{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T05:15:56Z","timestamp":1762665356037,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["4222022"],"award-info":[{"award-number":["4222022"]}]},{"name":"National Key Research and Development Program of China","award":["2020YFB2104402"],"award-info":[{"award-number":["2020YFB2104402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Event\u2013event relation extraction (ERE) is an important and challenging task in natural language processing. At present, state-of-the-art ERE methods mainly adopt supervised learning, especially deep learning, which needs a large number of high-quality labeled event corpora. However, these methods will face the challenge of few-shot learning for extracting Chinese multiple event\u2013event relations. Complex deep learning models often cannot converge effectively on small Chinese event corpora. And the manual event relation labeling is a very time-consuming and uncertain work. This paper proposes a joint extraction model for multiple Chinese event\u2013event relations based on weighted double consistency constraint learning, named the Chinese event\u2013event relations miner (CERMiner), to extract multiple types of Chinese emergency event\u2013event relations jointly. After encoding event pairs from their contexts, a group of weighted double consistency constraint, including common sense constraints and domain constraints, are designed and integrated into model learning to accelerate model convergence on few-shot corpora. To evaluate the effectiveness of the CERMiner model, we conduct experiments on the CEC dataset, which contains three relation types\u2014CE, EC, and AC\u2014with 697, 200, and 242 instances, respectively. We report Precision, Recall, and F1-score as evaluation metrics. Our method achieves 84.8%, 72.7%, and 78.2% in Precision, Recall, and F1-score, respectively, outperforming the SGT baseline by 1.7% in F1-score. These results demonstrate that the proposed model can better realize joint extraction of multiple Chinese emergency event\u2013event relations in low-resource environments compared to existing state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/sym17111910","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T14:55:15Z","timestamp":1762527315000},"page":"1910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Joint Extraction Model of Multiple Chinese Emergency Event\u2013Event Relations Based on Weighted Double Consistency Constraint Learning"],"prefix":"10.3390","volume":"17","author":[{"given":"Jianhui","family":"Chen","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyi","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0396-3542","authenticated-orcid":false,"given":"Lianfang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zitong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haonan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102024","DOI":"10.1016\/j.ijdrr.2020.102024","article-title":"CrowdEIM: Crowdsourcing emergency information management tasks to the mobile social media users","volume":"54","author":"Shen","year":"2021","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s10462-025-11203-z","article-title":"Web intelligence (wi) 3.0: In search of a better-connected world to create a future intelligent society","volume":"58","author":"Kuai","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17369","DOI":"10.1007\/s00500-023-08882-7","article-title":"Bomedical event causal relation extraction based on a knowledge-guided hierarchical graph network","volume":"27","author":"Zhang","year":"2023","journal-title":"Soft Comput."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yao, H.-R., Breitfeller, L., Naik, A., Zhou, C., and Rose, C. (2024, January 21\u201325). Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM \u201924), Boise, ID, USA.","DOI":"10.1145\/3627673.3679520"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yong, S.J., Dong, K., and Sun, A. (2024, January 12\u201316). DOCoR: Document-level OpenIE with Coreference Resolution. Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, Kyoto, Japan.","DOI":"10.1145\/3539597.3573038"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/TASLP.2022.3153256","article-title":"Exploit feature and relation hierarchy for relation extraction","volume":"30","author":"Zhang","year":"2022","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_7","first-page":"2220","article-title":"Earthquake Information Extraction and Comparison from Different Sources Based on Web Text","volume":"252","author":"Han","year":"2019","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xiao, H., Zheng, S., and Chen, X.Y. (2023, January 15\u201317). Temporal Relationship Extraction of Conflict Events in Open Source Military Journalism. Proceedings of the 2023 2nd International Conference on Artificial Intelligence and Computer Information Technology (AICIT), Yichang, China.","DOI":"10.1109\/AICIT59054.2023.10277795"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Qiu, J., and Sun, L. (2024, January 13\u201316). A Joint Graph Neural Model for Chinese Domain Event and Relation Extraction with Character-Word Fusion. Proceedings of the 2024 10th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC62609.2024.10942111"},{"key":"ref_10","unstructured":"Prasad, R., Dinesh, N., Lee, A., Miltsakaki, E., Robaldo, L., Joshi, A., and Webber, B. (2008, January 28\u201330). The Penn Discourse TreeBank 2.0. Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC\u201908), Marrakech, Morocco."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1093\/llc\/13.4.177","article-title":"Automatic Extraction of Cause-Effect Information from Newspaper Text Without Knowledge-based Inferencing","volume":"13","author":"Khoo","year":"1998","journal-title":"Lit. Linguist. Comput."},{"key":"ref_12","unstructured":"Nichols, M. (2008). Efficient Pattern Search in Large, Partial-Order Data Sets. [Ph.D. Thesis, University of Waterloo]."},{"key":"ref_13","unstructured":"Shen, J., Wu, Z., Lei, D., Shang, J., Ren, X., and Han, J. (2019). SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chang, D.-S., and Choi, K.-S. (2004, January 22\u201324). Causal relation extraction using cue phrase and lexical pair probabilities. Proceedings of the First International Joint Conference on Natural Language Processing, Hainan Island, China.","DOI":"10.1007\/978-3-540-30211-7_7"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.ipm.2009.09.002","article-title":"A knowledge-rich approach to identifying semantic relations between nominals","volume":"46","author":"Girju","year":"2010","journal-title":"Inf. Process. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., and Wang, W. (2013). Convolution Neural Network for Relation Extraction. Advanced Data Mining and Applications, Springer.","DOI":"10.1007\/978-3-642-53917-6"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"79","DOI":"10.26599\/TST.2020.9010063","article-title":"Event Temporal Relation Extractionwith Attention Mechanism and Graph Neural Network","volume":"27","author":"Xu","year":"2022","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, T., and Wang, Z. (2023, January 5\u20137). LDRC: Long-tail Distantly Supervised Relation Extraction via Contrastive Learning. Proceedings of the 2023 7th International Conference on Machine Learning and Soft Computing, Chongqing, China.","DOI":"10.1145\/3583788.3583804"},{"key":"ref_19","first-page":"11058","article-title":"Selecting Optimal Context Sentences for Event-Event Relation Extraction","volume":"36","author":"Man","year":"2025","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"El-allaly, E.-d., Sarrouti, M., and En-Nahnahi, N. (2022). An attentive joint model with transformer-based weighted graph convolutional network for extracting adverse drug event relation. J. Biomed. Inform., 125.","DOI":"10.1016\/j.jbi.2021.103968"},{"key":"ref_21","first-page":"24141","article-title":"Logic Induced High-Order Reasoning Network for Event-Event Relation Extraction","volume":"39","author":"Huang","year":"2025","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, M., Cao, Y., Zhang, Y., and Liu, Z. (2023, January 9\u201314). CHEER: Centrality-aware High-order Event Reasoning Network for Document-level Event Causality Identification. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto, ON, Canada.","DOI":"10.18653\/v1\/2023.acl-long.604"},{"key":"ref_23","unstructured":"Li, P., Zhu, Q., Zhou, G., and Wang, H. (2016, January 11\u201316). Global Inference to Chinese Temporal Relation Extraction. Proceedings of the COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1109\/TIV.2022.3160502","article-title":"Relationship Extraction Method for Urban Rail Transit Operation Emergencies Records","volume":"8","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.ins.2023.01.143","article-title":"CFERE: Multi-type Chinese financial event relation extraction","volume":"630","author":"Wan","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1007\/s11517-024-03031-0","article-title":"Multi-contrast learning-guided lightweight few-shot learning scheme for predicting breast cancer molecular subtypes","volume":"62","author":"Pan","year":"2024","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9864","DOI":"10.1007\/s10489-024-05670-0","article-title":"Pseudo-label