Knowledge Graph Embedding: A Survey of Approaches and Applications
Knowledge Graph Embedding: A Survey of Approaches and Applications
复制标题
知识图嵌入：方法和应用综述
DOI：
10.1109/tkde.2017.2754499
复制
发表时间：
2017-12-01
影响因子：
8.9
通讯作者：
Guo, Li
中科院分区：
文献类型：
--
作者：
Wang, Quan;Mao, Zhendong;Guo, Li
知识图嵌入是将知识图中的实体和关系嵌入到连续的向量空间中，在保持知识图固有结构的同时简化操作。它可以使各种下游任务受益，例如KG完成和关系提取，因此迅速获得了广泛的关注。在本文中，我们对现有技术进行了系统回顾，不仅包括最新技术，还包括最新趋势。特别地，我们根据嵌入任务中使用的信息类型进行审查。首先介绍仅使用KG中观察到的事实进行嵌入的技术。我们描述了总体框架、具体的模型设计、典型的训练过程以及这些技术的优缺点。在此之后，我们讨论的技术，进一步纳入额外的信息，除了事实。我们特别关注实体类型、关系路径、文本描述和逻辑规则的使用。最后，我们简要介绍了KG嵌入如何应用于各种下游任务，如KG完成，关系提取，问答等。
Knowledge graph (KG) embedding is to embed components of a KG including entities and relations into continuous vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. It can benefit a variety of downstream tasks such as KG completion and relation extraction, and hence has quickly gained massive attention. In this article, we provide a systematic review of existing techniques, including not only the state-of-the-arts but also those with latest trends. Particularly, we make the review based on the type of information used in the embedding task. Techniques that conduct embedding using only facts observed in the KG are first introduced. We describe the overall framework, specific model design, typical training procedures, as well as pros and cons of such techniques. After that, we discuss techniques that further incorporate additional information besides facts. We focus specifically on the use of entity types, relation paths, textual descriptions, and logical rules. Finally, we briefly introduce how KG embedding can be applied to and benefit a wide variety of downstream tasks such as KG completion, relation extraction, question answering, and so forth.