From Natural-language Regulations to Enterprise Data using Knowledge Representation and Model Transformations

Enterprises today face an unprecedented regulatory regime and are increasingly looking to technology to ease their regulatory compliance concerns. Formal approaches in research focus on checking compliance of business processes against rules, and assume usage of matching terminology on both sides. We focus on run-time compliance of enterprise data, and the specific problem of identifying enterprise data relevant to a regulation, in an automated manner. We present a knowledge representation approach and semi-automated solution using models and model transformations to extract the same from distributed enterprise databases. We use a Semantics of Business Vocabulary and Rules (SBVR) model of regulation rules as the basis to arrive at the necessary and sufficient model of enterprise data. The approach is illustrated using a real-life case study of the MiFID-II financial regulation.