{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T15:11:28Z","timestamp":1774278688038,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Undergraduate Training Programs for Innovations by NEFU","award":["202510225416"],"award-info":[{"award-number":["202510225416"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Text-based automatic compliance checking (ACC) employs natural language processing technologies to scrutinize a corporation\u2019s business documents, ensuring adherence to related normative texts. The current methods fall into two primary categories: symbol-based and embedding-based approaches. Symbol-based methods, noted for their accuracy and transparent processing, suffer from limited versatility. Conversely, embedding-based methods operate independently of expert knowledge yet often yield challenging-to-interpret results and require substantial volumes of annotated data. While both types of methods exhibit advantages in different aspects, the current research fails to combine these advantages effectively. Therefore, the existing methods fail to balance interpretability, generalization ability, and accuracy, which are key requirements for practical compliance systems. To address this problem, we introduce a novel approach termed the Dynamic Prompt-based Logic-Aided Compliance Checker (DPLACC), which is grounded in the prompt learning framework. This method initially parses target texts, transforming the results into first-order logical expressions. It subsequently retrieves pertinent knowledge from a knowledge graph, converting the knowledge into analogous first-order logical expressions. These expressions are then encoded into a global semantic vector via a pre-trained first-order logistic encoder. Ultimately, the semantics of expressions and initial texts are amalgamated within the prompt template, facilitating the logical knowledge enhancement of model reasoning. Experiments on Chinese and English datasets demonstrate that DPLACC comprehensively outperforms existing methods based solely on symbols or embeddings in terms of accuracy, precision, recall, and F1 score and significantly surpasses current mainstream large language models. Furthermore, DPLACC exhibits enhanced interpretability and reduced data dependence, maintaining 70% checking accuracy with as few as ten training samples. This capability allows DPLACC to be rapidly deployed in data-scarce real-world scenarios with minimal annotation overhead, thus offering a practical pathway toward the scalable implementation of compliance inspection systems.<\/jats:p>","DOI":"10.3390\/bdcc10030095","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T13:53:34Z","timestamp":1774274014000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Dynamic Prompt-Based Logic-Aided Compliance Checker"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9767-050X","authenticated-orcid":false,"given":"Wenxi","family":"Sheng","sequence":"first","affiliation":[{"name":"Aulin College, Northeast Forestry University, Harbin150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Wei","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9581-1947","authenticated-orcid":false,"given":"Yinuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aulin College, Northeast Forestry University, Harbin150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bowen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aulin College, Northeast Forestry University, Harbin150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3464-2292","authenticated-orcid":false,"given":"Jingyun","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100039","DOI":"10.1016\/j.dibe.2020.100039","article-title":"The promise of automated compliance checking","volume":"5","author":"Amor","year":"2021","journal-title":"Dev. Built Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1007\/978-3-642-32885-5_21","article-title":"Where Did I Misbehave? Diagnostic Information in Compliance Checking","volume":"Volume 7481","author":"Barros","year":"2012","journal-title":"Business Process Management"},{"key":"ref_3","unstructured":"Kharbili, M.E., Medeiros, A.K.A.D., Stein, S., and van der Aalst, W.M. (2008, January 27\u201328). Business process compliance checking: Current state and future challenges. Proceedings of the Modellierung Betrieblicher Informationssysteme (MobIS 2008), Saarbr\u00fccken, Germany."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103262","DOI":"10.1016\/j.cose.2023.103262","article-title":"Automated GDPR compliance assessment for cross-border personal data transfers in android applications","volume":"130","author":"Rodriguez","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chalkidis, I., Fergadiotis, M., Manginas, N., Katakalou, E., and Malakasiotis, P. (2021). Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU\/UK legislation where text similarity has limitations. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2021.eacl-main.305"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"129648","DOI":"10.1109\/ACCESS.2021.3108226","article-title":"A Semantic Approach for Automated Rule Compliance Checking in Construction Industry","volume":"9","author":"Guo","year":"2021","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103006","DOI":"10.1016\/j.autcon.2019.103006","article-title":"Semantic approach to compliance checking of underground utilities","volume":"109","author":"Xu","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.is.2015.02.007","article-title":"Compliance monitoring in business processes: Functionalities, application, and tool-support","volume":"54","author":"Ly","year":"2015","journal-title":"Inf. