Establishing similarity across multi-granular topological-relation ontologies

Authors:
Matthew P. Dube;Max J. Egenhofer
Affiliations:
National Center for Geographic Information and Analysis and Department of Spatial Information Science and Engineering, University of Maine, Orono, ME;National Center for Geographic Information and Analysis and Department of Spatial Information Science and Engineering, University of Maine, Orono, ME
Venue:
QuaCon'09 Proceedings of the 1st international conference on Quality of context
Year:
2009

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Abstract

Within the Geospatial Semantic Web, selecting a different ontology for a spatial data set will enable that data's analysis in a different context. Analyses of multiple data sets, each based on a different ontology, require appropriate bridges across the ontologies. This paper focuses on establishing such a bridge across two topological-relation ontologies of different granularity--the standard eight detailed toplogical relations and five coarse topological relations. By mapping the conceptual neighborhood graphs onto a zonal representation, the different granularities are aligned spatially, yielding a reasoned approach to determining similarity values for the bridges across the two ontologies. A comparison with bridge lengths from an averaged model shows the better quality of zonal model.