{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:34:24Z","timestamp":1779294864582,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Semantics of Business Vocabulary and Rules (SBVR) is a standard that is applied in describing business knowledge in the form of controlled natural language. Business process designers develop SBVR from formal documents and later translate it into business process models. In many immature companies, these documents are often unavailable and could hinder resource efficiency efforts. This study introduced a novel approach called informal document to SBVR (ID2SBVR). This approach is used to extract operational rules of SBVR from informal documents. ID2SBVR mines fact type candidates using word patterns or extracting triplets (actor, action, and object) from sentences. A candidate fact type can be a complex, compound, or complex-compound sentence. ID2SBVR extracts fact types from candidate fact types and transforms them into a set of SBVR operational rules. The experimental results show that our approach can be used to generate the operational rules of SBVR from informal documents with an accuracy of 0.91. Moreover, ID2SBVR can also be used to extract fact types with an accuracy of 0.96. The unstructured data is successfully converted into semi-structured data for use in pre-processing. ID2SBVR allows the designer to automatically generate business process models from informal documents.<\/jats:p>","DOI":"10.3390\/bdcc6040119","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T20:32:23Z","timestamp":1666211543000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["ID2SBVR: A Method for Extracting Business Vocabulary and Rules from an Informal Document"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2926-7839","authenticated-orcid":false,"given":"Irene","family":"Tangkawarow","sequence":"first","affiliation":[{"name":"Informatics Department, Faculty of Engineering, Universitas Negeri Manado, Minahasa 95618, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riyanarto","family":"Sarno","sequence":"additional","affiliation":[{"name":"Informatics Department, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6560-2975","authenticated-orcid":false,"given":"Daniel","family":"Siahaan","sequence":"additional","affiliation":[{"name":"Informatics Department, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.jss.2016.09.046","article-title":"Model-Based M2M Transformations Based on Drag-and-Drop Actions: Approach and Implementation","volume":"122","author":"Skersys","year":"2016","journal-title":"J. Syst. Softw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lopez, H.A., Marquard, M., Muttenthaler, L., and Stromsted, R. (2019, January 28\u201331). Assisted Declarative Process Creation from Natural Language Descriptions. Proceedings of the IEEE International Enterprise Distributed Object Computing Workshop, EDOCW, Paris, France.","DOI":"10.1109\/EDOCW.2019.00027"},{"key":"ref_3","first-page":"20","article-title":"Research on the Application of Data Mining Method Based on Decision Tree in CRM","volume":"21","author":"Deng","year":"2018","journal-title":"J. Adv. Oxid. Technol."},{"key":"ref_4","first-page":"67","article-title":"Influence of Structured, Semi-Structured, Unstructured Data on Various Data Models","volume":"8","author":"Praveen","year":"2017","journal-title":"Int. J. Isc. Eng. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102095","DOI":"10.1016\/j.ipm.2019.102095","article-title":"Big Data Adoption: State of the Art and Research Challenges","volume":"56","author":"Baig","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_6","unstructured":"McConnell, C.R., and Fallon, F.L. (2013). Human Resource Management in Health Care, Jones & Bartlett Publishers."},{"key":"ref_7","first-page":"281","article-title":"Modeling Business Rule Parallelism by Introducing Inclusive and Complex Gateways in Semantics of Business Vocabulary and Rules","volume":"14","author":"Tangkawarow","year":"2021","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_8","first-page":"360","article-title":"Transforming BPMN 2.0 Business Process Model into SBVR Business Vocabulary and Rules","volume":"46","author":"Mickeviciute","year":"2017","journal-title":"Inf. Technol. Control"},{"key":"ref_9","unstructured":"Mickeviciute, E., Skersys, T., Nemuraite, L., and Butleris, R. (2015, January 4\u201316). SBVR Business Vocabulary and Rules Extraction from BPMN Business Process Models. Proceedings of the 8th IADIS International Conference Information Systems 2015, IS 2015, Funchal, Portugal."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101822","DOI":"10.1016\/j.datak.2020.101822","article-title":"Natural Language Processing-Enhanced Extraction of SBVR Business Vocabularies and Business Rules from UML Use Case Diagrams","volume":"128","author":"Danenas","year":"2020","journal-title":"Data Knowl. