关键词: 时序知识图谱, 知识抽取, 时间信息抽取, 知识融合, 知识推理

Abstract: As a bridge connecting data, knowledge, and intelligence, knowledge graph has been widely applied in such as search assistance, intelligent recommendation, question-answering systems, and natural language processing. However, with the expansion of application scenarios, static knowledge graph gradually has shown limitations in handling dynamic knowledge. The emergence of temporal knowledge graph addresses this shortcoming by integrating temporal information into the graph structure, enabling a more accurate representation of the dynamic changes in knowledge. This paper provides a comprehensive study on the construction of temporal knowledge graph. It begins by introducing the concept of temporal knowledge graph and clarifying its value in handling dynamic knowledge. It then delves into the construction process of temporal knowledge graph, dividing the core process into three key stages: knowledge extraction, knowledge fusion, and knowledge computing. Subsequently, we have thoroughly organized each stage, each stage is detailed with task definitions, research summaries, and the application of large language models. In the knowledge extraction stage, we focus on named entity recognition, relation extraction, and time information extraction; in the fusion stage, entity alignment and entity linking are discussed; and in the computation phase, we emphasis on knowledge reasoning. Finally, we explore the challenges faced at each stage and look forward to future research directions, providing insights for enhancing temporal knowledge graph quality.

Key words: temporal knowledge graph, knowledge extraction, temporal information extraction, knowledge fusion, knowledge reasoning