{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T10:39:26Z","timestamp":1770979166639,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2022YFF0604502"],"award-info":[{"award-number":["2022YFF0604502"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Symmetry"],"abstract":"<jats:p>Relation extraction in the equipment domain often exhibits asymmetric patterns, where entities participate in multiple overlapping relations that break the expected structural symmetry of semantic associations. Such asymmetry increases task complexity and reduces extraction accuracy in conventional approaches. To address this issue, we propose a symmetry- and asymmetry-aware dual-path retrieval and in-context learning-based large language model. Specifically, the BGE-M3 embedding model is fine-tuned for domain-specific adaptation, and a multi-level retrieval database is constructed to capture both global semantic symmetry at the sentence level and local asymmetric interactions at the relation level. A dual-path retrieval strategy, combined with Reciprocal Rank Fusion, integrates these complementary perspectives, while task-specific prompt templates further enhance extraction accuracy. Experimental results demonstrate that our method not only mitigates the challenges posed by overlapping and asymmetric relations but also leverages the latent symmetry of semantic structures to improve performance. Experimental results show that our approach effectively mitigates challenges from overlapping and asymmetric relations while exploiting latent semantic symmetry, achieving an F1-score of 88.53%, a 1.86% improvement over the strongest baseline (GPT-RE).<\/jats:p>","DOI":"10.3390\/sym17101647","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T08:10:51Z","timestamp":1759738251000},"page":"1647","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Symmetry- and Asymmetry-Aware Dual-Path Retrieval and In-Context Learning-Based LLM for Equipment Relation Extraction"],"prefix":"10.3390","volume":"17","author":[{"given":"Mingfei","family":"Tang","sequence":"first","affiliation":[{"name":"College of Computer Science, Beijing Information Science & Technology University (BISTU), Beijing 102200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing Information Science & Technology University (BISTU), Beijing 102200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhipeng","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing Information Science & Technology University (BISTU), Beijing 102200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolong","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing Information Science & Technology University (BISTU), Beijing 102200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiulei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing Information Science & Technology University (BISTU), Beijing 102200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s10462-025-11280-0","article-title":"A survey on cutting-edge relation extraction techniques based on language models","volume":"58","author":"Lopez","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12784","DOI":"10.1109\/TNNLS.2023.3264735","article-title":"Joint entity and relation extraction with set prediction networks","volume":"35","author":"Sui","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhong, L., Wu, J., Li, Q., Peng, H., and Wu, X. (2023). A Comprehensive Survey on Automatic Knowledge Graph Construction. ACM Comput. Surv., 56.","DOI":"10.1145\/3618295"},{"key":"ref_4","unstructured":"Srihari, R., and Li, W. (May, January 29). A question answering system supported by information extraction. Proceedings of the Sixth Conference on Applied Natural Language Processing, ANLC \u201900, Seattle, WA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhang, N., Xie, X., Deng, S., Yao, Y., Tan, C., Huang, F., Si, L., and Chen, H. (2022, January 25\u201329). KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction. Proceedings of the ACM Web Conference 2022, WWW \u201922, Lyon, France.","DOI":"10.1145\/3485447.3511998"},{"key":"ref_6","unstructured":"He, Y., Ji, H., Li, S., Liu, Y., and Chang, C.H. (2022). An Improved Baseline for Sentence-level Relation Extraction. Volume 2: Short Papers, Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, Online, 20\u201323 November 2022, Association for Computational Linguistics."},{"key":"ref_7","unstructured":"Aydar, M., Bozal, O., and Ozbay, F. (2020). Neural relation extraction: A survey. arXiv."},{"key":"ref_8","unstructured":"Chinchor, N., and Marsh, E. (May, January 29). Muc-7 information extraction task definition. Proceedings of the Seventh Message Understanding Conference (MUC-7), Fairfax, VA, USA."},{"key":"ref_9","unstructured":"Lafferty, J., McCallum, A., and Pereira, F. (July, January 28). Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proceedings of the ICML, Williamstown, MA, USA."},{"key":"ref_10","unstructured":"Pawar, S., Palshikar, G.K., and Bhattacharyya, P. (2017). Relation extraction: A survey. