Formalising natural language specifications using a cognitive linguistic/configuration based approach. (December 2015) - Selway, Matt; Grossmann, Georg; Mayer, Wolfgang; Stumptner, Markus
Abstract: This paper addresses the problem of transforming business specifications written in natural language into formal models suitable for use in information systems development. It proposes a method for transforming controlled natural language specifications based on the Semantics of Business Vocabulary and Business Rules standard. This approach is unique in combining techniques from Model-Driven Engineering (MDE), Cognitive Linguistics, and Knowledge-based Configuration, which allows the reliable semantic processing of specifications and integration with existing MDE tools to improve productivity, quality, and time-to-market in software development. The method first learns the vocabulary of the specification from glossary-like definitions then parses the rules of the specification and outputs the resulting formal SBVR model. Both aspects of the method are tested separately, with the system correctly learning 98% of the vocabulary and correctly interpreting 98% of the rules of an SBVR SE based example. Finally, the proposed method is compared to state-of-the-art approaches for creating formal models from natural language specifications, arguing that it meets the criteria necessary to fulfil the three goals of (1) shifting control of specification to non-technical business experts, (2) reducing the manual effort involved in formalising specifications, and (3) supporting business experts in creating well-formed sets of business vocabularies and rules. Abstract : Highlights: A method for deep processing of natural language business specifications is proposed. The method is based on Cognitive Linguistics and Knowledge-based Configuration. The method acquires vocabulary from a business glossary and parses business rules. The vocabulary acquisition achieves an accuracy of 96%. The semantic analysis of rules achieves an accuracy of 98%.