Semantic Integration and Automation of Cultural Heritage Risk Data: A CIDOC-CRM Workflow for Decision Support at the Territorial Scale

The increasing availability of digital documentation in cultural heritage has amplified the need for interoperable systems capable of integrating heterogeneous data and supporting risk-informed conservation strategies. In the field of Disaster Risk Management (DRM), the application of structured methodologies—such as the ICCROM-CCI ABC Method—is often hindered by fragmented data sources, inconsistent terminology, and limited interoperability across institutions. This study presents a semantic workflow for the harmonization, enrichment, and integration of cultural heritage risk assessment data within a CIDOC Conceptual Reference Model (CIDOC-CRM)-compliant environment. The proposed system is structured as an Extract–Transform–Load (ETL) pipeline that converts heterogeneous assessment records into interoperable semantic knowledge graphs. The workflow combines controlled vocabularies, project-specific thesauri for risk agents and heritage typologies, and formal ontology mapping implemented through the Mapping Memory Manager (3M) and executed with the X3ML engine. The resulting data are deployed within a ResearchSpace environment, enabling semantic querying, cross-dataset exploration, and integration with external knowledge infrastructures. The workflow was applied to a dataset comprising 295 cultural heritage sites in the municipality of Ravenna (Italy). The transformation process generated a CIDOC-CRM-compliant knowledge graph containing 134,611 RDF triples and 18,954 entities, integrating information on cultural assets, risk scenarios, actors, documentary resources, and quantitative risk assessments. Through the adoption of persistent identifiers and semantic mappings, the workflow also supports interoperability with external cultural heritage resources, including ArCo and GeoNames, facilitating the contextualization and enrichment of local risk assessment data. By transforming fragmented assessment records into structured and interoperable knowledge, the proposed workflow contributes to bridging semantic and information gaps in cultural heritage risk management. The study demonstrates the feasibility of integrating risk assessment data within an ontology-based semantic infrastructure and highlights its potential to support data integration, semantic interoperability, knowledge reuse, and future decision-support applications for preventive conservation and territorial risk management.