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Automated Construction of a Knowledge Graph of Nuclear Fusion Energy for Effective Elicitation and Retrieval of Information

arXiv 2504.07738 ↗First posted 10 Apr 2025cs.CLcited by 0

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Abstract (arXiv)

In this document, we discuss a multi-step approach to automated construction of a knowledge graph, for structuring and representing domain-specific knowledge from large document corpora. We apply our method to build the first knowledge graph of nuclear fusion energy, a highly specialized field characterized by vast scope and heterogeneity. This is an ideal benchmark to test the key features of our pipeline, including automatic named entity recognition and entity resolution. We show how pre-trained large language models can be used to address these challenges and we evaluate their performance against Zipf's law, which characterizes human natural language. Additionally, we develop a knowledge-graph retrieval-augmented generation system that uses multiple prompts with large language models to provide contextually relevant answers to natural-language queries, including complex multi-hop questions requiring reasoning across interconnected entities.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 43 references that are in the map. First posted on arXiv 10 Apr 2025.

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15 of 43 references.

References · 15

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  3. 2018A Survey on Deep Learning for Named Entity Recognitioncited by 282
  4. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systemscited by 265
  5. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 257
  6. 2023ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTcited by 250
  7. 2022Large Language Models are Few-Shot Clinical Information Extractorscited by 207
  8. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  9. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
  10. 2023PromptNER: Prompting For Named Entity Recognitioncited by 35
  11. 2023Iterative Zero-Shot LLM Prompting for Knowledge Graph Constructioncited by 25
  12. 2024How well do LLMs cite relevant medical references? An evaluation framework and analysescited by 21
3 more
  1. 2022BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Modelscited by 20
  2. 2022An Automatic Ontology Generation Framework with An Organizational Perspectivecited by 6
  3. 2024KnowledgeHub: An end-to-end Tool for Assisted Scientific Discoverycited by 1

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  1. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
  2. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
  3. 2025Leveraging Spreading Activation for Improved Document Retrieval in Knowledge-Graph-Based RAG Systems
  4. 2025KGRAG-Ex: Explainable Retrieval-Augmented Generation with Knowledge Graph-based Perturbations
  5. 2026BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphs
  6. 2026Enabling Knowledge Graph Understanding at Scale with the EXplore Your Graphs ENgine (EXYGEN)
  7. 2025DSRAG: A Domain-Specific Retrieval Framework Based on Document-derived Multimodal Knowledge Graph
  8. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation