From Regulatory Texts to BRMS: How to Guide the Acquisition of Business Rules?

Abstract

Abstract. This paper tackles the problem of rule acquisition, which is critical for the development of BRMS. The proposed approach assumes that regulations written in natural language (NL) are an important source of knowledge but that turning them into formal statements is a complex task that cannot be fully automated. The present paper focuses on the first phase of this acquisition process, the normalization phase that aims at transforming NL statements into controlled language (CL), rather than on their formalization into an operational rule base. We show that turning a NL text into a set of self-sufficient and independent CL rules is itself a complex task that involves some lexical and syntactic normalizations but also the restoration of contextual information and of implicit semantic entities to get a set of self-sufficient and unambiguous rule statements. We also present the SemEx tool that supports the proposed acquisition methodology based on the selection of the relevant text fragments and their progressive and interactive transformation into CL rule statements.

Integrating Agents into a Collaborative Knowledge-based System for Business Rules Consistency Management

“…The work presented in [10] tackles the rule acquisition problem, which is crucial and critical for the development of BRMS. The proposed approach assumes that regulations written in natural language are an important and essential source of knowledge, but turning them into formal statements is a complex task that cannot be completely automated.…”

Handling Semantic Complexity of Big Data using Machine Learning and RDF Ontology Model

“…The majority of these rule harvesting methods do not have support for finding and measuring semantic complexity and consistency. Moreover, the combination of manual and automated efforts gives the better result of rule harvesting [23]. With the lack of adherence to development guidelines, it is difficult for the application developer to navigate through the code and naming conventions.…”

Représentation et vérification d’un environnement intelligent à partir de spécifications utilisateur en langage naturel

“…Toutefois, les spécifications en LN sont souvent porteuses de formulations ambiguës ou implicites pouvant mener à des problèmes d'interprétation et ne sont donc pas considérées comme formelles. L'automatisation d'un passage direct entre spécifications LN et spécifications formelles est difficile, si ce n'est impossible à effectuer (Guissé et al, 2012). Cette problématique n'est pas récente (Fraser et al, 1991 ;Vadera, Meziane, 1994 ;Fougères, Trigano, 1997).…”