Design and Validation of a FAIR-Compliant Semantic Ontology for Sustainability Reporting - Tri Frida Suryati; Sultan Syah; Sri Rahayu Syah (Jurnal Ekonomi Ichsan Sidenreng Rappang, 2026)
Sustainable fashion involves complex interactions among material, manufacturing, ethical, and socio-cultural dimensions; however, a formalized semantic framework remains insufficiently developed. This study aims to develop and validate a semantic ontology framework, namely the Fashion and Textile Semantic Knowledge Ontology (FTOs), to systematically and interoperably represent and integrate various dimensions of sustainable fashion. The FTOs was constructed using the NeOn Ontology Engineering Methodology (Scenario 2) to ensure modularity, integration of multiple sources, and iterative refinement through collaboration with domain experts. The ontology consists of eight primary classes: Fiber Type, Yarn Composition, Fabric Lifecycle, Manufacturing Process, Ethical Design, Consumer Awareness, Cultural Aesthetic, and Policy Framework. These classes are interlinked through semantic relations, including isMadeOf, affects, contributesTo, influences, and enables. Validation was conducted using SPARQL-based competency questions and structured domain-expert evaluation, resulting in a mean precision of 95.9 percent, a recall of 90.4 percent, and an overall validity index of 4.74 out of 5. These outcomes confirm the ontology's semantic coherence, inferential reliability, and practical applicability. The integration of cultural and ethical dimensions bridges sustainability semantics with identity-driven consumption patterns, providing a novel contribution to sustainability informatics. The FTOs framework is FAIR-compliant, reusable, and interoperable. It enables AI-driven applications in circular economy modeling, eco-label tracking, and policy simulation, and supports the operationalization of SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production). The ontology provides a replicable foundation for cross-domain sustainable knowledge integration, with potential for extension to multi-lingual datasets and dynamic lifecycle analytics.