Large Language Models for Knowledge Graph Embedding: A Survey

Large language models (LLMs) have attracted a lot of attention in various fields due to their superior performance, aiming to train hundreds of millions or more parameters on large amounts of text data to understand and generate natural language. As the superior performance of LLMs becomes apparent, they are increasingly being applied to knowledge graph embedding (KGE)-related tasks to improve the processing results. Traditional KGE representation learning methods map entities and relations into a low-dimensional vector space, enabling the triples in the knowledge graph to satisfy a specific scoring function in the vector space. However, based on the powerful language understanding and semantic modeling capabilities of LLMs, which have recently been invoked to varying degrees in different types of KGE-related scenarios such as multi-modal KGE and open KGE according to their task characteristics, researchers are increasingly exploring how to integrate LLMs to enhance knowledge representation, improve generalization to unseen entities or relations, and support reasoning beyond static graph structures. In this paper, we investigate a wide range of approaches for performing LLMs-related tasks in different types of KGE scenarios. To better compare the various approaches, we summarize each KGE scenario in a classification. In the article we also discuss the applications in which the methods are mainly used and suggest several forward-looking directions for the development of this new research area.