The State of the Art Large Language Models for Knowledge Graph Construction from Text: Techniques, Tools, and Challenges

Abstract

Knowledge graphs (KG) play a crucial role in many modern applications. Industrial knowledge is scattered in large volumes of both structured and unstructured data sources and bringing them to a unified knowledge graph can bring a lot of value. However, automatically constructing a KG from natural language text is challenging due the ambiguity and impreciseness of the natural languages. Recently, many approaches have been proposed to transform natural language text to triples to construct KGs. Out of those, approaches based on large language models (LLMs) predominantly lead in many subtasks related to knowledge graph construction such as entity and relation extraction. In this presentation, we will focus on the state of the art of -LLM-based methods, techniques and tools for constructing knowledge graphs from text, their capabilities, limitations and current challenges. It aims to summarize the research progress over the KG construction from text, specifically focusing on the information acquisition branch entailing entity and relation extraction covering the state-of-the-art transformer methods and tools. This will be useful for any practitioner who is interested in building knowledge graphs for their organizations.