{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:21:24Z","timestamp":1760149284819,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China Major Program","award":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"],"award-info":[{"award-number":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"],"award-info":[{"award-number":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Planning Project of Jilin Province","award":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"],"award-info":[{"award-number":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"]}]},{"name":"Guangdong Universities\u2019 Innovation Team Project","award":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"],"award-info":[{"award-number":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"]}]},{"name":"Guangdong Key Disciplines Project","award":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"],"award-info":[{"award-number":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"]}]},{"name":"Projects of CNPC","award":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"],"award-info":[{"award-number":["2021ZD0112500","61972174","62172187","20220201145GX","20200708112YY","20220601112FG","2021KCXTD015","2021ZDJS138","2021DQ0503","2020B-4019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Managed pressure drilling (MPD) is the most effective means to ensure drilling safety, and MPD is able to avoid further deterioration of complex working conditions through precise control of the wellhead back pressure. The key to the success of MPD is the well control strategy, which currently relies heavily on manual experience, hindering the automation and intelligence process of well control. In response to this issue, an MPD knowledge graph is constructed in this paper that extracts knowledge from published papers and drilling reports to guide well control. In order to improve the performance of entity extraction in the knowledge graph, a few-shot Chinese entity recognition model CEntLM-KL is extended from the EntLM model, in which the KL entropy is built to improve the accuracy of entity recognition. Through experiments on benchmark datasets, it has been shown that the proposed model has a significant improvement compared to the state-of-the-art methods. On the few-shot drilling datasets, the F-1 score of entity recognition reaches 33%. Finally, the knowledge graph is stored in Neo4J and applied for knowledge inference.<\/jats:p>","DOI":"10.3390\/e25071097","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T00:47:03Z","timestamp":1690159623000},"page":"1097","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain"],"prefix":"10.3390","volume":"25","author":[{"given":"Siqing","family":"Wei","sequence":"first","affiliation":[{"name":"Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1147-3968","authenticated-orcid":false,"given":"Yanchun","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun 130012, China"},{"name":"Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Science and Technology, Zhuhai 519041, China"}]},{"given":"Xiaoran","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Xiaohui","family":"Weng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130012, China"}]},{"given":"Jiasheng","family":"Fu","sequence":"additional","affiliation":[{"name":"CNPC Engineering Technology R&D Company Limited, National Engineering Research Center of Oil & Gas Drilling and Completion Technology, Beijing 102206, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1088-7998","authenticated-orcid":false,"given":"Xiaosong","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dai, Z., Wang, X., Ni, P., Li, Y., Li, G., and Bai, X. (2019, January 19\u201321). Named entity recognition using BERT BiLSTM CRF for Chinese electronic health records. Proceedings of the 2019 12th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI), Suzhou, China.","DOI":"10.1109\/CISP-BMEI48845.2019.8965823"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cui, L., Wu, Y., Liu, J., Yang, S., and Zhang, Y. (2021, January 1\u20136). Template-Based Named Entity Recognition Using BART. Proceedings of the Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Online.","DOI":"10.18653\/v1\/2021.findings-acl.161"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fritzler, A., Logacheva, V., and Kretov, M. (2019, January 8\u201312). Few-shot classification in named entity recognition task. Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, Limassol, Cyprus.","DOI":"10.1145\/3297280.3297378"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4245","DOI":"10.1109\/TKDE.2020.3038670","article-title":"Few-Shot Named Entity Recognition via Meta-Learning","volume":"34","author":"Li","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ma, T., Jiang, H., Wu, Q., Zhao, T., and Lin, C.-Y. (2022, January 22\u201327). Decomposed Meta-Learning for Few-Shot Named Entity Recognition. Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland.","DOI":"10.18653\/v1\/2022.findings-acl.124"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, J., Liu, Q., Lin, H., Han, X., and Sun, L. (2022, January 22\u201327). Few-shot Named Entity Recognition with Self-describing Networks. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland.","DOI":"10.18653\/v1\/2022.acl-long.392"},{"key":"ref_7","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Schick, T., and Sch\u00fctze, H. (2021, January 19\u201323). Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, Online. Main Volume.","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Schick, T., and Sch\u00fctze, H. (2021, January 6\u201311). It\u2019s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. 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.185"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shin, T., Razeghi, Y., Logan, R.L., Wallace, E., and Singh, S. (2020, January 16\u201320). AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online.","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gao, T., Fisch, A., and Chen, D. (2021, January 1\u20136). Making Pre-trained Language Models Better Few-shot Learners. 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), Online.","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"ref_12","unstructured":"Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., and Tang, J. (2021). GPT understands, too. arXiv, preprint."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.procs.2016.09.123","article-title":"Named entity recognition over electronic health records through a combined dictionary-based approach","volume":"100","author":"Quimbaya","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ma, R., Zhou, X., Gui, T., Tan, Y., Li, L., Zhang, Q., and Huang, X. (2021). Template-free prompt tuning for few-shot NER. arXiv, preprint.","DOI":"10.18653\/v1\/2022.naacl-main.420"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wiseman, S., Shieber, S., and Rush, A. (November, January 31). Learning Neural Templates for Text Generation. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1356"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, D., Chen, Z., He, W., Zhong, L., Tao, Y., and Yang, M. (2021, January 5). A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems. Proceedings of the 1st Workshop on Document-Grounded Dialogue and Conversational Question Answering (DialDoc 2021), Online.","DOI":"10.18653\/v1\/2021.dialdoc-1.3"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"117725","DOI":"10.1016\/j.eswa.2022.117725","article-title":"A hereditary attentive template-based approach for complex Knowledge Base Question Answering systems","volume":"205","author":"Gomes","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2018.05.001","article-title":"A content-based recommender system for computer science publications","volume":"157","author":"Wang","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3669","DOI":"10.1109\/TKDE.2020.3028943","article-title":"Deep feature-based text clustering and its explanation","volume":"34","author":"Guan","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ma, J., Ballesteros, M., Doss, S., Anubhai, R., Mallya, S., Al-Onaizan, Y., and Roth, D. (2022, January 22\u201327). Label Semantics for Few Shot Named Entity Recognition. Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland.","DOI":"10.18653\/v1\/2022.findings-acl.155"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/7\/1097\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:17:02Z","timestamp":1760127422000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/7\/1097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,22]]},"references-count":20,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["e25071097"],"URL":"https:\/\/doi.org\/10.3390\/e25071097","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2023,7,22]]}}}