Integration of Ontology Data through Learning Instance Matching
Chao Wang, Jie Lü, Guangquan Zhang · 2006
Information integration with the aid of ontology can roughly be divided into two levels: schema level and data level. Most research has been focused on the schema level, i.e., mapping/matching concepts and properties in different ontologies with each other. However, the data level integration is equally important, especially in the decentralized semantic Web environment. Noticing that ontology data (in the form of instances of concepts) from different sources often have different perspectives and may overlap with each other, we develop a matching method that utilizes the features of ontology and employs the machine learning approach to integrate those instances. By exploring ontology features, this method performs better than other general methods, which is revealed in our experiments. Through the process that implements the matching method, ontology data can be integrated together to offer more sophisticated services