Formalising Natural Language Specifications Using a Cognitive Linguistics/Configuration Based Approach

This paper addresses the problem of transforming natural language business specifications into formal models suitable for use in information systems. In particular, the Semantics of Business Vocabulary and Business Rules (SBVR) standard is used as a starting point for both the natural language specifications and the formal representation. In recent years, SBVR-based approaches have been proposed for transforming natural language business rules into models, such as UML Class Diagrams, however, most focus on the transformations performed after the SBVR models have been created and, therefore, simplify or entirely neglect the natural language to SBVR transformation. There are a number of difficulties in transforming natural language into formal models, such as ambiguity and inconsistency. This paper presents a solution based on a unique combination of techniques from Cognitive Linguistics and Knowledge-based Configuration in order to transform natural language business specifications into SBVR models. We present a comparative survey of state-of-the-art approaches and argue that current solutions do not fulfil the criteria necessary to meet our goals. We demonstrate our approach and show how it improves translation of natural language business specifications into formal models and increases the level of automation for Model-Driven Engineering.