Semantic Intelligence Against CSAM: The PreventCSA@EU Ontology Framework for Classification and Investigation

2026-08-13

AI Summary

This study addresses the significant challenges posed by inconsistent legal definitions and classification standards for child sexual abuse and exploitation material (CSAM/CSEM) across jurisdictions, which impede cross-institutional collaboration and automated processing. To overcome this, the work proposes PreventCSA@EU, a semantic-driven ontology framework that integrates, for the first time, INHOPE UCS labels, Dublin Core-DMCI metadata, and Schema.org to construct a hierarchical ontology model tailored for CSA/CSE investigations. Centered on core entities—such as media objects, content, persons, depictions, and investigative reports—the framework systematically harmonizes existing ontologies and international metadata standards. This integration enables consistent CSAM/CSEM categorization, supports child identification, and facilitates case analysis, thereby substantially enhancing interoperability across systems and laying a critical technical foundation for the European Union’s planned CSAR central database.

Abstract

This work presents the PreventCSA@EU ontology, a semantically grounded framework designed to support the identification, classification, annotation, and analysis of online Child Sexual Abuse and Child Sexual Exploitation Material (CSAM/CSEM). The growing circulation and dissemination of CSAM/CSEM across digital environments, combined with inconsistencies in legal definitions and classification practices across jurisdictions, highlights the need for semantically interoperable frameworks capable of supporting cross-organizational cooperation and automated processing. The proposed ontology is developed through a systematic review and comparative analysis of existing CSA/CSE-related, metadata oriented, and investigative ontologies and taxonomies, with its primary design aimed at addressing the operational needs and domain-specific requirements of national LEA Directorates. It introduces a hierarchical semantic model built around core entities such as Media Object, Content, Person, Depiction, and Investigative Report, while enabling structured alignment with INHOPE UCS labels, Dublin Core-DMCI Metadata Terms, and Schema.org. The proposed framework emphasizes ontology-driven interoperability for structured annotation and analysis of CSA/CSE-related data, supporting consistent classification, child identification, and investigative processes for offender prosecution. The design aims extend existing classification approaches with additional conceptual structures for database conceptualization, process modeling, and ontology-driven data management. By integrating established classification standards with a novel hierarchical ontology, the proposed framework enhances cross-system compatibility, with particular relevance to emerging EU-level data infrastructures, including the envisaged EU Center database under the proposed Child Sexual Abuse Regulation (CSAR).

Problem

• CSAM
• CSEM
• semantic interoperability
• classification inconsistency
• cross-jurisdictional cooperation

Innovation

• ontology
• semantic interoperability
• CSAM classification
• hierarchical semantic model
• cross-system compatibility