Spatial Semantics for the Evaluation of Administrative Geospatial Ontologies

Author:

Abdelmoty Alia I.1ORCID,Muhajab Hanan12ORCID,Satoti Abdurauf1

Affiliation:

1. School of Computer Science & Informatics, Cardiff University, Cardiff CF24 4AG, Wales, UK

2. College of Engineering and Computer Science, Jazan University, Jazan 45142, Saudi Arabia

Abstract

Administrative geography is concerned with the hierarchy of areas related to national and local government in a country. They form an important dataset in the country’s open data provision and act as the geo-referencing backdrop for many types of geospatial data. Proprietary ontologies are built to model and represent these data with little focus on spatial semantics. Studying the quality of these ontologies and developing methods for their evaluation are needed. This paper addresses these problems by studying the spatial semantics of administrative geography data and proposes a uniform set of qualitative semantics that encapsulates the inherent spatial structure of the administrative divisions and allows for the application of spatial reasoning. Topological and proximity semantics are defined and combined into a single measure of spatial completeness and used for defining a set of competency questions to be used in the evaluation process. The significance of the novel measure of completeness and competency questions is demonstrated on four prominent real world administrative geography ontologies. It is shown how these can provide an objective measure of quality of the geospatial ontologies and gaps in their definition. The proposed approach to defining spatial completeness complements the established methods in the literature, that primarily focus on the syntactical and structural dimensions of the ontologies, and offers a novel approach to ontology evaluation in the geospatial domain.

Publisher

MDPI AG

Reference54 articles.

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3. Cavallaro, G., Heras, D.B., Lunga, D., Werner, M., and Züfle, A. (2021). Proceedings of the 1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, ACM.

4. (2024, March 13). Global Open Data Index. Available online: http://index.okfn.org/place.html.

5. (2024, March 13). Ordnance Survey Mapping Agency. Available online: https://www.ordnancesurvey.co.uk/.

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