旅游与城乡规划学院,成都 610059

Abstract: The knowledge graph is mainly used to extract essential information from complex data to generate a relational network. Its excellent recognition ability for complex relations and strong description ability for data make knowledge graph technology have high application value. This article provides theoretical support for applying knowledge graphs in the marine field and gives a general overview of the related technologies of knowledge graphs. It explains the excellent application of Citespace’s document analysis tools. It systematically sorts out semi-structured and unstructured data extraction techniques in the marine field and analyzes the principle and subsequent improvement of critical technologies such as named entity recognition, relation extraction, event extraction, knowledge fusion, and knowledge reasoning. The landing scene and prospects of applying knowledge graph technology in the marine field are summarized and prospected.

Key words: knowledge graph, named entity recognition, relation extraction, event extraction, knowledge fusion, Citespace

摘要: 知识图谱主要用于从复杂数据中抽取出关键信息以生成关系网络,其对于复杂关系出色的识别能力以及对于数据较强的描述能力使得知识图谱技术具有很高的应用价值。为给知识图谱在海洋领域的应用提供理论支撑,对知识图谱相关技术进行了总体概述。阐述Citespace文献分析工具的出色应用,针对海洋领域半结构化和非结构化数据抽取技术进行了系统整理,并分析了诸如命名实体识别、关系抽取、事件抽取、知识融合以及知识推理等关键性技术的原理及后续改进,对海洋领域应用知识图谱技术的落地场景及未来前景进行总结与展望。

关键词: 知识图谱, 命名实体识别, 关系抽取, 事件抽取, 知识融合, Citespace