LLM-empowered knowledge graph construction: A survey

2025-10-23

AI Summary

This paper addresses the limitations of traditional rule- and statistics-based approaches in knowledge graph (KG) construction—namely, ontology engineering, knowledge extraction, and knowledge fusion. It proposes a novel “language-driven generative KG construction” paradigm that unifies schema-based and schema-agnostic methods, establishing synergistic mechanisms between large language models (LLMs) and symbolic KGs for structured organization and open-ended semantic expression. The method integrates prompt engineering, knowledge representation learning, automated reasoning, and multimodal modeling to enable dynamic, interpretable knowledge acquisition and fusion. The study comprehensively surveys technical pathways and bottlenecks, identifying three key research directions: LLM reasoning enhancement via KGs, agent memory modeling with KGs, and multimodal KG construction. Ultimately, this work advances the development of adaptive, neuro-symbolic intelligent knowledge systems.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Knowledge Graphs (KGs) have long served as a fundamental infrastructure for structured knowledge representation and reasoning. With the advent of Large Language Models (LLMs), the construction of KGs has entered a new paradigm-shifting from rule-based and statistical pipelines to language-driven and generative frameworks. This survey provides a comprehensive overview of recent progress in LLM-empowered knowledge graph construction, systematically analyzing how LLMs reshape the classical three-layered pipeline of ontology engineering, knowledge extraction, and knowledge fusion. We first revisit traditional KG methodologies to establish conceptual foundations, and then review emerging LLM-driven approaches from two complementary perspectives: schema-based paradigms, which emphasize structure, normalization, and consistency; and schema-free paradigms, which highlight flexibility, adaptability, and open discovery. Across each stage, we synthesize representative frameworks, analyze their technical mechanisms, and identify their limitations. Finally, the survey outlines key trends and future research directions, including KG-based reasoning for LLMs, dynamic knowledge memory for agentic systems, and multimodal KG construction. Through this systematic review, we aim to clarify the evolving interplay between LLMs and knowledge graphs, bridging symbolic knowledge engineering and neural semantic understanding toward the development of adaptive, explainable, and intelligent knowledge systems.

Problem

• Surveying LLM-driven knowledge graph construction paradigms
• Analyzing LLM impact on ontology engineering and knowledge extraction
• Bridging symbolic knowledge engineering with neural semantic understanding

Innovation

• LLMs shift KG construction from rule-based to generative frameworks
• LLMs reshape ontology engineering, extraction, and fusion pipeline
• LLMs enable both schema-based and schema-free KG construction paradigms