Towards Provenance-Centric Spatial Data Supply Chains: A Review of Semantic Web Technologies

Spatial data supply chains (SDSCs) require robust provenance mechanisms to ensure data quality, traceability, and interoperability across geospatial workflows. This study presents a systematic review of semantic web–based approaches to provenance modelling in SDSCs, synthesising evidence from 156 studies published between 2001 and 2025. The review evaluates the use of semantic technologies, including RDF, OWL, SPARQL, and GeoSPARQL, and benchmarks existing provenance models against criteria of granularity, scalability, and standards compliance. The findings reveal fragmented lineage practices, limited feature-level provenance representation, and persistent challenges related to real-time processing, interoperability, and scalability. To address these gaps, the study identifies the need for GeoPROV, a minimal and interoperable semantic framework that extends W3C PROV with spatial semantics while maintaining compatibility with ISO lineage standards and emerging catalogue specifications. GeoPROV can enhance trust in real-world spatial data ecosystems. The review concludes by outlining practical implications for operationalising GeoPROV in SDSCs, identifying research priorities for automated provenance capture and big-data scalability, and highlighting the role of semantic reasoning in improving trust, transparency, and reproducibility in spatial data governance.