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An Approach to Mine SBVR Vocabularies and Rules from Business Documents

Authors:

Pavan Kumar Chittimalli,

Chandan Prakash,

Abhidip BhattacharyyaAuthors Info & Claims

ISEC '20: Proceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)

Article No.: 12, Pages 1 - 11

https://doi.org/10.1145/3385032.3385046

Published: 25 March 2020 Publication History

Abstract

Enterprises model the behavior of their business to prepare a communication standard for business analysts and to specify requirements to Information Technology (IT) people. The communication gap between IT group and business analysts, who lie on the opposite end of the business spectrum exists due to the different terminologies used in their respective fields regarding the same context. This gap has led to major software failures which prompted the OMG group has come up with a new standard - Semantic of Business Vocabulary and Business Rules (SBVR). Declarative models are provided by SBVR to represent Business Vocabulary and Business Rules which can be understood by everyone working throughout the business spectrum. Each business is governed by business rules which are constrained by the regulation policy set up by the policy guidelines of the organization and government regulations set up on the organization. Business rules are specified in documents like user guides, requirement documents, terms and conditions, do's and don'ts. Typically a Business Analyst interprets the document and manually extracts rules based on his understanding which leads to potential discrepancies, ambiguities and quality issues in the software system. To minimize such errors, in this paper we present an unsupervised approach to automatically extract SBVR vocabularies and rules from domain-specific business documents. We also present our initial results and comparative study with our earlier approach.

References

[1]

[n. d.]. EU-RentACar case study. http://www.businessrulesgroup.org/first_paper/br01ad.htm. [Online; accessed 18-April-2016].

[2]

[n. d.]. Object Management Group(OMG). http://www.omg.org. [Online; accessed 29-September-2015].

[3]

[n. d.]. Poor Communication Leads to Project Failure One Third of the Time. Retrieved Oct 20, 2018 from https://www.coreworx.com/pmi-study-reveals-poor-communication-leads-to-project-failure-one-third-of-the-time/

[4]

[n. d.]. Reserve Bank of India (RBI), Master Circulars. https://rbi.org.in/scripts/BS_ViewMasCirculardetails.aspx/?id=9031. [Online; accessed 18-April-2016].

[5]

[n. d.]. Semantics Of Business Vocabulary And Rules (SBVR). http://www.omg.org/spec/SBVR/. [Online; accessed 29-September-2015].

[6]

Mehdi Allahyari, Seyed Amin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, and Krys Kochut. 2017. A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques. CoRR abs/1707.02919 (2017). arXiv:1707.02919 http://arxiv.org/abs/1707.02919

[7]

Kritika Anand, Pavan Kumar Chittimalli, and Ravindra Naik. 2018. An Automated Detection of Inconsistencies in SBVR-based Business Rules Using Many-sorted Logic. In Practical Aspects of Declarative Languages, Francesco Calimeri, Kevin Hamlen, and Nicola Leone (Eds.). Springer International Publishing, Cham, 80--96.

[8]

J. Atkinson-Abutridy, C. Mellish, and S. Aitken. 2004. Combining Information Extraction with Genetic Algorithms for Text Mining. IEEE Intelligent Systems 19, 3 (May 2004), 22--30. https://doi.org/10.1109/MIS.2004.4

Digital Library

[9]

Imran Sarwar Bajwa, Mark G. Lee, and Behzad Bordbar. 2011. SBVR Business Rules Generation from Natural Language Specification. In AI for Business Agility, Papers from the 2011 AAAI Spring Symposium, Technical Report SS-11-03, Stanford, California, USA, March 21-23, 2011.

[10]

Abhidip Bhattacharyya, Pavan Kumar Chittimalli, and Ravindra Naik. 2017. An Approach to Mine Business Rule Intents from Domain-specific Documents. In Proceedings of the 10th Innovations in Software Engineering Conference (ISEC '17). ACM, New York, NY, USA, 96--106. https://doi.org/10.1145/3021460.3021470

Digital Library

[11]

Abhidip Bhattacharyya, Pavan Kumar Chittimalli, and Ravindra Naik. 2018. Relation Identification in Business Rules for Domain-specific Documents. In Proceedings of the 11th Innovations in Software Engineering Conference (ISEC '18). ACM, New York, NY, USA, Article 14, 5 pages. https://doi.org/10.1145/3172871.3172884

Digital Library

[12]

Branimir K Boguraev. 2000. Towards finite-state analysis of lexical cohesion. In Proceedings of the 3rd international conference on finite-state methods for NLP.

[13]

Peter F. Brown, Peter V. deSouza, Robert L. Mercer, Vincent J. Della Pietra, and Jenifer C. Lai. 1992. Class-based N-gram Models of Natural Language. Comput. Linguist. 18, 4 (Dec. 1992), 467--479. http://dl.acm.org/citation.cfm?id=176313.176316

Digital Library

[14]

C. C. Chiang. 2006. Extracting business rules from legacy systems into reusable components. In 2006 IEEE/SMC International Conference on System of Systems Engineering. 6 pp.-. https://doi.org/10.1109/SYSOSE.2006.1652320

[15]

Pavan Kumar Chittimalli and Kritika Anand. 2016. Domain-independent method of detecting inconsistencies in SBVR-based business rules. In Proceedings of the International Workshop on Formal Methods for Analysis of Business Systems@ASE 2016. ACM, 9--16.

