A study on automatic ontology mapping of categorical information
Naijun Zhou · International Conference on Digital Government Research · 2003
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 Naive Bayes classifier are discussed, and improved algorithms for categorical ontologies mapping are proposed and compared to a standard word-by-word matching algorithm.