A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
复制标题
知识图研究综述：表示、获取与应用
DOI：
10.1109/tnnls.2021.3070843
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发表时间：
2021-04-24
影响因子：
10.4
通讯作者：
Yu, Philip S.
中科院分区：
文献类型：
--
作者：
Ji, Shaoxiong;Pan, Shirui;Yu, Philip S.
人类知识提供了对世界的正式理解。代表实体之间结构关系的知识图已成为越来越流行的认知和人类智能的研究方向。在这项调查中，我们对知识图进行了全面的综述，涵盖了有关：1）知识图表示学习的总体研究主题； 2）知识获取和完成； 3）时间知识图； 4）知识感知的应用程序，并总结了最新的突破和透视方向，以促进未来的研究。我们建议对这些主题进行全景分类和新的分类法。知识图嵌入是从表示空间的四个方面，评分函数，编码模型和辅助信息组织的。对于知识获取，尤其是知识图完成，嵌入方法，路径推理和逻辑规则推理都将得到审查。我们进一步探讨了几个新兴主题，包括元合并学习，常识性推理和时间知识图。为了促进知识图的未来研究，我们还提供了有关不同任务的数据集和开源库的策划集合。最后，我们对几个有前途的研究方向有了详尽的看法。
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.