arXiv ↔ knowledge graphs

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cross_type 2.81 — strongly supporting 6 facts

Knowledge graphs are the central subject of numerous research papers published on arXiv, including studies on entity disambiguation [1], medical QA [2], domain expertise enhancement [3], question answering [4], legal implications [5], and enterprise information exploration [6].

Facts (6)

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LLM-KG4QA: Large Language Models and Knowledge Graphs for ... github.com GitHub 4 facts

referenceThe paper 'Fact Finder -- Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs' (arXiv, 2024) discusses incorporating knowledge graphs to enhance the domain expertise of Large Language Models.

referenceEICopilot is a system designed to search and explore enterprise information over large-scale knowledge graphs using Large Language Model-driven agents (arXiv, 2025).

referenceThe paper 'A Prompt Engineering Approach and a Knowledge Graph based Framework for Tackling Legal Implications of Large Language Model Answers' (arXiv, 2024) proposes a framework combining prompt engineering and knowledge graphs to address legal implications in Large Language Model outputs.

referenceThe paper 'An Empirical Study over Open-ended Question Answering' (arXiv, 2024) investigates the OKGQA framework for Large Language Models and Knowledge Graphs in question answering.

Bridging the Gap Between LLMs and Evolving Medical Knowledge arxiv.org arXiv 1 fact

referenceRui Yang et al. (2024) published 'Kg-rank: Enhancing large language models for medical qa with knowledge graphs and ranking techniques' as an arXiv preprint (arXiv:2403.05881), which proposes using knowledge graphs and ranking to improve medical QA.

LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org arXiv 1 fact

referenceGerard Pons, Besim Bilalli, and Anna Queralt published 'Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation' as an arXiv preprint in 2025.