文章摘要
漆桂林,高桓,吴天星.知识图谱研究进展[J].情报工程,2017,3(1):004-025
知识图谱研究进展
The Research Advances of Knowledge Graph
  
DOI:10.3772/j.issn.2095-915x.2017.01.002
中文关键词: 人工智能,知识图谱,知识挖掘,情报分析
英文关键词: Artificial Intelligence, knowledge graph, knowledge mining, intelligence analysis
基金项目:国家自然科学基金面上项目:基于图的并行OWL本体推理方法研究(61672153)
作者单位
漆桂林 东南大学计算机科学与工程学院 
高桓 东南大学计算机科学与工程学院 
吴天星 东南大学计算机科学与工程学院 
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中文摘要:
      随着大数据时代的到来,知识工程受到了广泛关注,如何从海量的数据中提取有用的知识,是大数据分析的关键。知识图谱技术提供了一种从海量文本和图像中抽取结构化知识的手段,从而具有广阔的应用前景。本文首先简要回顾知识图谱的历史,探讨知识图谱研究的意义。其次,介绍知识图谱构建的关键技术,包括实体关系识别技术、知识融合技术、实体链接技术和知识推理技术等。然后,给出现有开放的知识图谱数据集的介绍。最后,给出知识图谱在情报分析中的应用案例。
英文摘要:
      With the advent of big data era, knowledge engineering has attracted wide attention, as mining knowledge from large-scale data is critical for big data analysis. Knowledge graph techniques provide a way to extract structured knowledge from large-scale texts and images, thus have wide application prospect. In this article, we first gave a brief overview of the history of knowledge graph, and discussed the importance of knowledge graph research. We then introduced key technologies of knowledge graph, including techniques of instance relation detection, techniques of knowledge fusion, techniques of instance mapping, and techniques of knowledge reasoning. After that, we introduced some well-known open knowledge graph datasets. Finally, we presented some use cases of knowledge graph in intelligence analysis.
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