知识图谱嵌入技术研究综述

知识图谱(KG)是一种用图模型来描述知识和建模事物之间关联关系的技术.知识图谱嵌入(KGE)作为一种被广泛采用的知识表示方法,其主要思想是将知识图谱中的实体和关系嵌入到连续的向量空间中,用来简化操作,同时保留KG的固有结构.可以使得多种下游任务受益,例如KG补全和关系提取等.首先对现有的知识图谱嵌入技术进行全面回顾,不仅包括使用KG中观察到的事实进行嵌入的技术,还包括添加时间维度的动态KG嵌入方法,以及融合多源信息的KG嵌入技术.对相关模型从实体嵌入、关系嵌入、评分函数等方面进行分析、对比与总结.然后简要介绍KG嵌入技术在下游任务中的典型应用,包括问答系统、推荐系统和关系提取等.最后阐述知识图谱嵌入面临的挑战,对未来的研究方向进行展望.

Overview on Knowledge Graph Embedding Technology Research

Knowledge graph (KG) is a kind of technology that uses graph model to describe the relationship between knowledge and modeling things. Knowledge graph embedding (KGE), as a widely adopted knowledge representation method, its main idea is to embed entities and relationships in a knowledge graph into a continuous vector space, which is used to simplify operations while preserving the intrinsic structure of the KG. It can benefit a variety of downstream tasks, such as KG completion, relation extraction, etc. Firstly, the existing knowledge graph embedding technologies are comprehensively reviewed, including not only techniques using the facts observed in KG for embedding, but also dynamic KG embedding methods that add time dimensions, as well as KG embedding technologies that integrate multi-source information. The relevant models are analyzed, compared and summarized from the perspectives of entity embedding, relation embedding and scoring functions. Then, typical applications of KG embedding technologies in downstream tasks are briefly introduced, including question answering systems, recommendation systems and relationship extraction. Finally, the challenges of knowledge graph embedding are expounded, and the future research directions are prospected.