Intelligent Systems in the Service of Cultural Heritage: The CARE Architecture for Concept-Aligned Ceramics Restoration

The restoration of archaeological ceramics is an intricate process built upon a rigorous system of concepts that bridge foundational theory and practical conservation. Building on previous efforts to systematize key terminology, this paper emphasizes the critical need for an unambiguous conceptual framework capable of guiding intervention goals and facilitating objective quality assessments. We present the CARE architecture ( Concept-Aligned Restoration with Evidence ), a research program designed for the seamless integration of advanced intelligent systems into the restoration workflow. Unlike generic AI approaches, CARE maintains the semantics of archaeology, museology, and conservation science as its core domain. We detail how contemporary intelligent tools—including graph neural networks, multimodal models, and differentiable simulation—can support documentation, automated fragment reassembly, condition diagnostics, and intervention planning while strictly adhering to the principles of reversibility and respect for cultural values. Central to this proposal is the CERAMON knowledge graph, a concept-oriented framework fully aligned with international standards such as CIDOC CRM. Particular emphasis is placed on risk-aware decision-making models and generative reconstructions of missing elements operating under mathematically grounded uncertainty. We introduce a set of specialized metrics—indices for semantic authenticity (S), irreversibility risk (Q), and intervention footprint (I)—designed to translate ethical principles into measurable data. This work addresses a multidisciplinary audience, seeking to align the progress of intelligent systems with human expertise, ethical governance, and the enhancement of public trust in the digital preservation of cultural heritage.