Digital Library

[16]

Pavan Kumar Chittimalli, Kritika Anand, Shrishti Pradhan, Sayandeep Mitra, Chandan Prakash, Rohit Shere, and Ravindra Naik. [n. d.]. BuRRiTo: A Framework to Extract, Specify, Verify and Analyze Business Rules. Automated Software Engineering (ASE 2020).

[17]

Fabio Ciravegna. 2001. Adaptive Information Extraction from Text by Rule Induction and Generalisation. In Proceedings of the 17th International Joint Conference on Artificial Intelligence - Volume 2 (IJCAI'01). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1251--1256.

Digital Library

[18]

A. B. Earls, S. M. Embury, and N. H. Turner. 2002. A Method for the Manual Extraction of Business Rules from Legacy Source Code. BT Technology Journal 20, 4 (Oct. 2002), 127--145.

Digital Library

[19]

Charles J. Fillmore. 1968. The Case for Case, Dins. In Universals in Linguistic Theory, Emmon Bach and R. Harms (Eds.). Holt, Rinehart, and Winston.

[20]

S. Ghaisas, M. Motwani, and P.R. Anish. 2013. Detecting system use cases and validations from documents. In Automated Software Engineering (ASE), 2013 IEEE/ACM 28th International Conference on. 568--573. https://doi.org/10.1109/ASE.2013.6693114

Digital Library

[21]

Matthew Honnibal and Mark Johnson. 2015. An Improved Non-monotonic Transition System for Dependency Parsing. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Lisbon, Portugal, 1373--1378. https://aclweb.org/anthology/D/D15/D15-1162

[22]

Hai Huang. 1996. Business Rule Extraction from Legacy Code. In Proceedings of the 20th Conference on Computer Software and Applications (COMPSAC '96). IEEE Computer Society, Washington, DC, USA, 162-. http://dl.acm.org/citation.cfm?id=872750.873408

Digital Library

[23]

François Lévy and Adeline Nazarenko. 2013. Formalization of Natural Language Regulations through SBVR Structured English. In Theory, Practice, and Applications of Rules on the Web, Leora Morgenstern, Petros Stefaneas, François Lévy, Adam Wyner, and Adrian Paschke (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 19--33.

[24]

Doug McDavid. [n. d.]. The Business-IT Gap: A Key Challenge. Retrieved Oct 20, 2018 from http://www.almaden.ibm.com/coevolution/pdf/mcdavid.pdf

[25]

Ion Muslea et al. 1999. Extraction patterns for information extraction tasks: A survey. In The AAAI-99 Workshop on Machine Learning for Information Extraction, Vol. 2.

[26]

T. Nasukawa and T. Nagano. 2001. Text analysis and knowledge mining system. IBM Systems Journal 40, 4 (2001), 967--984. https://doi.org/10.1147/sj.404.0967

Digital Library

[27]

Rahul Pandita, Xusheng Xiao, Hao Zhong, Tao Xie, Stephen Oney, and Amit Paradkar. 2012. Inferring Method Specifications from Natural Language API Descriptions. In Proceedings of the 34th International Conference on Software Engineering (ICSE '12). IEEE Press, Piscataway, NJ, USA, 815--825.

Digital Library

[28]

Erik Putrycz and Anatol W. Kark. 2008. Rule Representation, Interchange and Reasoning on the Web: International Symposium, RuleML 2008, Orlando, FL, USA, October 30-31, 2008. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, Chapter Connecting Legacy Code, Business Rules and Documentation, 17--30. https://doi.org/10.1007/978-3-540-88808-6_5

Digital Library

[29]

Lawrence R Rabiner and Biing-Hwang Juang. 1986. An introduction to hidden Markov models. ASSP Magazine, IEEE 3, 1 (1986), 4--16.

[30]

Colette Rolland and Camille Ben Achour. 1998. Guiding the Construction of Textual Use Case Specifications. Data Knowl. Eng. 25, 1-2 (March 1998), 125--160. https://doi.org/10.1016/S0169-023X(97)86223-4

Digital Library

[31]

Ronald G. Ross. [n. d.]. The RuleSpeakÂ® Business Rule Notation. Retrieved Oct 20, 2018 from http://www.brcommunity.com/articles.php?id=b282

[32]

A. Sinha, A. Paradkar, P. Kumanan, and B. Boguraev. 2009. A linguistic analysis engine for natural language use case description and its application to dependability analysis in industrial use cases. In Dependable Systems Networks, 2009. DSN '09. IEEE/IFIP International Conference on. 327--336. https://doi.org/10.1109/DSN.2009.5270320

[33]

H. M. Sneed. 2001. Extracting business logic from existing COBOL programs as a basis for redevelopment. In Program Comprehension, 2001. IWPC 2001. Proceedings. 9th International Workshop on. 167--175. https://doi.org/10.1109/WPC.2001.921728

[34]

