嵌入式静态知识图谱补全研究进展

Advancements in Embedded Static Knowledge Graph Completion Research

知识图谱是一种应用广泛且语义丰富的数据表示形式,日益成为知识工程领域的重要技术。但是由于现实世界中的知识图谱往往存在不完整和含糊的信息,阻碍了知识图谱应用性能。知识图谱补全技术旨在通过预测缺失的实体或关系来丰富知识图谱的内容,是近年来研究的热点,特别是在知识图谱补全任务中采用嵌入式方法取得了显著进展。回顾近年来嵌入式静态知识图谱补全方法,从空间平移、张量分解、神经网络模型、预训练语言模型等角度开展分类探讨。这些方法通过将实体关系嵌入到连续向量空间中,实现了更好的语义表示和推理能力;同时,在捕捉实体间复杂关系、利用图结构信息等方面具有潜在优势。

Knowledge graphs are widely used and semantically rich data representations,which is increasingly becoming a crucial technology in the field of knowledge engineering.However,real-world knowledge graphs often suffer from incompleteness and ambiguity,hindering their application performance.Knowledge graph completion techniques aim to enrich the content of knowledge graphs by predicting missing entities or relations,has been a hot research topic in recent years.In particular,embedding-based approaches have made remarkable progress in knowledge graph completion tasks.It reviews recent embedding-based static knowledge graph completion methods,categorizing them based on approaches such as translation-based models,tensor factorization,neural network models,and pre-trained language models.These methods achieve an improved semantic representation and inferential capabilities by embedding entity relations into continuous vector spaces.At the same time,it has potential advantages in capturing complex relationships between entities and utilizing graph structural information.