🤖 AI Summary
This study addresses the challenge of aligning ESG controversy events—extracted from unstructured news—with international normative frameworks (e.g., the UN Global Compact). To bridge this gap, we propose a semi-automated, lightweight ontology construction method that integrates large language models (LLMs) with formal RDF schema design, transforming abstract sustainability principles into reusable, interpretable semantic templates. These templates enable structured event knowledge extraction from news texts and the construction of an ESG controversy knowledge graph explicitly aligned with global standards. Our key contribution is the automated, accurate, and interpretable mapping of normative principles to machine-readable rules—ensuring cross-regional consistency. The resulting framework supports precise identification and semantic provenance tracing of regulatory violations, providing a scalable, transparent knowledge infrastructure for regulatory compliance assessment and sustainable investment decision-making.
📝 Abstract
The growing importance of environmental, social, and governance data in regulatory and investment contexts has increased the need for accurate, interpretable, and internationally aligned representations of non-financial risks, particularly those reported in unstructured news sources. However, aligning such controversy-related data with principle-based normative frameworks, such as the United Nations Global Compact or Sustainable Development Goals, presents significant challenges. These frameworks are typically expressed in abstract language, lack standardized taxonomies, and differ from the proprietary classification systems used by commercial data providers. In this paper, we present a semi-automatic method for constructing structured knowledge representations of environmental, social, and governance events reported in the news. Our approach uses lightweight ontology design, formal pattern modeling, and large language models to convert normative principles into reusable templates expressed in the Resource Description Framework. These templates are used to extract relevant information from news content and populate a structured knowledge graph that links reported incidents to specific framework principles. The result is a scalable and transparent framework for identifying and interpreting non-compliance with international sustainability guidelines.