As global urbanisation trends continue, with projections indicating that 70% of the world population will reside in cities by 2050, there is growing concern over the sustainability of rural and mountain regions. These areas face increasing depopulation, threatening their socio-economic value chains (VCs). Addressing these challenges requires access to reliable and interoperable geospatial data. Knowing the exact location of mountain VCs can provide important insights to support territorial resilience. This paper investigates how Knowledge Representation and Semantic Web technologies can enhance the analysis of geographic data to support sustainable development in rural territories. As a case study, we use data from the H2020 MOVING project, encompassing 454 VCs across 16 European mountain regions. Our findings show that semantic technologies offer a valuable framework for integrating heterogeneous datasets, thereby improving decision-making and fostering resilience in rural areas.

Linked geospatial data for rural territorial sustainability: a knowledge graph of european mountain value chains

Pratelli N.
;
Lenzi E.
;
Bartalesi Lenzi V.
2025

Abstract

As global urbanisation trends continue, with projections indicating that 70% of the world population will reside in cities by 2050, there is growing concern over the sustainability of rural and mountain regions. These areas face increasing depopulation, threatening their socio-economic value chains (VCs). Addressing these challenges requires access to reliable and interoperable geospatial data. Knowing the exact location of mountain VCs can provide important insights to support territorial resilience. This paper investigates how Knowledge Representation and Semantic Web technologies can enhance the analysis of geographic data to support sustainable development in rural territories. As a case study, we use data from the H2020 MOVING project, encompassing 454 VCs across 16 European mountain regions. Our findings show that semantic technologies offer a valuable framework for integrating heterogeneous datasets, thereby improving decision-making and fostering resilience in rural areas.
2025
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
979-8-3315-1435-8
Knowledge representation; Semantic web; GeoSPARQL; CIDOC CRM; Wikidata; OpenStreetMap
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/563943
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