Large Language Models for Knowledge Graph Embedding: A Survey

TL;DR

This survey reviews how large language models are increasingly used to enhance knowledge graph embedding tasks, highlighting different approaches, scenarios, and future research directions.

Contribution

It provides a comprehensive classification of LLM-based KGE approaches and discusses their applications and potential future developments.

Findings

LLMs improve knowledge graph embedding performance.

Various approaches are categorized based on KGE scenarios.

Future directions include multi-modal and open KGE applications.

Abstract

Large language models (LLMs) have garnered significant attention for their superior performance in many knowledge-driven applications on the world wide web.These models are designed to train hundreds of millions or more parameters on large amounts of text data, enabling them to understand and generate naturallanguage effectively. 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, enablingthe triples in the knowledge graph to satisfy a specific scoring function in thevector space. However, based on the powerful language understanding and seman-tic modeling capabilities of LLMs, that have recently been invoked to varying degrees in different types of KGE related scenarios such as multi-modal KGE andopen KGE according to their task characteristics. In this paper, we investigate awide 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. Finally, we discuss the applications in which the methods are mainly used and suggest several forward-looking directions for the development of this new research area.