A framework for semantic reconciliation of disparate earth observation thematic data

  • Authors:

  • S. S. Durbha;R. L. King;V. P. Shah;N. H. Younan

  • Affiliations:

  • Department of Electrical and Computer Engineering, GeoResources Institute (GRI), Mississippi State University, MS 39762, USA;Bagley College of Engineering, Mississippi State University, MS 39762, USA;Department of Electrical and Computer Engineering, GeoResources Institute (GRI), Mississippi State University, MS 39762, USA;Department of Electrical and Computer Engineering, GeoResources Institute (GRI), Mississippi State University, MS 39762, USA

  • Venue:
  • Computers & Geosciences
  • Year:
  • 2009

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Abstract

There is a growing demand for digital databases of topographic and thematic information for a multitude of applications in environmental management, and also in data integration and efficient updating of other spatially oriented data. These thematic data sets are highly heterogeneous in syntax, structure and semantics as they are produced and provided by a variety of agencies having different definitions, standards and applications of the data. In this paper, we focus on the semantic heterogeneity in thematic information sources, as it has been widely recognized that the semantic conflicts are responsible for the most serious data heterogeneity problems hindering the efficient interoperability between heterogeneous information sources. In particular, we focus on the semantic heterogeneities present in the land cover classification schemes corresponding to the global land cover characterization data. We propose a framework (semantics enabled thematic data Integration (SETI)) that describes in depth the methodology involved in the reconciliation of such semantic conflicts by adopting the emerging semantic web technologies. Ontologies were developed for the classification schemes and a shared-ontology approach for integrating the application level ontologies as described. We employ description logics (DL)-based reasoning on the terminological knowledge base developed for the land cover characterization which enables querying and retrieval that goes beyond keyword-based searches.