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e13035","DOI":"10.1111\/exsy.13035","article-title":"NeuralConflict: Using neural networks to identify norm conflicts in normative documents","volume":"41","author":"Huang","year":"2024","journal-title":"Expert Syst."},{"key":"ref_10","unstructured":"Aires, J.P., and Meneguzzi, F. (2017). Norm conflict identification using a convolutional neural network. Proceedings of the International Workshop on Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems, Springer."},{"key":"ref_11","first-page":"317","article-title":"Modelling and accessing regulatory knowledge for computer-assisted compliance audit","volume":"21","author":"Dimyadi","year":"2016","journal-title":"J. Inf. Technol. Constr."},{"key":"ref_12","unstructured":"Pauwels, P., and Zhang, S. (2015). Semantic rule-checking for regulation compliance checking: An overview of strategies and approaches. Proceedings of the 32rd International CIB W78 Conference, Eindhoven, The Netherlands, 27\u201329 October 2015, CIB."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101288","DOI":"10.1016\/j.aei.2021.101288","article-title":"Ontology and rule-based natural language processing approach for interpreting textual regulations on underground utility infrastructure","volume":"48","author":"Xu","year":"2021","journal-title":"Adv. Eng. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"04016037","DOI":"10.1061\/(ASCE)CP.1943-5487.0000583","article-title":"Semantic-Based Logic Representation and Reasoning for Automated Regulatory Compliance Checking","volume":"31","author":"Zhang","year":"2017","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"04024049","DOI":"10.1061\/JCCEE5.CPENG-5950","article-title":"Research on a BIM Model Quality Compliance Checking Method Based on a Knowledge Graph","volume":"39","author":"Zhu","year":"2025","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Branting, K., Petersen, S., Shin, D., Finegan, J., Balhana, C., Lyte, A., and Pfeifer, C. (2019, January 17\u201321). ADEPT: Automated Directive Extraction from Policy Texts. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law, Montreal, QC, Canada.","DOI":"10.1145\/3322640.3326724"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10115-017-1142-1","article-title":"Are we done with business process compliance: State of the art and challenges ahead","volume":"57","author":"Hashmi","year":"2018","journal-title":"Knowl. Inf. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104063","DOI":"10.1016\/j.compind.2023.104063","article-title":"Neural semantic tagging for natural language-based search in building information models: Implications for practice","volume":"155","author":"Shahinmoghadam","year":"2023","journal-title":"Comput. Ind."},{"key":"ref_19","unstructured":"Hjelseth, E., and Nisbet, N. (2011, January 26\u201328). Capturing normative constraints by use of the semantic mark-up RASE methodology. Proceedings of the CIB W78-W102 Conference, Sophia Antipolis, France."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Abuzir, Y., and Abuzir, M.O. (2012). Constructing the Civil Engineering Thesaurus (CET) Using ThesWB. Proceedings of the Computing in Civil Engineering (2002), American Society of Civil Engineers.","DOI":"10.1061\/40652(2003)34"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"04015014","DOI":"10.1061\/(ASCE)CP.1943-5487.0000346","article-title":"Semantic NLP-Based Information Extraction from Construction Regulatory Documents for Automated Compliance Checking","volume":"30","author":"Zhang","year":"2016","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1061\/(ASCE)CO.1943-7862.0000131","article-title":"Concept Relation Extraction from Construction Documents Using Natural Language Processing","volume":"136","author":"Kandil","year":"2010","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.autcon.2017.02.003","article-title":"Ontology-based semi-supervised conditional random fields for automated information extraction from bridge inspection reports","volume":"81","author":"Liu","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1038\/s41597-024-04320-x","article-title":"CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking","volume":"12","author":"Hettiarachchi","year":"2025","journal-title":"Sci. Data"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Governatori, G., Milosevic, Z., and Sadiq, S. (2006). Compliance checking between business processes and business contracts. Proceedings of the 2006 10th IEEE International Enterprise Distributed Object Computing Conference (EDOC\u201906), Hong Kong, China, 16\u201320 October 2006, IEEE.","DOI":"10.1109\/EDOC.2006.22"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Firmansyah, B., and Arman, A.A. (2022). A Systematic Literature Review of RegTech: Technologies, Characteristics, and Architectures. Proceedings of the 2022 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung, Indonesia, 8\u20139 November 2022, IEEE.","DOI":"10.1109\/ICITSI56531.2022.9970830"},{"key":"ref_27","unstructured":"Thimm, H., and Schneider, P. (2022, January 26\u201328). Relation Extraction from Environmental Law Text Using Natural Language Understanding. Proceedings of the EnviroInfo 2022. Gesellschaft f\u00fcr Informatik eV, Hamburg, Germany."