论文标题
知识图的调查：表示，获取和应用
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
论文作者
论文摘要
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水平智力的研究方向。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）知识图表学习的总体研究主题，2）知识获取和完成，3）时间知识图，以及4）知识知识应用程序，并总结了最近的突破性和透视指导，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，尤其是知识图的完成，嵌入方法，路径推理和逻辑规则推理。我们进一步探讨了几个新兴主题，包括元关系学习，常识性推理和时间知识图。为了促进知识图的未来研究，我们还为不同任务提供了精心策划的数据集和开源库。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 towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of 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 meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.