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.jss.2018.03.061","article-title":"Extracting SBVR Business Vocabularies and Business Rules from UML Use Case Diagrams","volume":"141","author":"Skersys","year":"2018","journal-title":"J. Syst. Softw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/978-3-319-26832-3_12","article-title":"A Graph Processing Based Approach for Automatic Detection of Semantic Inconsistency between BPMN Process Model and SBVR Rules","volume":"Volume 9468","author":"Mishra","year":"2015","journal-title":"Mining Intelligence and Knowledge Exploration"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dumas, M., La Rosa, M., Mendling, J., and Reijers, H.A. (2013). Fundamentals of Business Process Management, Springer.","DOI":"10.1007\/978-3-642-33143-5"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102237","DOI":"10.1016\/j.ipm.2020.102237","article-title":"Business Process Improvement and the Knowledge Flows That Cross a Private Online Social Network: An Insurance Supply Chain Case","volume":"57","author":"Leon","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1007\/s10796-018-9826-y","article-title":"Formal Model of Business Processes Integrated with Business Rules","volume":"21","author":"Kluza","year":"2019","journal-title":"Inf. Syst. Front."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.infsof.2017.07.001","article-title":"A Method for Generation and Design of Business Processes with Business Rules","volume":"91","author":"Kluza","year":"2017","journal-title":"Inf. Softw. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Aiello, G., Di Bernardo, R., Maggio, M., Di Bona, D., and Re, G.L. (2014, January 17\u201319). Inferring Business Rules from Natural Language Expressions. Proceedings of the IEEE 7th International Conference on Service-Oriented Computing and Applications, SOCA 2014, Matsue, Japan.","DOI":"10.1109\/SOCA.2014.39"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/978-981-13-6052-7_33","article-title":"Generating SBVR-XML Representation of a Controlled Natural Language","volume":"Volume 932","author":"Arshad","year":"2019","journal-title":"Communications in Computer and Information Science"},{"key":"ref_19","first-page":"618","article-title":"Generating RDFS Based Knowledge Graph from SBVR","volume":"Volume 932","author":"Akhtar","year":"2019","journal-title":"Intelligent Technologies and Applications. INTAP 2018. Communications in Computer and Information Science"},{"key":"ref_20","first-page":"6","article-title":"Natural Language Processing Approach for UML Class Model Generation from Software Requirement Specifications via SBVR","volume":"27","author":"Mohanan","year":"2018","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.2298\/CSIS140106079S","article-title":"Extracting Business Vocabularies from Business Process Models: SBVR and BPMN Standards-Based Approach","volume":"11","author":"Skersys","year":"2014","journal-title":"Comput. Sci. Inf. Syst."},{"key":"ref_22","unstructured":"Tantan, O., and Akoka, J. (2014, January 1\u20133). Automated Transformation of Business Rules into Business Processes From SBVR to BPMN. Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, Vancouver, BC, Canada."},{"key":"ref_23","first-page":"703","article-title":"SBVRwiki (Tool Presentation)","volume":"1289","author":"Kluza","year":"2014","journal-title":"CEUR Workshop Proc."},{"key":"ref_24","first-page":"453","article-title":"From SBVR to BPMN and DMN Models. Proposal of Translation from Rules to Process and Decision Models","volume":"Volume 9693","author":"Kluza","year":"2016","journal-title":"Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016"},{"key":"ref_25","first-page":"38","article-title":"BPM2Text: A Language Independent Framework for Business Process Models to Natural Language Text","volume":"10","author":"Rodrigues","year":"2016","journal-title":"ISys-Braz. J. Inf. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ferreira, R.C.B., Thom, L.H., and Fantinato, M. (2017, January 26\u201329). A Semi-Automatic Approach to Identify Business Process Elements in Natural Language Texts. Proceedings of the ICEIS 2017: 19th International Conference on Enterprise Information Systems, Porto, Portugal.","DOI":"10.5220\/0006305902500261"},{"key":"ref_27","first-page":"1","article-title":"NLP4BPM\u2014Natural Language Processing Tools for Business Process Management","volume":"1920","author":"Delicado","year":"2017","journal-title":"CEUR Workshop Proc."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Iqbal, U., and Bajwa, I.S. (2017). Generating UML Activity Diagram from SBVR Rules. 