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related 5.09 — strongly supporting 33 facts
Knowledge Graphs and Large Language Models are deeply interconnected through research focused on using LLMs to automate the construction and enrichment of knowledge graphs {fact:8, fact:10, fact:24}, as well as utilizing knowledge graphs to ground LLMs, improve their factual accuracy, and mitigate hallucinations {fact:3, fact:11, fact:13, fact:21}.
Facts (33)
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Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org 4 facts
claimThe BEAR method uses Large Language Models (LLMs) solely to parse and extract information from documents for Knowledge Graph (KG) construction, failing to utilize other potential benefits LLMs offer for KG construction.
referenceThe Right for Right Reasons (R3) methodology for Knowledge Graph Question Answering (KGQA) using LLMs treats common sense KGQA as a tree-structured search to utilize commonsense axioms, making the reasoning procedure verifiable.
referenceKhorashadizadeh et al. identified methods using Large Language Models for knowledge graph construction tasks including text-to-ontology mapping, entity extraction, ontology alignment, and knowledge graph validation through fact-checking and inconsistency detection.
referenceLMExplainer uses a Knowledge Graph and a graph attention neural network to understand key decision signals of LLMs and convert them into natural language explanations for better explainability.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 3 facts
referenceXu et al. (2024) introduced 'Generate-on-Graph', a method that treats large language models as both an agent and a knowledge graph for incomplete knowledge graph question answering.
claimPrompt engineering for Knowledge Graph (KG) completion involves designing input prompts to guide Large Language Models (LLMs) in inferring and filling missing parts of KGs, which enhances multi-hop link prediction and allows handling of unseen cues in zero-sample scenarios.
referenceLLM-facteval (Luo et al., 2023c) proposes a Knowledge Graph-based framework to systematically evaluate Large Language Models by generating questions from Knowledge Graph facts across generic and domain-specific contexts.
LLM-Powered Knowledge Graphs for Enterprise Intelligence and ... arxiv.org 3 facts
procedureThe knowledge-graph-enhanced LLM system answers analytics queries by retrieving statistics from the knowledge graph, refining the data via the LLM, and generating actionable insights.
claimIn the knowledge graph, identified entities are represented as nodes, while relationships inferred by Large Language Models (LLMs) are represented as edges.
claimThe framework uses large language models to automate entity extraction, relationship inference, and contextual enrichment, creating a unified graph representation where nodes represent entities like people, topics, or events, and edges represent relationships.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com 2 facts
claimOpenBG is a recommendation systems-oriented knowledge graph that utilizes large language models to process and understand user preferences from textual data, which improves recommendation accuracy.
claimLLMs facilitate KG-to-text generation and question-answering by generating human-like descriptions of facts stored within a knowledge graph.
The construction and refined extraction techniques of knowledge ... nature.com 2 facts
referenceThe article titled 'The construction and refined extraction techniques of knowledge graph based on large language models' was published in the journal Scientific Reports in 2026 by authors Peng, L., Yang, P., Juexiang, Y., et al.
claimThe paper titled 'The construction and refined extraction techniques of knowledge' proposes integrating Large Language Models (LLMs) to overcome barriers in specialized Knowledge Graph (KG) construction.
How to Improve Multi-Hop Reasoning With Knowledge Graphs and ... neo4j.com 2 facts
claimGraphRAG is a retrieval-augmented generation (RAG) technique that utilizes a knowledge graph to enhance the accuracy, context, and explainability of responses generated by large language models (LLMs).
claimWhen integrated with LLMs, a knowledge graph grounds the model in specific data by organizing structured and unstructured information into a connected data layer, enabling more accurate and explainable AI insights.
Efficient Knowledge Graph Construction and Retrieval from ... - arXiv arxiv.org 2 facts
procedureThe proposed GraphRAG framework utilizes a dependency-based knowledge graph construction pipeline that leverages industrial-grade NLP libraries to extract entities and relations from unstructured text, eliminating the need for Large Language Models (LLMs) in the construction phase.
claimBuilding a knowledge graph at enterprise scale incurs significant GPU or CPU costs and high latency when relying on Large Language Models or heavyweight NLP pipelines for entity and relation extraction.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org 1 fact
procedureIncorporating fairness-aware techniques into Knowledge Graph retrieval, such as reranking based on bias detection, and integrating them with counterfactual prompting can mitigate bias in Large Language Models.
LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org 1 fact
referenceYejin Kim, Eojin Kang, Juae Kim, and H. Howie Huang authored 'Causal Reasoning in Large Language Models: A Knowledge Graph Approach', published as an arXiv preprint in October 2024.
Combining large language models with enterprise knowledge graphs frontiersin.org 1 fact
claimThe authors of 'Combining large language models with enterprise knowledge graphs' identify LLMs, knowledge graph, relation extraction, knowledge graph enrichment, AI, enterprise AI, carbon footprint, and human in the loop as the primary keywords for their research.
A Knowledge Graph-Based Hallucination Benchmark for Evaluating ... arxiv.org 1 fact
referenceKG-fpq is a framework for evaluating factuality hallucination in large language models using knowledge graph-based false premise questions.
[PDF] LLM-Powered Knowledge Graphs for Enterprise Intelligence and ... arxiv.org 1 fact
referenceThe framework introduced in 'LLM-Powered Knowledge Graphs for Enterprise Intelligence and Analytics' uses large language models (LLMs) to unify various enterprise data sources into a comprehensive, activity-centric knowledge graph.
KGHaluBench: A Knowledge Graph-Based Hallucination ... researchgate.net 1 fact
claimKGHaluBench is a Knowledge Graph-based hallucination benchmark designed to evaluate Large Language Models.
Knowledge Graphs Enhance LLMs for Contextual Intelligence linkedin.com 1 fact
claimKnowledge graphs enable context-aware reasoning in Large Language Models (LLMs) by allowing the model to understand how entities relate, such as a customer's history, product dependencies, or upstream inputs in a process.
A question-answering framework for geospatial data retrieval ... tandfonline.com 1 fact
claimThe authors of the paper 'A question-answering framework for geospatial data retrieval' utilize a knowledge graph as an external knowledge base to improve the performance of Large Language Models (LLMs) in the domain of spatiotemporal data retrieval.
A Knowledge Graph-Based Hallucination Benchmark for Evaluating ... aclanthology.org 1 fact
procedureThe KGHaluBench framework utilizes a knowledge graph to dynamically construct challenging, multifaceted questions for LLMs, with question difficulty statistically estimated to address popularity bias.
Unlock the Power of Knowledge Graphs and LLMs - TopQuadrant topquadrant.com 1 fact
claimLarge language models enable faster knowledge graph creation and curation by performing entity resolution, automated tagging of unstructured data, and entity and class extraction.
A Knowledge-Graph Based LLM Hallucination Evaluation Framework semanticscholar.org 1 fact
claimGraphEval is a hallucination evaluation framework for Large Language Models that represents information using Knowledge Graph structures, as presented in the paper 'A Knowledge-Graph Based LLM Hallucination Evaluation Framework' by Sansford and Richardson.
KG-IRAG: A Knowledge Graph-Based Iterative Retrieval-Augmented ... arxiv.org 1 fact
procedureKG-IRAG evaluation comparisons are conducted by feeding standard data into Large Language Models in three formats: raw data (data frame), context-enhanced data, and Knowledge Graph (KG) triplet representations.
Applying Large Language Models in Knowledge Graph-based ... arxiv.org 1 fact
procedureThe concept matching approach developed by Hertling and Paulheim uses open source Large Language Models (LLMs) to match candidate concepts from two different knowledge graph inputs, utilizing cardinality and confidence filters to improve result quality.
Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org 1 fact
claimThe authors of the study adapted the publicly available MedRAG code and its associated knowledge graph to enable Large Language Models to generate responses grounded in external, validated medical information.
A knowledge-graph based LLM hallucination evaluation framework amazon.science 1 fact
claimThe GraphEval framework identifies hallucinations in Large Language Models by utilizing Knowledge Graph structures to represent information.