Building interoperability across borders: a linked open metadata framework for an ASEAN GLAM aggregation platform

Enhancing Interoperability of SPARQL Endpoints: RESTful and OAI-PMH API for the DH-ATLAS Project

MetaFAIR Ecosystem for SSH Digital Libraries: Dataset Packaging, Validation, and SPARQL Publication

A Graded Model of Semantic Commitment for Curated Cultural Heritage Data Integration

This paper presents a semantic hypermedia framework for documenting and interpreting cultural heritage artifacts, with particular attention to decontextualized and refunctionalized architectural components, through curated integration of heterogeneous sources. Built on Linked Open Data principles, it addresses two key challenges: mediating semantic complexity for domain experts, and ensuring data quality when integrating external resources. Knowledge patterns map CIDOC-CRM to a project-specific model aligned with scholarly practice, while external datasets—such as the Getty vocabularies, OpenStreetMap, Zotero, and Iconclass—are incorporated through a graded model of semantic commitment, distinguishing authority alignment, partial reuse, and full ontology adoption. External information is materialized in a knowledge graph to ensure reproducibility and long-term accessibility. By framing external linking and semantic mediation as a hypermedia design problem, the approach shows how controlled integration can support navigation, enrichment, and scholarly reuse of cultural heritage data while preserving curatorial control. We contribute a practical model for dataset curation, semantic mediation, and graph-based knowledge representation in cultural heritage hypermedia systems, developed through the design and implementation of a case study.

OpenDataGOV-AP: A Linked Data Application Profile for parliamentary activities

Semantic Intelligence Against CSAM: The PreventCSA@EU Ontology Framework for Classification and Investigation

This work presents the PreventCSA@EU ontology, a semantically grounded framework designed to support the identification, classification, annotation, and analysis of online Child Sexual Abuse and Child Sexual Exploitation Material (CSAM/CSEM). The growing circulation and dissemination of CSAM/CSEM across digital environments, combined with inconsistencies in legal definitions and classification practices across jurisdictions, highlights the need for semantically interoperable frameworks capable of supporting cross-organizational cooperation and automated processing. The proposed ontology is developed through a systematic review and comparative analysis of existing CSA/CSE-related, metadata oriented, and investigative ontologies and taxonomies, with its primary design aimed at addressing the operational needs and domain-specific requirements of national LEA Directorates. It introduces a hierarchical semantic model built around core entities such as Media Object, Content, Person, Depiction, and Investigative Report, while enabling structured alignment with INHOPE UCS labels, Dublin Core-DMCI Metadata Terms, and Schema.org. The proposed framework emphasizes ontology-driven interoperability for structured annotation and analysis of CSA/CSE-related data, supporting consistent classification, child identification, and investigative processes for offender prosecution. The design aims extend existing classification approaches with additional conceptual structures for database conceptualization, process modeling, and ontology-driven data management. By integrating established classification standards with a novel hierarchical ontology, the proposed framework enhances cross-system compatibility, with particular relevance to emerging EU-level data infrastructures, including the envisaged EU Center database under the proposed Child Sexual Abuse Regulation (CSAR).

Metadata Quality and Discoverability in the NCCU CoDE Open Data Hub: A FAIR- and FGDC CSDGM-Informed Assessment

University geospatial hubs support research dissemination, teaching, public data access, and community engagement, but their value depends on whether resources can be found, accessed, interpreted, integrated, and reused. This study evaluates 747 publicly listed items in North Carolina Central University’s CoDE Open Data Hub using FAIR principles as an interpretive framework and the Federal Geographic Data Committee Content Standard for Digital Geospatial Metadata (FGDC CSDGM) as the domain-specific measurement basis. Nineteen criteria were scored on explicit criterion-specific 0–2 rules using live ArcGIS item properties, formal metadata XML, and available layer properties. Discoverability was evaluated for a proportionally stratified sample of 153 items through exact-title, keyword, location, item-type, category-browsing, and click-depth tests. The equal-criterion benchmark averaged 28.57 of 38 (75.19%); equal FAIR-dimension weighting produced a mean of 73.26%, and 95.85% of items retained the same descriptive performance band. Independent rescoring of 50 stratified items produced 76.74% exact criterion-level agreement, a linear weighted Cohen’s kappa of 0.587, and an absolute-agreement ICC of 0.715 for total scores. FAIR-aligned scores were highest for Findability (87.25%) and Accessibility (85.96%), followed by Reusability (67.26%) and Interoperability (52.59%). Exact-title and item-type searches retrieved all sampled items, while keyword search was successful for 88.24%. The results distinguish public availability from technical reuse readiness and identify spatial reference, attribute definitions, lineage, limitations, category structure, and navigation depth as priorities. The performance bands are study-specific descriptive summaries rather than FGDC compliance levels or FAIR certification. The study provides an evidence-preserving, type-aware method for applying FAIR principles alongside geospatial metadata standards in heterogeneous ArcGIS Hub catalogs.