CEON: Circular Economy Ontology Network

2026-06-01 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Circular Economy & Sustainability · Depth: Advanced, quick

Summary

The Circular Economy Ontology Network (CEON) was developed within the Onto-DESIDE project to address challenges in knowledge representation and semantic interoperability for the circular economy (CE) domain. Recognizing that increasing resource circularity is crucial for sustainability, CEON aims to enable information sharing and communication across diverse industry sectors involved in product life cycles. It defines cross-sectorial concepts to fill existing gaps in CE knowledge representation and facilitates semantics-aware data documentation. The network's utility is demonstrated through cross-industry data documentation scenarios, specifically spanning the construction, electronics, and textile sectors, highlighting its role in supporting circular strategies like reuse, refurbishing, remanufacturing, and recycling.

Key takeaway

For AI Architects or Research Scientists designing information systems for sustainability, CEON offers a framework to enhance semantic interoperability across diverse industrial data. You should consider integrating ontology networks like CEON to standardize knowledge representation for circular economy initiatives, particularly when dealing with complex product life cycles spanning sectors like construction, electronics, or textiles. This approach can streamline data sharing and communication, directly supporting the implementation of circular strategies.

Key insights

CEON provides a cross-sectorial ontology network to enable semantic interoperability and data documentation for circular economy strategies.

Principles

• Semantic interoperability is key for circularity.
• Cross-sectoral concepts bridge CE knowledge gaps.

Method

CEON was developed within the Onto-DESIDE project to define cross-sectorial concepts and enable semantics-aware data documentation, demonstrated via construction, electronics, and textile scenarios.

In practice

• Document product life cycle data semantically.
• Facilitate information sharing across industries.

Topics

• Circular Economy
• Ontology Network
• Semantic Interoperability
• Knowledge Representation
• Product Life Cycle
• Sustainability

Best for: AI Scientist, AI Engineer, AI Architect, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.