| 引用本文： |
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覃晓,廖兆琪,施宇,元昌安.知识图谱技术进展及展望[J].广西科学院学报,2020,36(3):242-251. [点击复制]
- QIN Xiao,LIAO Zhaoqi,SHI Yu,YUAN Chang'an.Progress and Prospect of Knowledge Graph Technology[J].Journal of Guangxi Academy of Sciences,2020,36(3):242-251. [点击复制]
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| 摘要: |
| 随着大数据的发展，知识图谱（Knowledge Graph）关键技术及其应用成为人工智能最热门的研究领域之一。本文从知识图谱的定义、架构以及常见的知识库出发，对知识图谱构建的知识表达和知识自动获取技术进行总结和回顾，讨论其研究要点和发展趋势，介绍知识图谱技术常见的应用场景，并结合本团队的研究对知识图谱的发展趋势进行展望。 |
| 关键词: 自然语言处理 知识图谱 知识表达 知识抽取 模型改进 |
| DOI：10.13657/j.cnki.gxkxyxb.20201027.009 |
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| 基金项目:国家自然基金项目（61962006）和广西创新驱动重大项目（AA18118047）资助。 |
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| Progress and Prospect of Knowledge Graph Technology |
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QIN Xiao1, LIAO Zhaoqi1, SHI Yu1, YUAN Chang'an2
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| (1.Nanning Normal University, Nanning, Guangxi, 530299, China;2.Guangxi Academy of Sciences, Nanning, Guangxi, 530007, China) |
| Abstract: |
| With the development of big data, the key technology and application research of knowledge graph have become one of the most popular research fields of artificial intelligence. Based on the definition,architecture and common knowledge base of knowledge graph, this article summarizes and reviews the knowledge representation and automatic knowledge acquisition technology constructed by knowledge graph,and discusses its research points and development trends.Then the common application scenarios of knowledge graph technology are introduced. Finally, combined with the research of the team, we discuss the trends of knowledge graph. |
| Key words: natural language processing knowledge graph knowledge representation knowledge extraction model improvement |
