Representing Normative Regulations in OWL DL for Automated Compliance Checking Supported by Text Annotation

2025-04-08

AI Summary

To address the inefficiency and error-proneness of manual regulatory compliance checking, this paper proposes an OWL DL formalization method for natural language specifications. The method introduces a novel structured text annotation scheme and employs a rule-driven deterministic transformation algorithm to automatically map specification texts to OWL DL ontologies. It further integrates Protégé with the HermiT reasoner to enable machine-readable semantic representation and automated compliance verification. A proof-of-concept evaluation in the construction domain demonstrates successful translation of multiple natural language regulations into OWL DL ontologies and accurate identification of compliant and non-compliant scenarios. This work bridges a critical gap between regulatory semantic modeling and automated reasoning, delivering a scalable, methodology-driven foundation for automating compliance checking.

Technology Category

• Knowledge Representation and Reasoning: Description Logics
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Compliance checking is the process of determining whether a regulated entity adheres to these regulations. Currently, compliance checking is predominantly manual, requiring significant time and highly skilled experts, while still being prone to errors caused by the human factor. Various approaches have been explored to automate compliance checking, however, representing regulations in OWL DL language which enables compliance checking through OWL reasoning has not been adopted. In this work, we propose an annotation schema and an algorithm that transforms text annotations into machine-interpretable OWL DL code. The proposed approach is validated through a proof-of-concept implementation applied to examples from the building construction domain.

Problem

• Automating compliance checking using OWL DL representation
• Reducing manual effort and errors in regulation adherence
• Transforming text annotations into machine-readable OWL DL code

Innovation

• Automated compliance checking using OWL DL
• Text annotations transformed into OWL DL
• Proof-of-concept in building construction