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Agichtein, E., and Gravano, L. (2000, January 2\u20137). Snowball: Extracting relations from large plain-text collections. Proceedings of the Fifth ACM Conference on Digital Libraries, San Antonio, TX, USA.","DOI":"10.1145\/376284.375774"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hasegawa, T., Sekine, S., and Grishman, R. (2004, January 21\u201326). Discovering Relations among Named Entities from Large Corpora. Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), Barcelona, Spain.","DOI":"10.3115\/1218955.1219008"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"51315","DOI":"10.1109\/ACCESS.2020.2980859","article-title":"Improving graph convolutional networks based on relation-aware attention for end-to-end relation extraction","volume":"8","author":"Hong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dai, D., Xiao, X., Lyu, Y., Dou, S., She, Q., and Wang, H. (2019, January 29\u201331). Joint extraction of entities and overlapping relations using position-attentive sequence labeling. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.","DOI":"10.1609\/aaai.v33i01.33016300"},{"key":"ref_15","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_16","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_17","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_18","doi-asserted-by":"crossref","unstructured":"Zeng, X., He, S., Zeng, D., Liu, K., Liu, S., and Zhao, J. (2019, January 3\u20137). Learning the extraction order of multiple relational facts in a sentence with reinforcement learning. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China.","DOI":"10.18653\/v1\/D19-1035"},{"key":"ref_19","unstructured":"Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems 33, Conference on Neural Information Processing Systems."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, K., Guti\u00e9rrez, 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_21","unstructured":"Bouamor, H., Pino, J., and Bali, K. GPT-RE: In-context Learning for Relation Extraction using Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing."},{"key":"ref_22","unstructured":"Efeoglu, S., and Paschke, A. (2024). Retrieval-augmented generation-based relation extraction. arXiv."},{"key":"ref_23","unstructured":"Chaplot, D.S. (2023). Albert q. jiang, alexandre sablayrolles, arthur mensch, chris bamford, devendra singh chaplot, diego de las casas, florian bressand, gianna lengyel, guillaume lample, lucile saulnier, l\u00e9lio renard lavaud, marie-anne lachaux, pierre stock, teven le scao, thibaut lavril, thomas wang, timoth\u00e9e lacroix, william el sayed. arXiv."},{"key":"ref_24","unstructured":"Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., and Bhosale, S. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv."},{"key":"ref_25","first-page":"1","article-title":"Scaling instruction-finetuned language models","volume":"25","author":"Chung","year":"2024","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, C., Liu, X., Chen, Z., Hong, H., Tang, J., and Song, D. (2022, January 22\u201327). DEEPSTRUCT: Pretraining of Language Models for Structure Prediction. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022, Dublin, Ireland.","DOI":"10.18653\/v1\/2022.findings-acl.67"},{"key":"ref_27","unstructured":"Paolini, G., Athiwaratkun, B., Krone, J., Ma, J., Achille, A., Anubhai, R., Santos, C.N.D., Xiang, B., and Soatto, S. (2021, January 3\u20137). Structured prediction as translation between augmented natural languages. Proceedings of the International Conference on Learning Representations, ICLR, Virtual."},{"key":"ref_28","unstructured":"Cao, Y., Feng, Y., and Xiong, D. (2024). AutoRE: Document-Level Relation Extraction with Large Language Models. Volume 3: System Demonstrations, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand, 11\u201316 August 2024, Association for Computational Linguistics."},{"key":"ref_29","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_30","unstructured":"Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., and Liu, T. (2022). A survey on in-context learning. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, J., Jia, Z., and Zheng, Z. (2023, January 6\u201310). Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore.","DOI":"10.18653\/v1\/2023.emnlp-main.334"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, J., Xiao, S., Zhang, P., Luo, K., Lian, D., and Liu, Z. (2024). Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv.","DOI":"10.18653\/v1\/2024.findings-acl.137"},{"key":"ref_33","unstructured":"Korhonen, A., Traum, D., and M\u00e0rquez, L. Matching the Blanks: Distributional Similarity for Relation Learning. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1647\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T08:19:00Z","timestamp":1759738740000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"references-count":33,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["sym17101647"],"URL":"https:\/\/doi.org\/10.3390\/sym17101647","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]}}}