A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Multi-layered approach to aligning heterogeneous ontologies
Multi-layered approach to aligning heterogeneous ontologies
Validating Multi-column Schema Matchings by Type
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Towards effective geographic ontology matching
GeoS'07 Proceedings of the 2nd international conference on GeoSpatial semantics
Structure-based methods to enhance geospatial ontology alignment
GeoS'07 Proceedings of the 2nd international conference on GeoSpatial semantics
Global Interoperability Using Semantics, Standards, Science and Technology (GIS3T)
Computer Standards & Interfaces
Geographically-typed semantic schema matching
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Deontic Logic Based Ontology Alignment Technique for E-Learning
International Journal of Intelligent Information Technologies
Towards ontological similarity for spatial hierarchies
Proceedings of the Third ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
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The alignment of separate ontologies by matching related concepts continues to attract great attention within the database and artificial intelligence communities, especially since semantic heterogeneity across data sources remains a widespread and relevant problem. In particular, the Geographic Information System (GIS) domain presents unique forms of semantic heterogeneity that require a variety of matching approaches. Our approach considers content-based techniques for aligning GIS ontologies. We examine the associated instance data of the compared concepts and apply a content-matching strategy to measure similarity based on value types based on N-grams present in the data. We focus special attention on a method applying the concepts of mutual information and N-grams by developing 2 separate variations and testing them over GIS dataset including multi-jurisdictions. In order to align concepts, first we find the appropriate columns. For this, we will exploit mutual information between two columns based on the type distribution of their content. Intuitively, if two columns are semantically same, type distribution should be very similar. We justify the conceptual validity of our ontology alignment technique with a series of experimental results that demonstrate the efficacy and utility of our algorithms on a wide-variety of authentic GIS data.