},{"key":"ref_28","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aires, J.P., Monteiro, J., Granada, R., and Meneguzzi, F. (2018). Norm Conflict Identification using Vector Space Offsets. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 8\u201313 July 2018, IEEE.","DOI":"10.1109\/IJCNN.2018.8489119"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Moghadasi, M.N., and Zhuang, Y. (2018). Sent2Vec: A New Sentence Embedding Representation With Sentimental Semantic. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 10\u201313 December 2020, IEEE.","DOI":"10.1109\/BigData50022.2020.9378337"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gupta, C., Huddar, N., Bhalerao, P., Patil, R.N., and Kumbhar, P. (2025, January 11\u201312). Enhancing Compliance Checking with Fine-Tuned LLMs and Retrieval\u2014Augmented Generation (RAG). Proceedings of the 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS), Bangalore, India.","DOI":"10.1109\/ICCAMS65118.2025.11234633"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"131494","DOI":"10.1016\/j.neucom.2025.131494","article-title":"TemPrompt: Multi-task prompt learning for temporal relation extraction in RAG-based crowdsourcing systems","volume":"656","author":"Yang","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1080\/07421222.2025.2561383","article-title":"Leveraging Large Language Models for Hate Speech Detection: Multi-Agent, Information-Theoretic Prompt Learning for Enhancing Contextual Understanding","volume":"42","author":"Lee","year":"2025","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1007\/s10462-025-11121-0","article-title":"A soft prompt learning method for medical text classification with simulated human cognitive capabilities","volume":"58","author":"Wang","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, X., Li, L., Zhang, N., Tan, C., Huang, F., Si, L., and Chen, H. (2022, January 11\u201315). Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain.","DOI":"10.1145\/3477495.3531746"},{"key":"ref_36","unstructured":"Kenton, J.D.M.W.C., and Toutanova, L.K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the naacL-HLT, Minneapolis, MN, USA, 2\u20137 June 2019, Association for Computational Linguistics."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Yang, H., Liu, X.Y., and Wang, C.D. (2023). FinGPT: Open-Source Financial Large Language Models. arXiv.","DOI":"10.2139\/ssrn.4489826"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.aiopen.2022.11.003","article-title":"PTR: Prompt Tuning with Rules for Text Classification","volume":"3","author":"Han","year":"2022","journal-title":"AI Open"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103029","DOI":"10.1016\/j.ipm.2022.103029","article-title":"Fake news detection via knowledgeable prompt learning","volume":"59","author":"Jiang","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"113118","DOI":"10.1016\/j.knosys.2025.113118","article-title":"KG-prompt: Interpretable knowledge graph prompt for pre-trained language models","volume":"311","author":"Chen","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Du, Z., Qian, Y., Liu, X., Ding, M., Qiu, J., Yang, Z., and Tang, J. (2022). GLM: General Language Model Pretraining with Autoregressive Blank Infilling. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics.","DOI":"10.18653\/v1\/2022.acl-long.26"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Aires, J.P., Granada, R.L., Monteiro, J., Barros, R.C., and Meneguzzi, F.R. (2019, January 13\u201317). Classification of contractual conflicts via learning of semantic representations. Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Montreal, QC, Canada.","DOI":"10.65109\/JFBH7096"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sun, C., Qiu, X., Xu, Y., and Huang, X. (2019). How to fine-tune bert for text classification?. Proceedings of the China National Conference on Chinese Computational Linguistics, Springer.","DOI":"10.1007\/978-3-030-32381-3_16"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","article-title":"Bidirectional LSTM with attention mechanism and convolutional layer for text classification","volume":"337","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rath, P.K., and Basak, R. (2020). Analysis of Deep Neural Networks for Textual Entailment Recognition. Proceedings of the 2020 Fourth International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 8\u201310 January 2020, IEEE.","DOI":"10.1109\/ICISC47916.2020.9171075"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Yanagimoto, H. (2020). Text Entailment Generation with Attention-based Sequence-to-sequence Model. Proceedings of the 2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI), Kitakyushu, Japan, 1\u201315 September 2020, IEEE.","DOI":"10.1109\/IIAI-AAI50415.2020.00029"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sun, C., Liu, Y., Jia, C., Liu, B., and Lin, L. (2017). Recognizing text entailment via bidirectional LSTM model with inner-attention. Proceedings of the International Conference on Intelligent Computing, Springer.","DOI":"10.1007\/978-3-319-63315-2_39"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/10\/3\/95\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T14:24:04Z","timestamp":1774275844000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/10\/3\/95"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,21]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["bdcc10030095"],"URL":"https:\/\/doi.org\/10.3390\/bdcc10030095","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,21]]}}}