2016 6th International Conference on Innovative Computing Technology, INTECH 2016, IEEE Xplore.","DOI":"10.1109\/INTECH.2016.7845094"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"70","DOI":"10.4018\/IJOSSP.2019040104","article-title":"Requirements to Class Model via SBVR","volume":"10","author":"Mohanan","year":"2019","journal-title":"Int. J. Open Source Softw. Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.is.2019.02.001","article-title":"From BPMN Process Models to DMN Decision Models","volume":"83","author":"Bazhenova","year":"2019","journal-title":"Inf. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/978-3-319-77028-4_65","article-title":"A Controlled Natural Language Editor for Semantic of Business Vocabulary and Rules","volume":"Volume 738","year":"2018","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"ref_32","unstructured":"Object Management Group, OMG, and Object Management Group (2021, January 20). Semantics of Business Vocabulary and Business Rules Version 1.0. Available online: https:\/\/www.omg.org\/spec\/SBVR\/1.5\/PDF."},{"key":"ref_33","unstructured":"Object Management Group (2021, January 23). Semantics of Business Vocabulary and Business Rules V.1.5 Annex H- The RuleSpeak Business Rule Notation. Available online: https:\/\/www.omg.org\/spec\/SBVR\/1.5\/About-SBVR\/."},{"key":"ref_34","unstructured":"Business Rule Solutions, L., and RuleSpeak (2021, March 12). Business Rule Solutions, LLC. Available online: http:\/\/www.rulespeak.com\/en\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"19","DOI":"10.5296\/ijl.v10i2.12916","article-title":"A Cross-Disciplinary Investigation of Textual Metadiscourse Markers in Academic Writing","volume":"10","author":"Aluthman","year":"2018","journal-title":"Int. J. Linguist."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1016\/j.ipm.2018.08.001","article-title":"Semantic Text Classification: A Survey of Past and Recent Advances","volume":"54","author":"Ganiz","year":"2018","journal-title":"Inf. Process. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.esp.2018.12.002","article-title":"A Functional Analysis of Text-Oriented Formulaic Expressions in Written Academic Discourse: Multiword Sequences vs. Single Words","volume":"54","author":"Wang","year":"2019","journal-title":"Engl. Specif. Purp."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ipm.2013.08.006","article-title":"The Impact of Preprocessing on Text Classification","volume":"50","author":"Uysal","year":"2014","journal-title":"Inf. Process. Manag."},{"key":"ref_39","unstructured":"Steven, B., Loper, E., and Klein, E. (2009). Natural Language Processing with Python*, O\u2019Reilly Media Inc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.sbspro.2015.04.116","article-title":"Determining the Intonation Contours of Compound-Complex Sentences Uttered by Turkish Prospective Teachers of English","volume":"186","author":"Demirezen","year":"2015","journal-title":"Procedia-Soc. Behav. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107219","DOI":"10.1016\/j.asoc.2021.107219","article-title":"How to Design the Fair Experimental Classifier Evaluation","volume":"104","author":"Stapor","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.ipm.2018.01.002","article-title":"Correlation Analysis of Performance Measures for Multi-Label Classification","volume":"54","author":"Pereira","year":"2018","journal-title":"Inf. Process. Manag."},{"key":"ref_43","first-page":"349","article-title":"Neural Style Transfer and Geometric Transformations for Data Augmentation on Balinese Carving Recognition Using MobileNet","volume":"13","author":"Darma","year":"2020","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"102434","DOI":"10.1016\/j.ipm.2020.102434","article-title":"User and Item-Aware Estimation of Review Helpfulness","volume":"58","author":"Mauro","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_45","unstructured":"Joshi, B., Macwan, N., Mistry, T., and Mahida, D. (2018, January 9). Text Mining and Natural Language Processing in Web Data Mining. Proceedings of the 2nd International Conference on Current Research Trends in Engineering and Technology, Gujarat, India."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102060","DOI":"10.1016\/j.ipm.2019.102060","article-title":"Fuzzy Topic Modeling Approach for Text Mining over Short Text","volume":"56","author":"Rashid","year":"2019","journal-title":"Inf. Process. Manag."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/6\/4\/119\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:57:40Z","timestamp":1760144260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/6\/4\/119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,19]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["bdcc6040119"],"URL":"https:\/\/doi.org\/10.3390\/bdcc6040119","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,19]]}}}