Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
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Computer Science > Computation and Language
arXiv:2511.03217 (cs)
[Submitted on 5 Nov 2025 (v1), last revised 26 Jun 2026 (this version, v2)]
Title:Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
Authors:Shaghayegh Kolli, Richard Rosenbaum, Timo Cavelius, Lasse Strothe, Andrii Lata, Jana Diesner
View a PDF of the paper titled Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification, by Shaghayegh Kolli and 5 other authors
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Abstract:Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information. At the same time, knowledge-graph-based fact-checkers deliver precise and interpretable evidence, yet suffer from limited coverage or latency. By integrating LLMs with knowledge graphs and real-time search agents, we introduce a hybrid fact-checking approach that leverages the individual strengths of each component. Our system comprises three autonomous steps: 1) a Knowledge Graph (KG) Retrieval for rapid one-hop lookups in DBpedia, 2) an LM-based classification guided by a task-specific labeling prompt, producing outputs with internal rule-based logic, and 3) a Web Search Agent invoked only when KG coverage is insufficient. Our pipeline achieves an F1 score of 0.93 on the FEVER benchmark on the Supported/Refuted split without task-specific fine-tuning. To address Not enough information cases, we conduct a targeted reannotation study showing that our approach frequently uncovers valid evidence for claims originally labeled as Not Enough Information (NEI), as confirmed by both expert annotators and LLM reviewers. With this paper, we present a modular, opensource fact-checking pipeline with fallback strategies and generalization across datasets.
| Comments: | Paper has been accepted at 9th wiNLP workshop at EMNLP |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Information Retrieval (cs.IR) |
| MSC classes: | 68T50 |
| ACM classes: | I.2.7; H.3.3 |
| Cite as: | arXiv:2511.03217 [cs.CL] |
| (or arXiv:2511.03217v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2511.03217
arXiv-issued DOI via DataCite
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Submission history
From: Timo Cavelius [view email][v1] Wed, 5 Nov 2025 06:10:05 UTC (195 KB)
[v2] Fri, 26 Jun 2026 07:15:49 UTC (9,519 KB)
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View a PDF of the paper titled Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification, by Shaghayegh Kolli and 5 other authors
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