{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T04:49:42Z","timestamp":1782017382886,"version":"3.54.5"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningbo Municipal Project","award":["2024A-154-G"],"award-info":[{"award-number":["2024A-154-G"]}]},{"name":"Ningbo Key Research and Development Program","award":["2025Z046"],"award-info":[{"award-number":["2025Z046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Knowledge graphs represent real-world facts as structured triplets and underpin a wide range of applications, including question answering, recommendation, and retrieval-augmented generation. Automatically extracting such triplets from unstructured text is essential for scalable knowledge base construction. Traditional extraction methods require task-specific training data and struggle to generalize across domains. Large language models (LLMs) offer an alternative through in-context learning, enabling flexible extraction without fine-tuning. However, LLMs frequently hallucinate\u2014generating plausible triplets unsupported by the source text. The root cause is the lack of provenance: existing methods produce triplets without explicit links to their textual origins, making faithfulness unverifiable. This paper presents Anchor-Extraction-Verification-Supplement (AEVS), a framework that grounds every triplet element to the source text. AEVS operates in three stages: (1) anchor discovery identifies entities, relation phrases, and attribute values with precise positions, forming a constrained extraction vocabulary; (2) grounded extraction generates triplets linked to discovered anchors; and (3) restoration-based verification validates triplets through hierarchical matching, with a coverage-aware supplement ensuring comprehensive extraction. Experiments on WebNLG, REBEL, and Wiki-NRE demonstrate consistent improvements over both trained models and LLM-based baselines. Ablation studies confirm that anchor-based constraints are the primary mechanism for hallucination reduction. Dedicated analyses of anchor discovery quality, computational cost (2.83\u20134.28 LLM calls per sample), and hallucination rates (0.23\u201320.23% across model\u2013dataset configurations) provide insights into the framework\u2019s practical applicability and limitations.<\/jats:p>","DOI":"10.3390\/computers15030178","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T15:49:42Z","timestamp":1773071382000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Grounded Knowledge Graph Extraction via LLMs: An Anchor-Constrained Framework with Provenance Tracking"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuzhao","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315199, China"},{"name":"School of Computer Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Genlang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315199, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binhua","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6624-322X","authenticated-orcid":false,"given":"Yan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315199, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","article-title":"A Survey on Knowledge Graphs: Representation, Acquisition, and Applications","volume":"33","author":"Ji","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_2","unstructured":"Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., and Larson, J. (2024). From Local to Global: A Graph RAG Approach to Query-Focused Summarization. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mintz, M., Bills, S., Snow, R., and Jurafsky, D. (2009). Distant Supervision for Relation Extraction without Labeled Data. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Association for Computational Linguistics.","DOI":"10.3115\/1690219.1690287"},{"key":"ref_4","unstructured":"Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., and Xu, B. (August, January 30). Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wei, Z., Su, J., Wang, Y., Tian, Y., and Chang, Y. (2020, January 5\u201310). A Novel Cascade Binary Tagging Framework for Relational Triple Extraction. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.acl-main.136"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yu, B., Zhang, Y., Liu, T., Zhu, H., and Sun, L. (2020, January 8\u201313). TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking. Proceedings of the 28th International Conference on Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.coling-main.138"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Huguet Cabot, P.L., and Navigli, R. (2021). REBEL: Relation Extraction By End-to-end Language Generation. Findings of the Association for Computational Linguistics: EMNLP 2021, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2021.findings-emnlp.204"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Josifoski, M., De Cao, N., Peyrard, M., Petroni, F., and West, R. (2022, January 10\u201315). GenIE: Generative Information Extraction. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, WA, USA.","DOI":"10.18653\/v1\/2022.naacl-main.342"},{"key":"ref_9","unstructured":"Wei, X., Cui, X., Cheng, N., Wang, X., Zhang, X., Huang, S., Xie, P., Xu, J., Chen, Y., and Zhang, M. (2023). ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wan, Z., Cheng, F., Mao, Z., Liu, Q., Song, H., Li, J., and Kurohashi, S. (2023, January 6\u201310). GPT-RE: In-context Learning for Relation Extraction using Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore.","DOI":"10.18653\/v1\/2023.emnlp-main.214"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, B., and Soh, H. (2024, January 12\u201316). Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, FL, USA.","DOI":"10.18653\/v1\/2024.emnlp-main.548"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1145\/3571730","article-title":"Survey of Hallucination in Natural Language Generation","volume":"55","author":"Ji","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_13","first-page":"1083","article-title":"Kernel Methods for Relation Extraction","volume":"3","author":"Zelenko","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_14","unstructured":"Zeng, D., Liu, K., Lai, S., Zhou, G., and Zhao, J. (2014, January 23\u201329). Relation Classification via Convolutional Deep Neural Network. Proceedings of the COLING 2014, the 25th International Conference on Computational Linguistics, Dublin, Ireland."},{"key":"ref_15","unstructured":"Baldini Soares, L., FitzGerald, N., Ling, J., and Kwiatkowski, T. (August, January 28). Matching the Blanks: Distributional Similarity for Relation Learning. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhong, Z., and Chen, D. (2021, January 6\u201311). A Frustratingly Easy Approach for Entity and Relation Extraction. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Online.","DOI":"10.18653\/v1\/2021.naacl-main.5"},{"key":"ref_17","unstructured":"Dixit, K., and Al-Onaizan, Y. (August, January 28). Span-Level Model for Relation Extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_18","unstructured":"Eberts, M., and Ulges, A. (September, January 29). Span-based Joint Entity and Relation Extraction with Transformer Pre-training. Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wadden, D., Wennberg, U., Luan, Y., and Hajishirzi, H. (2019, January 3\u20137). Entity, Relation, and Event Extraction with Contextualized Span Representations. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, Hong Kong, China.","DOI":"10.18653\/v1\/D19-1585"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, J., and Lu, W. (2020, January 16\u201320). Two Are Better Than One: Joint Entity and Relation Extraction with Table-Sequence Encoders. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Online.","DOI":"10.18653\/v1\/2020.emnlp-main.133"},{"key":"ref_21","unstructured":"Shang, Y.M., Huang, H., and Mao, X.L. (March, January 22). OneRel: Joint Entity and Relation Extraction with One Module in One Step. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual."},{"key":"ref_22","unstructured":"Guo, Z., Zhang, Y., and Lu, W. (August, January 28). Attention Guided Graph Convolutional Networks for Relation Extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_23","unstructured":"Fu, T.J., Li, P.H., and Ma, W.Y. (August, January 28). GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zeng, S., Xu, R., Chang, B., and Li, L. (2020, January 16\u201320). Double Graph Based Reasoning for Document-level Relation Extraction. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Online.","DOI":"10.18653\/v1\/2020.emnlp-main.127"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zeng, X., Zeng, D., He, S., Liu, K., and Zhao, J. (2018, January 15\u201320). Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1047"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nayak, T., and Ng, H.T. (2020, January 7\u201312). Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i05.6374"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dognin, P.L., Padhi, I., Melnyk, I., and Das, P. (2021, January 7\u201311). ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic.","DOI":"10.18653\/v1\/2021.emnlp-main.83"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1007\/s11280-024-01297-w","article-title":"LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities","volume":"27","author":"Zhu","year":"2024","journal-title":"World Wide Web"},{"key":"ref_29","unstructured":"Li, B., Fang, G., Yang, Y., Wang, Q., Ye, W., Zhao, W., and Zhang, S. (2023). Evaluating ChatGPT\u2019s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1145\/3641850","article-title":"CodeKGC: Code Language Model for Generative Knowledge Graph Construction","volume":"23","author":"Bi","year":"2024","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, K., Gutierrez, B.J., and Su, Y. (2023). Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors. Findings of the Association for Computational Linguistics: ACL 2023, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2023.findings-acl.50"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Reimers, N., and Gurevych, I. (2019, January 3\u20137). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China.","DOI":"10.18653\/v1\/D19-1410"},{"key":"ref_33","unstructured":"Ferreira, T.C., Gardent, C., Ilinykh, N., van der Lee, C., Mille, S., Moussallem, D., and Shimorina, A. (2020, January 18). The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020). Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), Dublin, Ireland (Virtual)."},{"key":"ref_34","unstructured":"Distiawan, B., Weikum, G., Qi, J., and Zhang, R. (August, January 28). Neural Relation Extraction for Knowledge Base Enrichment. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/3\/178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T09:14:08Z","timestamp":1773220448000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/3\/178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,9]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["computers15030178"],"URL":"https:\/\/doi.org\/10.3390\/computers15030178","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,9]]}}}