technology intelligence engineering 004 2017 年第 3 卷 第1期 知识图谱研究进展 The Research Advances of Knowledge Graph 东南大学计算机科学与工程学院 南京 211189 School of Computer Science and Engineering, Southeast University, Nanjing 211189, China 漆桂林 高桓 吴天星 QI GuiLin GAO Huan WU TianXing 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。 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 knowledg