meta-learner in semi-supervised few-shot learning for remote sensing image scene classification","volume":"54","author":"Miao","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.patrec.2022.06.012","article-title":"Baby steps towards few-shot learning with multiple semantics","volume":"160","author":"Schwartz","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"225088","DOI":"10.1109\/ACCESS.2020.3042672","article-title":"A Neural Relation Extraction Model for Distant Supervision in Counter-Terrorism Scenario","volume":"8","author":"Hou","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, S., and Song, D. (2022, January 20\u201323). FPC: Fine-tuning with Prompt Curriculum for Relation Extraction. 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 Only.","DOI":"10.18653\/v1\/2022.aacl-main.78"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, Y. (2021). A transfer learning model with multi-source domains for biomedical event trigger extraction. BMC Genom., 22.","DOI":"10.1186\/s12864-020-07315-1"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Parulian, N., Ji, H., Elsayed, A., Myers, S., and Palmer, M. (2021, January 1\u20136). Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Online.","DOI":"10.18653\/v1\/2021.acl-long.489"},{"key":"ref_33","unstructured":"Yuan, l., Cai, Y., and Huang, J. (November, January 28). Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model. Proceedings of the 32nd ACM International Conference on Multimedia, Melbourne, Australia."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.ymeth.2024.08.007","article-title":"Biomedical event causal relation extraction with deep knowledge fusion and Roberta-based data augmentation","volume":"231","author":"Li","year":"2024","journal-title":"Methods"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Giunchiglia, E., Stoian, M.C., and Lukasiewicz, T. (2022). Deep Learning with Logical Constraints. arXiv.","DOI":"10.24963\/ijcai.2022\/767"},{"key":"ref_36","first-page":"5700","article-title":"MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks","volume":"36","author":"Hoernle","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_37","unstructured":"Dovier, A., Montanari, A., and Orlandini, A. (2019). Knowledge Enhanced Neural Networks for Relational Domains. PRICAI 2019: Trends in Artificial Intelligence, Proceedings of the Pacific Rim International Conference on Artificial Intelligence Cuvu, Yanuka Island, Fiji, 26 August 2019, Springer International Publishing."},{"key":"ref_38","unstructured":"Webber, B., Cohn, T., He, Y., and Liu, Y. (2020, January 6\u201320). Joint Constrained Learning for Event-Event Relation Extraction. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online."},{"key":"ref_39","first-page":"189","article-title":"Research on Event-oriented Ontology Model","volume":"36","author":"Liu","year":"2009","journal-title":"Comput. Sci."},{"key":"ref_40","first-page":"791","article-title":"Topic Representation Integrated with Event Knowledge","volume":"40","author":"Sun","year":"2017","journal-title":"Chin. J. Comput."},{"key":"ref_41","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv."},{"key":"ref_42","unstructured":"Chen, Z., Badrinarayanan, V., Lee, C.-Y., and Rabinovich, A. (2017). GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks. arXiv."},{"key":"ref_43","unstructured":"Riloff, E., Chiang, D., Hockenmaier, J., and Tsujii, J. (, January 2\u20134). Graph Convolution over Pruned Dependency Trees Improves Relation Extraction. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.neucom.2021.12.044","article-title":"BERT gated multi-window attention network for relation extraction","volume":"492","author":"Xu","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_45","unstructured":"Riloff, E., Chiang, D., Hockenmaier, J., and Tsujii, J. (2018, January 2\u20134). Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium."},{"key":"ref_46","unstructured":"Carpuat, M., Marneffe, M.-C., and Meza Ruiz, I.V. (2022). Extracting Temporal Event Relation with Syntax-guided Graph Transformer. Findings of the Association for Computational Linguistics: NAACL 2022, Association for Computational Linguistics."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/11\/1910\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T05:11:53Z","timestamp":1762665113000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/11\/1910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":46,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["sym17111910"],"URL":"https:\/\/doi.org\/10.3390\/sym17111910","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,11,7]]}}}