Abstract
In the early phases of software development, both of business analysts and IT architects collaborate to define the business needs in a consistent and unambiguous format before exploiting them to produce a software solution to the problem have been defined. Given the divergence of the interest areas of each intervenor, the natural language remains the most adequate format to define the business needs in order to avoid misunderstanding. This informal support suffers from ambiguity leading to inconsistencies, which will affect the reliability of the final solution. Accordingly, the Object Management Group (OMG) has proposed the “Semantic Business Vocabulary and Rules” (SBVR) standard which offers the opportunity to gather business rules in a natural language format having a formal logic aspect, letting the possibility to be understood by not only the different stakeholders but also directly processed by the machine. Since the SBVR standard is born to represent business rules by combining business vocabulary, it would be wise to give a great attention to the latter. In this paper we present an approach to extract business vocabulary according to SBVR Structured English as one of possibly notation that can map to the SBVR Meta-Model, with a view to provide a relevant resource for the next software deployment steps.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Copyright 2003. Business Rules Group. Version 2.0, 1 November 2003. Edited by Ronald G. Ross. www.BusinessRulesGroup.org
Semantics of Business Vocabulary and Rules (SBVR), Version 1.4, Object Management Group (2017). www.omg.org/spec/SBVR/
Stanford NLP. nlp.stanford.edu/
OMG: Object Management Group. http://www.omg.org/
Jackson, D.A.: A Language & Tool for Relational Models (2012). http://alloy.mit.edu/alloy/
Object Management Group, Inc.: Unified Modeling Language (UML)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Bajwa, I.S., Lee, M.G., Bordbar, B.: SBVR business rules generation from natural language specification. Artificial Intelligence for Business Agility—Papers from the AAAI 2011 Spring Symposium (SS-11-03)
Bajwa, I.S., Lee, M., Bordbar, B., Ali, A.: Addressing semantic ambiguities in natural language constraints. In: Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference (2012)
Bajwa, I.S., Bordbar, B., Lee, M., Anastasakis, K.: NL2 alloy: a tool to generate alloy from NL constraints. J. Digital Inf. Manage. 10(6), 365–372 (2012)
Umber, A., Bajwa, I.S., Asif Naeem, M.: NL-based automated software requirements elicitation and specification. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds.) Advances in Computing and Communications, ACC 2011. Communications in Computer and Information Science, vol. 191. Springer, Heidelberg (2011)
Umber, A., Bajwa, I.S.: A step towards ambiguity less natural language software requirements specifications. IJWA 4, 12–21 (2012)
Umber, A., Bajwa, I.S.: Minimizing ambiguity in natural language software requirements specification. In: 2011 Sixth International Conference on Digital Information Management, 26–28 September 2011
Bajwa, I.S., Asif Naeem, M.: On specifying requirements using a semantically controlled representation. In: Muñoz, R., Montoyo, A., Métais, E. (eds.) Natural Language Processing and Information Systems, NLDB 2011. Lecture Notes in Computer Science, vol. 6716. Springer, Heidelberg (2011)
Ramzan, S., Bajwa, I.S., Ul Haq, I., Asif Naeem, M.: A model trans-formation from NL to SBVR. In: Ninth International Conference on Digital Information Management (ICDIM 2014), 29 September–1 October 2014
Ramzan, S., Bajwa, I.S., Ramzan, B.: A natural language metamodel for generating controlled natural language based requirements. Sci. Int. (Lahore) 28(3), 2767–2775 (2016). ISSN 1013-5316
Roychoudhury, S., Sunkle, S., Kholkar, D., Kulkarni, V.: From natural language to SBVR model authoring using structured English for compliance checking. In: 2017 IEEE 21st International Enterprise Distributed Object Computing Conference
Hypsky, R., Kreslikova, J.: Definition of business rules using business vocabulary and semantics. Acta Informatica Pragensia 6(2), 100–113 (2017). https://doi.org/10.18267/j.aip.103
Afreen, H., Bajwa, I.S.: A framework for automated object oriented analysis of natural language software specifications. Int. J. Softw. Eng. Appl. (2012)
Thakore, D.M., Patki, R.P.: Extraction of class model from software requirement using transitional SBVR format at analysis phase. Int. J. Adv. Res. Comput. Sci. 3(7) (2012). ISSN No 0976-5697
Mohanan, M., Samuel, P.: Open NLP based refinement of software requirements. Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 8, 293–300 (2016). ISSN 2150-7988
Mohanan, M., Samuel, P.: Software requirement elicitation using natural language processing. In: Snášel,V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds.) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol. 424. Springer, Cham (2016)
Njonko, P.B.F., El Abed, W.: From natural language business requirements to executable models via SBVR. In: International Conference on Systems and Informatics (ICSAI2012)
Merriam-Webster (2018). Merriam-Webster.com
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Haj, A., Balouki, Y., Gadi, T. (2019). Automatic Extraction of SBVR Based Business Vocabulary from Natural Language Business Rules. In: Khoukhi, F., Bahaj, M., Ezziyyani, M. (eds) Smart Data and Computational Intelligence. AIT2S 2018. Lecture Notes in Networks and Systems, vol 66. Springer, Cham. https://doi.org/10.1007/978-3-030-11914-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-11914-0_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11913-3
Online ISBN: 978-3-030-11914-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)