A Comprehensive Review of Relation Prediction Techniques in Knowledge Graph

Yuxuan Lu, Shiyu Yang, Benzhao Tang

Abstract: Knowledge graphs organize entity relations using a graph structure, facilitating knowledge representation. In research, relation prediction within knowledge graphs plays a crucial role, aiding inference, latent knowledge discovery, and revealing intricate associations between entities. We present an overview of this field’s development and methods. Initially, we introduce fundamental concepts, relation prediction task definitions, and evaluation metrics. Subsequently, we delve into research, spanning rule-based, statistical, and modern approaches like representation learning, deep learning and large language models. We explore transductive and inductive learning modes, discussing their relevance in relation prediction, and classify and summarize these methods. Additionally, we evaluate method strengths, weaknesses, and suitable scenarios, providing insights. Finally, we address future research directions and challenges in knowledge graph relation prediction, offering guidance for further study and practical applications.