LLMs as Knowledge Graph Refiners: Mitigating Factual Inconsistencies in Generative Knowledge Extraction (2026.acl-long)
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| Challenge: | Knowledge graphs (KGs) represent real-world entities and their relations in a structured form. |
| Approach: | They propose a framework that performs triple-level refinement on KGs constructed via GKE. |
| Outcome: | The proposed framework improves KG quality from diverse perspectives. |
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| Challenge: | Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination . |
| Approach: | They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality . |
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Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction (2024.emnlp-main)
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| Challenge: | Existing methods for knowledge graph creation (KGC) are limited in their ability to scale up to text common in many real-world applications. |
| Approach: | They propose a framework for knowledge graph creation from input text using a pre-defined schema and a trained component that retrieves schema elements relevant to the input text. |
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Fact Finder - Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs (2026.eacl-demo)
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Daniel Steinigen, Roman Teucher, Timm Heine Ruland, Max Rudat, Nicolas Flores-Herr, Peter Fischer, Nikola Milosevic, Christopher Schymura, Angelo Ziletti
| Challenge: | Recent advances in Large Language Models have demonstrated their proficiency in answering natural language queries. |
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KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models (2023.findings-emnlp)
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| Challenge: | Using large language models for complex reasoning tasks on knowledge graphs remains unexplored. |
| Approach: | They propose a multi-purpose framework leveraging large language models for complex reasoning tasks on knowledge graphs. |
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CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph (2024.acl-demos)
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| Challenge: | Large language models (LLMs) are susceptible to generating hallucinated content and often encompass factually inaccurate information. |
| Approach: | They propose a framework that leverages knowledge graphs to address the limitations of Large Language Models (LLMs) they identify and decompose required knowledge triples that are not present in the KG, enriching them and aligning updates with real-world demands. |
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From Graph to Text and Back: Semantic Fidelity in Automated Industrial Knowledge Graphs (2026.acl-industry)
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| Challenge: | Large Language Models (LLMs) often hallucinate entities or omit relations, posing unacceptable liability. |
| Approach: | They propose a self-supervised round-trip pipeline to enforce strict semantic fidelity in KG-to-text generation. |
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Correcting on Graph: Faithful Semantic Parsing over Knowledge Graphs with Large Language Models (2025.findings-acl)
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| Challenge: | Complex multi-hop questions require comprehensive retrieval and reasoning. |
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Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models (2026.eacl-long)
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| Challenge: | Knowledge graphs provide structured, verifiable grounding for large language models . current LLMs use KGs as auxiliary structures for text retrieval . |
| Approach: | They propose a pipeline that constructs KGs from open-domain texts using triplets and qualifiers. |
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Knowledge Graph-Enhanced Large Language Models via Path Selection (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown unprecedented performance in various real-world applications, but they are known to generate factually inaccurate outputs. |
| Approach: | They propose a framework to integrate external knowledge extracted from Knowledge Graphs (KGs) they propose to generate scores for knowledge paths with input texts via latent semantic matching. |
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Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2021.naacl-main)
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| Challenge: | Existing work on data-to-text generation focused on domain-specific benchmark datasets. |
| Approach: | They use a KG-Wikipedia text aligned corpus to verbalize the entire English Wikidata KG . they show that this approach can be used to integrate structured KGs and natural language corpora . |
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