Machine Learning
Automatic ontology mapping for agent communication
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Handling semantic heterogeneities using declarative agreements
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Ontology-driven geographic information systems
Ontology-driven geographic information systems
Querying heterogeneous land use data: problems and potential
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
Semantic integration and visualization for geospatial data portals
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Category mapping for the automatic integration of category-constrained web search
International Journal of Business Intelligence and Data Mining
Hierarchical directory mapping for category-constrained meta-search
Journal of Intelligent Information Systems
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Semantic heterogeneity of information is a major barrier of information and system interoperability. Defining ontology of data and mapping ontologies among heterogeneous information repositories is one approach to achieve interoperability. This paper focuses on the ontology mapping of categorical information, which usually have a tree structure with categories and subcategories. Subcategories can be considered as the definition of their upper level categories. Methods of automatic mapping of categorical information using Naïve Bayes classifier are discussed, and improved algorithms for categorical ontologies mapping are proposed and compared to a standard word-by-word matching algorithm.