Semantic Collaborative Environment for Natural Heritage with Integrating Data, Metadata and Paradata from Nature to Extended Digital Specimen

This study proposes an integrated framework that redefines digital natural heritage as a collaborative and interpretive ecosystem. At its foundation lies the E-DNH (Extended Digital Natural Heritage) ontology, semantically designed to interlink the biological, historical–cultural, and digital processual dimensions of heritage. The ontology ensures interoperability and contextual richness through alignment with international standards such as Darwin Core, CIDOC CRM, CRMdig, and PROV-O, forming the semantic basis for a knowledge graph–driven data model. The framework is operationalized through the HR3D (Hyper Reality 3D Digitalization) workflow, which combines AI-assisted structured-light scanning and photogrammetry to achieve high-fidelity reproduction under controlled environmental parameters. This process quantifies precision and reliability while preserving the paradata of digitization. The system further incorporates E-DNH Tools, a collaborative semantic annotation environment that allows diverse participants—researchers, curators, and the public—to explore, interpret, and enrich 3D heritage data in real time. These components converge within the Collaborative Extended Digital Natural Heritage Platform (C-EDNH), a cloud-based semantic infrastructure that integrates an NSId/DOI-based persistent identifier system for specimen traceability and global data exchange. By connecting ontology-driven datasets with persistent identifiers, the platform transforms static records into interoperable, verifiable, and living knowledge networks. Collectively, this research demonstrates that understanding and sharing natural heritage in digital space requires encompassing its biological, cultural, and technical contexts within an ontology-based knowledge graph architecture, while employing hyper-reality technologies to enable inclusive, multisensory, and collaborative engagement with heritage data. By integrating precision, meaning, and participation, the study establishes a foundation for a sustainable and human-centered Semantic Collaborative Environment for Natural Heritage.While developed for natural heritage contexts, the proposed framework’s data management architecture and semantic integration strategies offer transferable methodologies applicable to broader cultural heritage digitization challenges.