Abstract
Business rules are generally captured in a natural language. The inherit ambiguity of the latter is often seen as a cause for project failure, which makes it necessary to translate natural language business rules statements to another language sufficiently formal. However, business experts are generally not familiar with formal languages, which can complicate the communication between stakeholders. For this reason, Object Management Group (OMG) had proposed SBVR Standard (2008) for modeling complex organizations in a natural language but in a formal and detailed way. As a result, several studies have succeeded to increase the accuracy of their approaches by transforming their models from/to SBVR standard. Clearly then, the success of these approaches depends on the quality of the SBVR based statements used or generated. This paper presents an approach for checking conformance of both lexicon and syntax of Business Rules (BR) expressed with Semantic of Business Vocabulary and Rules (SBVR), to SBVR Structured English notation (SBVR-SE) using Natural Language Processing (NLP).
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Haj, A., Balouki, Y., Gadi, T. (2019). Automated Checking of Conformance to SBVR Structured English Notation. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_63
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DOI: https://doi.org/10.1007/978-3-030-11928-7_63
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