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The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Fact Verification in Knowledge Graphs Using LLMs

Shami, Farzad, Marchesin, Stefano, Silvello, Gianmaria·2025·Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval
DOI 10.1145/3726302.3730142
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  • A related work in fact verification has similarly employed multiple LLMs to evaluate the same factual statement, leveraging entropy-based measures as indicators of cross-model agreement and overall factual consistency.

    “A related work in fact verification has similarly employed multiple LLMs to evaluate the same factual statement, leveraging entropy-based measures as indicators of cross-model agreement and overall factual consistency [54].”

    From Provenance-driven nanopublications: representing source lineage and trust networks for multi-source assertions (2025) · cites this paper as [54]

    Section 3 (FactCheck; page unavailable) says four LLMs evaluate the correctness of the same knowledge-graph triple and combine their predictions by majority vote. It discusses model consistency when resolving ties, but does not describe entropy-based measures of cross-model agreement or factual consistency. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Citations

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Research works printed but not linked: 22

  1. 10.3390/info14060336 · DOI
  2. Naser Ahmadi Joohyung Lee Paolo Papotti and Mohammed Saeed. 2019. Explainable Fact Checking with Probabilistic Answer Set Programming. arXiv:1906.09198 [cs.DB] https://arxiv.org/abs/1906.09198 · DOI
  3. 10.1007/978-3-540-76298-0_52 · DOI · Paper page
  4. 10.1145/1376616.1376746 · DOI · Paper page
  5. 10.1145/2463676.2465297 · DOI
  6. Abhimanyu Dubey Abhinav Jauhri Abhinav Pandey Abhishek Kadian Ahmad Al-Dahle et al. 2024. The Llama 3 Herd of Models. arXiv:2407.21783 [cs.AI] https://arxiv.org/abs/2407.21783 · arXiv
  7. 10.1145/3289600.3290996 · DOI
  8. Junyang Gao, Xian Li, Yifan Ethan Xu, Bunyamin Sisman, Xin Luna Dong, and Jun Yang. 2019. Efficient Knowledge Graph Accuracy Evaluation. arXiv:1907.09657 https://arxiv.org/abs/1907.09657 · arXiv
  9. 10.1016/j.websem.2015.08.001 · DOI
  10. Qingyu Guo Fuzhen Zhuang Chuan Qin Hengshu Zhu Xing Xie Hui Xiong and Qing He. 2020. A Survey on Knowledge Graph-Based Recommender Systems. arXiv:2003.00911 [cs.IR] https://arxiv.org/abs/2003.00911 · arXiv
  11. 10.14778/3665844.3665865 · DOI
  12. 10.1145/3725279 · DOI · Paper page
  13. 10.1609/hcomp.v12i1.31605 · DOI · Paper page
  14. 10.1145/3627673.3679561 · DOI
  15. 10.18653/v1/D17-1183 · DOI
  16. Reham Omar Ishika Dhall Panos Kalnis and Essam Mansour. 2023. A Universal Question-Answering Platform for Knowledge Graphs. arXiv:2303.00595 [cs.AI] https://arxiv.org/abs/2303.00595 · arXiv
  17. 10.18653/v1/d17-1184 · DOI
  18. Morgane Riviere Shreya Pathak and Pier Giuseppe Sessa et al. 2024. Gemma 2: Improving Open Language Models at a Practical Size. arXiv:2408.00118 [cs.CL] https://arxiv.org/abs/2408.00118 · arXiv
  19. Prakhar Singh, Anubrata Das, Junyi Jessy Li, and Matthew Lease. 2022. The Case for Claim Difficulty Assessment in Automatic Fact Checking. arXiv:2109.09689 [cs.CL] https://arxiv.org/abs/2109.09689 · arXiv
  20. 10.18653/V1/2024.NAACL-LONG.18 · DOI · Paper page
  21. 10.1007/978-3-030-30793-6_36 · DOI
  22. 10.1145/3269206.3269308 · DOI

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Metadata

Authors
Shami, F., Marchesin, S., & Silvello, G.
Venue
Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher
ACM
Published
2025-07-13
Pages
3985-3989
DOI
10.1145/3726302.3730142
Cited by
10
References
5
Funders
HORIZON EUROPE Framework Programme (101137074)

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