Combining Acquisition and Debugging of Business Rule Models

  • Adeline Nazarenko
  • François Lévy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8035)


Business rules (BR) can be acquired from complex texts such as laws, regulations or contracts. However knowledge extraction and formalization is a complex task that involves business experts as well as Information Technology engineers and that is error-prone. Instead of waiting until the rule base is completed or the BR decision system is put into production to detect problems, we propose to detect inconsistencies and errors at an early stage, before the formalization work is completed. This paper presents the quality procedures that can be implemented in the process of BR acquisition from NL regulations. We show that the documented business rule models under construction are useful to detect potential anomalies at a semi-formal level of the BR base, where the rules exploit a formal vocabulary but are simply structured into premises and conclusions. Even at the prior and textual level, these documented models give the business experts a global and structured view over the NL regulation, which helps the formalization process.


Rule Base Source Text Semantic Annotation Regulatory Text Business Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Adeline Nazarenko
    • 1
  • François Lévy
    • 1
  1. 1.Sorbonne Paris Cité & CNRS (UMR 7030)LIPN, Université Paris 13France

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