Volume 16, Number 3/4

Geo-RDF Framework for Representing the Spatial Information of Bangladesh

  Authors

Md Hasan Hafizur Rahman ¹ and Hanif Seddiqui ², ¹ Comilla University, Bangladesh, ² University of Chittagong, Bangladesh

  Abstract

Geographic data plays a vital role in supporting modern location-based services on the World Wide Web (WWW). In Bangladesh, such data exists in structured, semi-structured, and unstructured formats, stored across government agencies, research institutions, and private organizations using diverse formats and protocols. This heterogeneity creates significant integration challenges, limiting operational efficiency and the development of applications ranging from navigation and logistics to personalized services and emergency response. Our research addresses this by transforming and integrating disparate datasets into a machine-understandable form. We modeled the complete administrative hierarchy of Bangladesh, from divisions to villages, generating 0.40 million RDF (Resource Description Framework) triples within a unified semantic repository, Geo-Bangladesh. This repository enables effortless integration and retrieval of geospatial data across all administrative levels. We further linked Geo-Bangladesh with related repositories, including the educational institutions and citizen information, enabling the mapping and visualization of entities along with locations. Using geoSPARQL, we retrieved and inferred spatial and non-spatial data, demonstrating the repository’s usability, interoperability, and effectiveness. Unlike raw GeoSPARQL implementations or general-purpose ontologies such as Geonames, Geo-Bangladesh is explicitly tailored to Bangladesh’s administrative structure and reconciles inconsistencies such as the 68 vs. 64 districts problem. Compared to OGC-compliant frameworks, our repository incorporates both semantic interoperability and localized reconciliation, demonstrating advantages in accuracy, query flexibility, and alignment with national data sources.

  Keywords

Semantic Web, Ontology, RDF, Spatial Data, geoSPARQL