Enhancing Geospatial Data Integration and Completeness throughSemantic Web and Linked Open Data

Abstract

This paper investigates methods for improving the integration and completeness of heterogeneous geospatial datasets using Semantic Web technologies and Linked Open Data. Interoperability issues, fragmented data sources, and incomplete datasets remain persistent challenges in the geospatial domain—often exacerbated by proprietary formats and lack of coordination among stakeholders. To address these, we propose an ontology-based approach built on the principles of knowledge representation and reasoning. By leveraging the Universal Spatial Knowledge Base (USKB) and rulebased inference mechanisms, our method facilitates semantic interoperability and supports completeness analysis across diverse data sources. As a demonstration of this approach, we present SPALOD (Spatial Data Management with Semantic Web Technology and Linked Open Data), a platform designed to integrate and manage complex geospatial datasets. SPALOD illustrates how semantic reasoning can ensure consistent data quality, enhance interoperability, and enable seamless integration across systems. Our results underline the feasibility and impact of semantic-enriched data management strategies in the geospatial context, offering a scalable solution for harmonizing and enriching spatial information about bicycle network.