Aligning ESG Controversy Data with International Guidelines through Semi-Automatic Ontology Construction

• URL: http://arxiv.org/abs/2509.10922v1
• Date: Sat, 13 Sep 2025 17:49:59 GMT
• Title: Aligning ESG Controversy Data with International Guidelines through Semi-Automatic Ontology Construction
• Authors: Tsuyoshi Iwata, Guillaume Comte, Melissa Flores, Ryoma Kondo, Ryohei Hisano,
• Abstract summary: We present a semi-automatic method for constructing structured knowledge representations of environmental, social, and governance events reported in the news.<n>Our approach uses lightweight ontology design, formal pattern modeling, and large language models to convert normative principles into reusable templates.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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