The document presents an adaptive machine learning framework for ontology matching that uses semi-supervised learning with user interaction to reduce the cost of manual annotation. The framework initializes with a pre-alignment and then iterates between training multiple learners and getting user feedback to label additional samples. Experiments on matching directories show the framework requires fewer labeled samples than supervised learning alone but achieves comparable performance to other matching systems.

1. An Adaptive Machine Learning Framework with User Interaction for Ontology Matching Hoai-Viet To 1 , Ryutaro Ichise 2 , and Hoai-Bac Le 1 1 Ho Chi Minh University of Science, Vietnam 2 National Institute of Informatics, Japan 04/26/10