An Approach to Mine SBVR Vocabularies and Rules from Business Documents

Chittimalli, Pavan Kumar; Prakash, Chandan; Naik, Ravindra; Bhattacharyya, Abhidip

doi:10.1145/3385032.3385046

Cited by 10 publications

(8 citation statements)

References 21 publications

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“…T. Skersys [24] propose an hybrid approach to extract general concepts, verb concepts, and SBVR statements from formal use case, enhanced later by P.Danenas [25] using an NLP-based approach instead of purely template-based rules. P. K. Chittimalli [26] propose an approach to mine SBVR based business vocabularies and rules from domainspecific business documents. They try to automatically identify entities and relations between them using heuristics, before generating business rules.…”

Section: B Natural Languaue To Sbvrmentioning

confidence: 99%

“…Table 1 is a comparative between approaches generating SBVR from textual specifications. [29] 2017 legal English text SBVR model semi-automatic I. S. Bajwa [23] 2017 Textual specification BR statements, then OCL constraints semi-automatic T. Skersys [24] 2018 Use Case Diagram General concept, verb concepts, and BR statements semi-automatic P. Danenas [25] 2020 P. K. Chittimalli [26] 2020 Textual business rules Entities, facts, and BR statements Automatic Our approach Textual business rules SBVR model: Terminological Dictionary: noun concepts and verb concepts with their specifications such as definitions, synonyms, and generalization relations. BR statements: the semantic formulation such as modal formulation, atomic formulation, instantiation formulation, negation, quantification, implication, and conjunctions.…”

Section: B Natural Languaue To Sbvrmentioning

confidence: 99%

“…Given that business rules are usually written by different intervenors, it is highly probable that writers use different expressions having same meaning or use abbreviations instead of the hole multi words noun. To deal with this issue, there is no single agreed method that is valid for all contexts, but existing approaches generally tend to ignore this problem such as P. K. Chittimalli [26], or manually mark terms having same meaning such as M. Selway [21], S. Roychoudhury [29], I. S. Bajwa [23], or trust only dictionary database to identify them T. Skersys [24], P. Danenas [25].…”

Section: − Synonyms and Preferred Designationmentioning

confidence: 99%

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The Semantic of Business Vocabulary and Business Rules: An Automatic Generation From Textual Statements

et al. 2021

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In the early phases of the software development process, specifications are mostly written in a natural language rather than formal models, which is not supported by the Model Driven Architecture (MDA). For this reason, the Semantic of Business Vocabulary and Rules (SBVR) is proposed by the Object Management Group to represent the textual specifications in a language comprehensible by both of humans and machines, to facilitate its integration in the MDA lifecycle. However, businesspeople are usually not familiar with SBVR standard. In this paper we present an approach to automatically transform textual business rules to an SBVR model, to facilitate its integration in nowadays information technology infrastructures. Our approach is distinguished from existing works in that it uses an in-depth Natural Language Processing to extract a more comprehensible SBVR model that includes the semantic formulation of each business rule statement, coupled with a Terminological Dictionary of extracted concepts, to which we have added further specifications such as definitions and synonyms. The evaluation of our approach shows that for three sets of business rules statements taken from different domains, we could generate the correct meaning with an average of F1-score exceeding 87%.

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Section: B Natural Languaue To Sbvrmentioning

confidence: 99%

Section: B Natural Languaue To Sbvrmentioning

confidence: 99%

Section: − Synonyms and Preferred Designationmentioning

confidence: 99%

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The Semantic of Business Vocabulary and Business Rules: An Automatic Generation From Textual Statements

et al. 2021

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“…Chittimalli et al 13 present an approach to mine business vocabularies and rules according to SBVR meta‐model from domain‐specific business documents. The extraction of entities and facts is fully automatic using a well‐formed heuristics.…”

Section: Related Workmentioning

confidence: 99%

Automated generation of terminological dictionary from textual business rules

Haj

Balouki

Gadi

2021

J Software Evolu Process

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To support decision making, organizations tend to operate according to Business Rules, which are usually represented in a natural language format easily understood by all intervenors. According to the business rules manifesto by the Business Rules Group (OMG), rules build on facts, and facts build on concepts as expressed by terms. To avoid ambiguity and misunderstanding, the standardization of the terminology used at the business level becomes a persistent need. However, doing so manually is error prone and time consuming, especially that the Business Rules are the subject of continuous updating. In this paper, we present an automated approach to generate the Business Vocabulary from textual statements of Business Rules. Our approach is distinguished from existing works in that it extracts the Terminological Dictionary as described by the Semantic of Business Vocabulary and Rules (SBVR) standard to provide a more comprehensive meaning for each concept. Accordingly, an in‐depth Natural Language Processing (NLP) is used to extract not only flat list of terms and relations, but also extra specifications and implicit knowledge. With a satisfactory result, our approach has proved its capability to automatically generate the SBVR Terminological Dictionary from large number of natural language business rules statements.

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“…BRs are generally neither few statements to be manually processed, nor enough to be processed using machine learning methods. Probably, this justifies the fact that approaches tend to ignore the existence of synonyms [4], or provide a user interface (UI) to add theme manually [5], or assume the prior existence of a domain knowledge containing the business vocabulary [6]. While other approaches try to identify synonyms at advanced steps of the development process [7].…”

Section: Introductionmentioning

confidence: 99%

Automated Identification of Semantic Similarity between Concepts of Textual Business Rules

Haj¹,

Balouki²,

Gadi³

2021

IJIES

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Business Rules (BR) are usually written by different stakeholders, which makes them vulnerable to contain different designations for a same concept. Such problem can be the source of a not well orchestrated behaviors. Whereas identification of synonyms is manual or totally neglected in most approaches dealing with natural language Business Rules. In this paper, we present an automated approach to identify semantic similarity between terms in textual BR using Natural Language Processing and knowledge-based algorithm refined using heuristics. Our method is unique in that it also identifies abbreviations/expansions (as a special case of synonym) which is not possible using a dictionary. Then, results are saved in a standard format (SBVR) for reusability purposes. Our approach was applied on more than 160 BR statements divided on three cases with an accuracy between 69% and 87% which suggests it to be an indispensable enhancement for other methods dealing with textual BR.

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An Approach to Mine SBVR Vocabularies and Rules from Business Documents

Cited by 10 publications

References 21 publications

The Semantic of Business Vocabulary and Business Rules: An Automatic Generation From Textual Statements

The Semantic of Business Vocabulary and Business Rules: An Automatic Generation From Textual Statements

Automated generation of terminological dictionary from textual business rules

Automated Identification of Semantic Similarity between Concepts of Textual Business Rules

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