# LLM-empowered knowledge graph construction: A survey

arXiv: https://arxiv.org/abs/2510.20345 · HTML: https://mlsanity.com/paper/2510.20345/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 50 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 30 of 31)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Zep: A Temporal Knowledge Graph Architecture for Agent Memory](https://mlsanity.com/paper/2501.13956/) (2025)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [A Comprehensive Survey on Automatic Knowledge Graph Construction](https://mlsanity.com/paper/2302.05019/) (2023)
- [KGGen: Extracting Knowledge Graphs from Plain Text with Language Models](https://mlsanity.com/paper/2502.09956/) (2025)
- [Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching](https://mlsanity.com/paper/2405.16884/) (2024)
- [KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment](https://mlsanity.com/paper/2502.06472/) (2025)
- [Ontology Generation using Large Language Models](https://mlsanity.com/paper/2503.05388/) (2025)
- [AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora](https://mlsanity.com/paper/2505.23628/) (2025)
- [Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning](https://mlsanity.com/paper/2503.12972/) (2025)
- [Ontology engineering with Large Language Models](https://mlsanity.com/paper/2307.16699/) (2023)
- [AutoRE: Document-Level Relation Extraction with Large Language Models](https://mlsanity.com/paper/2403.14888/) (2024)
- [LLMs4Life: Large Language Models for Ontology Learning in Life Sciences](https://mlsanity.com/paper/2412.02035/) (2024)
- [Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema](https://mlsanity.com/paper/2412.20942/) (2024)
- [Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education](https://mlsanity.com/paper/2407.10794/) (2024)
- [Causal Reasoning in Large Language Models: A Knowledge Graph Approach](https://mlsanity.com/paper/2410.11588/) (2024)
- [A Survey on Open Information Extraction from Rule-based Model to Large Language Model](https://mlsanity.com/paper/2208.08690/) (2022)
- [Graph-Augmented Reasoning: Evolving Step-by-Step Knowledge Graph Retrieval for LLM Reasoning](https://mlsanity.com/paper/2503.01642/) (2025)
- [Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models](https://mlsanity.com/paper/2312.01954/) (2023)
- [EntGPT: Entity Linking with Generative Large Language Models](https://mlsanity.com/paper/2402.06738/) (2024)
- [Schema-adaptable Knowledge Graph Construction](https://mlsanity.com/paper/2305.08703/) (2023)
- [Document-level Clinical Entity and Relation Extraction via Knowledge Base-Guided Generation](https://mlsanity.com/paper/2407.10021/) (2024)
- [Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation](https://mlsanity.com/paper/2505.02737/) (2025)
- [LKD-KGC: Domain-Specific KG Construction via LLM-driven Knowledge Dependency Parsing](https://mlsanity.com/paper/2505.24163/) (2025)
- [ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs](https://mlsanity.com/paper/2509.04696/) (2025)

## Built on by (most cited first, 21 of 21)

- [ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs](https://mlsanity.com/paper/2510.22590/) (2025)
- [Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2601.15678/) (2026)
- [LLM-Driven Ontology Construction for Enterprise Knowledge Graphs](https://mlsanity.com/paper/2602.01276/) (2026)
- [SPD-RAG: Sub-Agent Per Document Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.08329/) (2026)
- [Rate-Distortion Guided Knowledge Graph Construction from Lecture Notes Using Gromov-Wasserstein Optimal Transport](https://mlsanity.com/paper/2511.14595/) (2025)
- [AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature](https://mlsanity.com/paper/2602.18479/) (2026)
- [AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction](https://mlsanity.com/paper/2604.16950/) (2026)
- [Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation](https://mlsanity.com/paper/2604.03496/) (2026)
- [Schema-Aware Planning and Hybrid Knowledge Toolset for Reliable Knowledge Graph Triple Verification](https://mlsanity.com/paper/2604.04190/) (2026)
- [Automatic Ontology Construction Using LLMs as an External Layer of Memory, Verification, and Planning for Hybrid Intelligent Systems](https://mlsanity.com/paper/2604.20795/) (2026)
- [Overcoming the Impedance Mismatch: A Theoretical Roadmap for Fusing Foundation Models and Knowledge Graphs](https://mlsanity.com/paper/2606.15656/) (2026)
- [From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Science, Technology, and Innovation (STI) Analytics](https://mlsanity.com/paper/2607.21327/) (2026)
- [Evidence-Grounded Multimodal Knowledge Graph Construction for Multi-Lecture Educational Reasoning](https://mlsanity.com/paper/2608.03161/) (2026)
- [Task-Driven 3D Printability Assistance via Geometry- and Knowledge-Grounded LLM Reasoning](https://mlsanity.com/paper/2608.22128/) (2026)
- [Knowledge Cards: Structured Knowledge for AI Systems](https://mlsanity.com/paper/2608.26176/) (2026)
- [Do General NLP Embeddings Capture Ontological Reasoning?](https://mlsanity.com/paper/2609.00177/) (2026)
- [Physico-Geospatial Grounded Scene Interpretation for Mobile Robotics](https://mlsanity.com/paper/2609.06629/) (2026)
- [Scaling E-Commerce Attribute Extraction with Parallel Decoding](https://mlsanity.com/paper/2609.09716/) (2026)
- [Semantic Layer Induction from Raw Telemetry via Hierarchical LLM and RAG Abstraction](https://mlsanity.com/paper/2609.19615/) (2026)
- [Subjects, Not Authors: The Authorship Hazard in Agentic Dataspaces](https://mlsanity.com/paper/2609.30614/) (2026)
- [LLM-Guided Ontology-Driven Knowledge Graph Construction from Unstructured Text](https://mlsanity.com/paper/2609.31663/) (2026)

## Abstract (arXiv)

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.