{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T19:25:06Z","timestamp":1767900306438,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Advanced Materials-National Science and Technology Major Project","award":["2025ZD0620100"],"award-info":[{"award-number":["2025ZD0620100"]}]},{"name":"National Key Research and Development Program of China","award":["2023YFB4606200"],"award-info":[{"award-number":["2023YFB4606200"]}]},{"name":"Key Program of Science and Technology of Yunnan Province","award":["202302AB080020"],"award-info":[{"award-number":["202302AB080020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Knowledge graphs (KGs) offer a structured and collaborative approach to integrating diverse knowledge from various domains. However, constructing knowledge graphs typically requires significant manual effort and heavily relies on pretrained models, limiting their adaptability to specific sub-domains. This paper proposes an innovative, efficient, and locally deployable knowledge graph construction framework that leverages low-rank adaptation (LoRA) to fine-tune large language models (LLMs) in order to reduce noise. By integrating iterative optimization, consistency-guided filtering, and prompt-based extraction, the proposed method achieves a balance between precision and coverage, enabling the robust extraction of standardized subject\u2013predicate\u2013object triples from raw long texts. This makes it highly effective for knowledge graph construction and downstream reasoning tasks. We applied the parameter-efficient open-source model Qwen3-14B, and experimental results on the SciERC dataset show that, under strict matching (i.e., ensuring the exact matching of all components), our method achieved an F1 score of 0.358, outperforming the baseline model\u2019s F1 score of 0.349. Under fuzzy matching (allowing some parts of the triples to be unmatched), the F1 score reached 0.447, outperforming the baseline model\u2019s F1 score of 0.392, demonstrating the effectiveness of our approach. Ablation studies validate the robustness and generalization potential of our method, highlighting the contribution of each component to the overall performance.<\/jats:p>","DOI":"10.3390\/fi18010032","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T08:42:58Z","timestamp":1767688978000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LECITE: LoRA-Enhanced and Consistency-Guided Iterative Knowledge Graph Construction"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4874-9047","authenticated-orcid":false,"given":"Donghao","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Computer Engineering & Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3020-005X","authenticated-orcid":false,"given":"Quan","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Computer Engineering & Science, Shanghai University, Shanghai 200444, China"},{"name":"Center of Materials Informatics and Data Science, Materials Genome Institute, Shanghai University, Shanghai 200444, China"},{"name":"Key Laboratory of Silicate Cultural Relics Conservation, Shanghai University, Ministry of Education, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,6]]},"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":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_2","unstructured":"Shortliffe, E. (2012). Computer-Based Medical Consultations: MYCIN, Elsevier."},{"key":"ref_3","unstructured":"Bahdanau, D. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1080\/0951192X.2021.1972461","article-title":"An automatic machining process decision-making system based on knowledge graph","volume":"34","author":"Guo","year":"2021","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Huang, X., Zhang, J., Li, D., and Li, P. (2019, January 11\u201315). Knowledge graph embedding based question answering. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia.","DOI":"10.1145\/3289600.3290956"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hakkani-T\u00fcr, D., Celikyilmaz, A., Heck, L., Tur, G., and Zweig, G. (2014, January 14\u201318). Probabilistic enrichment of knowledge graph entities for relation detection in conversational understanding. Proceedings of the INTERSPEECH, Singapore.","DOI":"10.21437\/Interspeech.2014-479"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3549","DOI":"10.1109\/TKDE.2020.3028705","article-title":"A survey on knowledge graph-based recommender systems","volume":"34","author":"Guo","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, F., Yuan, N.J., Lian, D., Xie, X., and Ma, W.Y. (2016, January 13\u201317). Collaborative knowledge base embedding for recommender systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939673"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ye, H., Zhang, N., Chen, H., and Chen, H. (2022). Generative knowledge graph construction: A review. arXiv.","DOI":"10.18653\/v1\/2022.emnlp-main.1"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3618295","article-title":"A comprehensive survey on automatic knowledge graph construction","volume":"56","author":"Zhong","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"1","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_13","doi-asserted-by":"crossref","unstructured":"Yan, H., Dai, J., Ji, T., Qiu, X., and Zhang, Z. (2021). A unified generative framework for aspect-based sentiment analysis. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Virtual, 1\u20136 August 2021, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2021.acl-long.188"},{"key":"ref_14","unstructured":"Paolini, G., Athiwaratkun, B., Krone, J., Ma, J., Achille, A., Anubhai, R., Santos, C.N.d., Xiang, B., and Soatto, S. (2021). Structured prediction as translation between augmented natural languages. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lu, Y., Liu, Q., Dai, D., Xiao, X., Lin, H., Han, X., Sun, L., and Wu, H. (2022). Unified structure generation for universal information extraction. arXiv.","DOI":"10.18653\/v1\/2022.acl-long.395"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"\u017dukov-Gregori\u010d, A., Bachrach, Y., and Coope, S. (2018, January 15\u201320). Named entity recognition with parallel recurrent neural networks. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Melbourne, VIC, Australia.","DOI":"10.18653\/v1\/P18-2012"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Martins, P.H., Marinho, Z., and Martins, A.F. (2019). Joint learning of named entity recognition and entity linking. arXiv.","DOI":"10.18653\/v1\/P19-2026"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Choi, E., Levy, O., Choi, Y., and Zettlemoyer, L. (2018). Ultra-fine entity typing. arXiv.","DOI":"10.18653\/v1\/P18-1009"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8576","DOI":"10.1609\/aaai.v34i05.6380","article-title":"Fine-grained entity typing for domain independent entity linking","volume":"Volume 34","author":"Onoe","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_20","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: Technical Papers, Dublin, Ireland."},{"key":"ref_21","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L. (2020, January 5\u201310). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15566","DOI":"10.18653\/v1\/2023.acl-long.868","article-title":"Revisiting relation extraction in the era of large language models","volume":"Volume 2023","author":"Wadhwa","year":"2023","journal-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, B., and Soh, H. (2024). Extract, define, canonicalize: An llm-based framework for knowledge graph construction. arXiv.","DOI":"10.18653\/v1\/2024.emnlp-main.548"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12013","DOI":"10.1007\/s00521-025-11162-0","article-title":"Mitigating exposure bias in large language model distillation: An imitation learning approach","volume":"37","author":"Pozzi","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"ref_26","unstructured":"Jiang, A., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D., Casas, D., Bressand, F., Lengyel, G., Lample, G., and Saulnier, L. (2024). Mistral 7B. arXiv."},{"key":"ref_27","unstructured":"Yang, A., Li, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Gao, C., Huang, C., and Lv, C. (2025). Qwen3 technical report. arXiv."},{"key":"ref_28","first-page":"3","article-title":"Lora: Low-rank adaptation of large language models","volume":"1","author":"Hu","year":"2022","journal-title":"ICLR"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Luan, Y., He, L., Ostendorf, M., and Hajishirzi, H. (2018). Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. arXiv.","DOI":"10.18653\/v1\/D18-1360"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/1\/32\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T05:15:13Z","timestamp":1767849313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/1\/32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,6]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["fi18010032"],"URL":"https:\/\/doi.org\/10.3390\/fi18010032","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,6]]}}}