Natural language processing-enhanced extraction of SBVR business vocabularies and business rules from UML use case diagrams

Danėnas, Paulius; Skersys, Tomas; Butleris, Rimantas

doi:10.1016/j.datak.2020.101822

Cited by 29 publications

(17 citation statements)

References 44 publications

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“…A preexisting UML class model is intended to be consistent with the vocabulary used in the textual constraints. 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.…”

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%

See 2 more Smart Citations

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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“…e proposed approach achieved an average recall of 83.22%, average precision of 87.13%, and average F-value of 85.14. Danenas et al [41] propose the M2M conversion. Authors extract data from UML case diagrams using Text rumblings.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Analytics Approach to Minimize Complexity in Ambiguous Software Requirements

Ashfaq

Bajwa

Kazmi

et al. 2021

Scientific Programming

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An inconsistent and ambiguous Software Requirement Specification (SRS) document results in an erroneous/failed software project. Hence, it is a serious challenge to handle and process complex and ambiguous requirements. Most of the literature work focuses on detection and resolution of ambiguity in software requirements. Also, there is no standardized way to write unambiguous and consistent requirements. The goal of this research was to generate an ambiguity-less SRS document. This paper presents a new approach to write ambiguity-less requirements. Furthermore, we design a framework for Natural Language (NL) to Controlled Natural Language (CNL) (such as Semantic Business Vocabulary and Rules (SBVR)) transition and develop a prototype. The prototype also generates Resource Description Framework (RDF) representation. The SBVR has a shared meaning concept that minimizes ambiguity, and RDF representation is supported by query language such as SPARQL Protocol and RDF Query Language (SPARQL). The proposed approach can help software engineers to translate NL requirements into a format that is understandable by all stakeholders and also is machine processable. The results of our prototype are encouraging, exhibiting the efficient performance of our developed prototype in terms of usability and correctness.

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“…A. Awad [16] proposed an automated approach for querying a business process model repository, in which the most common sense was selected from WordNet [11] dictionary. Other approaches tried to extract synonyms using dictionary (like WordNet) without explanation neither about what was the algorithm used, nor if the context was taken into consideration or not, such in [17,18] in which SBVR business vocabularies and rules are extracted from UML use case diagrams; or [19] which presented an automatic approach to generate BPMN models from natural language text; Based entirely on dictionary results. (method not cited) T. Skersys [18] Use Case Diagram SBVR Business Vocabularies and Rules…”

Section: Related Workmentioning

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.

show abstract

Natural language processing-enhanced extraction of SBVR business vocabularies and business rules from UML use case diagrams

Cited by 29 publications

References 44 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

An Intelligent Analytics Approach to Minimize Complexity in Ambiguous Software Requirements

Automated Identification of Semantic Similarity between Concepts of Textual Business Rules

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