Xinyu Wang, Jianling Sun, Xiaohu Yang, Zhijun He, and S. Maddineni. 2004. Business rules extraction from large legacy systems. In Software Maintenance and Reengineering, 2004. CSMR 2004. Proceedings. Eighth European Conference on. 249--258. https://doi.org/10.1109/CSMR.2004.1281426

[35]

Xusheng Xiao, Amit Paradkar, Suresh Thummalapenta, and Tao Xie. 2012. Automated Extraction of Security Policies from Natural-language Software Documents. In Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering (FSE '12). ACM, New York, NY, USA, Article 12, 11 pages. https://doi.org/10.1145/2393596.2393608

Digital Library

[36]

Hao Zhong, Lu Zhang, Tao Xie, and Hong Mei. 2009. Inferring Resource Specifications from Natural Language API Documentation. In Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering (ASE '09). IEEE Computer Society, Washington, DC, USA, 307--318. https://doi.org/10.1109/ASE.2009.94

Digital Library

Cited By

Schumann GGómez J(2024)Detection of Contradictions and Inconsistencies in German Regulatory Documents2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692679(74-84)Online publication date: 22-Mar-2024
https://doi.org/10.1109/ICNLP60986.2024.10692679
Rajbhoj ANistala PPathan AKulkarni PKulkarni V(2023)RClassify: Combining NLP and ML to Classify Rules from Requirements Specifications Documents2023 IEEE 31st International Requirements Engineering Conference (RE)10.1109/RE57278.2023.00026(180-189)Online publication date: Sep-2023
https://doi.org/10.1109/RE57278.2023.00026
Abidin NA. Manaf N(2023)“OR” of Rule-Based Specification for Service ChoreographyServices Computing – SCC 202310.1007/978-3-031-51674-0_1(3-15)Online publication date: 17-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-51674-0_1
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Index Terms

An Approach to Mine SBVR Vocabularies and Rules from Business Documents
1. Computing methodologies
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

ISEC '20: Proceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)

February 2020

166 pages

ISBN:9781450375948

DOI:10.1145/3385032

General Chairs:
Sanjeev Jain
IIITDM Jabalpur, India
,
Atul Gupta
IIITDM Jabalpur, India
,
Program Chairs:
David Lo
SMU, Singapore
,
Diptikalyan Saha
IBM Research. India
,
Publications Chair:
Richa Sharma
Lock Haven University, USA

Copyright © 2020 ACM.

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Published: 25 March 2020

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ISEC 2020: 13th Innovations in Software Engineering Conference

February 27 - 29, 2020

Jabalpur, India

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Cited By

Schumann GGómez J(2024)Detection of Contradictions and Inconsistencies in German Regulatory Documents2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692679(74-84)Online publication date: 22-Mar-2024
https://doi.org/10.1109/ICNLP60986.2024.10692679
Rajbhoj ANistala PPathan AKulkarni PKulkarni V(2023)RClassify: Combining NLP and ML to Classify Rules from Requirements Specifications Documents2023 IEEE 31st International Requirements Engineering Conference (RE)10.1109/RE57278.2023.00026(180-189)Online publication date: Sep-2023
https://doi.org/10.1109/RE57278.2023.00026
Abidin NA. Manaf N(2023)“OR” of Rule-Based Specification for Service ChoreographyServices Computing – SCC 202310.1007/978-3-031-51674-0_1(3-15)Online publication date: 17-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-51674-0_1
Shere RChittimalli PNaik R(2022)Identifying and Extracting Hierarchical Information from Business PDF DocumentsProceedings of the 15th Innovations in Software Engineering Conference10.1145/3511430.3511440(1-11)Online publication date: 24-Feb-2022
https://dl.acm.org/doi/10.1145/3511430.3511440
Rajbhoj ANistala PKulkarni VSoni SPathan A(2022)DizSpec: Digitalization of Requirements Specification Documents to Automate Traceability and Impact Analysis2022 IEEE 30th International Requirements Engineering Conference (RE)10.1109/RE54965.2022.00030(243-254)Online publication date: Aug-2022
https://doi.org/10.1109/RE54965.2022.00030
Jiménez PCorchuelo R(2022)On the design of an advanced business rule engineSoftware: Practice and Experience10.1002/spe.311552:10(2097-2126)Online publication date: 21-Jun-2022
https://doi.org/10.1002/spe.3115
Prakash CChittimalli PNaik R(2021)Open Information Extraction Using Dependency Parser for Business Rule Mining in SBVR FormatProceedings of the 14th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)10.1145/3452383.3452396(1-11)Online publication date: 25-Feb-2021
https://dl.acm.org/doi/10.1145/3452383.3452396
Haj AJarrar ABalouki YGadir T(2021)The Semantic of Business Vocabulary and Business Rules: An Automatic Generation From Textual StatementsIEEE Access10.1109/ACCESS.2021.30716239(56506-56522)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3071623
Haj ABalouki YGadi T(2021)Automated generation of terminological dictionary from textual business rulesJournal of Software: Evolution and Process10.1002/smr.233933:5Online publication date: 26-Apr-2021
https://dl.acm.org/doi/10.1002/smr.2339

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