A comprehensive overview of knowledge graph completion

摘要

Knowledge Graph (KG) provides high-quality structured knowledge for various downstream knowledge-aware tasks (such as recommendation and intelligent question-answering) with its unique advantages of representing and managing massive knowledge. The quality and completeness of KGs largely determine the effectiveness of the downstream tasks. But in view of the incomplete characteristics of KGs, there is still a large amount of valuable knowledge is missing from the KGs. Therefore, it is necessary to improve the existing KGs to supplement the missed knowledge. Knowledge Graph Completion (KGC) is one of the popular technologies for knowledge supplement. Accordingly, there has a growing concern over the KGC technologies. Recently, there have been lots of studies focusing on the KGC field. To investigate and serve as a helpful resource for researchers to grasp the main ideas and results of KGC studies, and further highlight ongoing research in KGC, in this paper, we provide a all-round up-to-date overview of the current state-of-the-art in KGC.According to the information sources used in KGC methods, we divide the existing KGC methods into two main categories: the KGC methods relying on structural information and the KGC methods using other additional information. Further, each category is subdivided into different granularity for summarizing and comparing them. Besides, the other KGC methods for KGs of special fields (including temporal KGC, commonsense KGC, and hyper-relational KGC) are also introduced. In particular, we discuss comparisons and analyses for each category in our overview. Finally, some discussions and directions for future research are provided.