Sharing data and knowledge from heterogeneous sources
Environmental information systems in industry and public administration
Resolving semantic heterogeneity in schema integration
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
Conceptual Spaces: The Geometry of Thought
Conceptual Spaces: The Geometry of Thought
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Semantic Data Integration in Hierarchical Domains
IEEE Intelligent Systems
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Measuring semantic similarity between geospatial conceptual regions
GeoS'05 Proceedings of the First international conference on GeoSpatial Semantics
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In this paper we describe an algorithm for merging ontologies from heterogeneous geographic data sources. The algorithm is based on an asymmetric similarity function that considers the spatial distribution of thematic values in the datasets. It has been used in the context of a semantic framework that provides a set of semantic services to enable external clients to find, translate and integrate thematic information from different geographic datasets in a repository. An optimised version of the algorithm is also described enabling its execution in real time, even with large datasets. The algorithm has been tested in the context of merging datasets with more than 108 spatial units.