A Branch and Bound Algorithm to Scale Alignment of Large Ontologies

Increasingly, ontologies are being developed and exposed on the Web to support a variety of applications, including biological knowledge sharing, enhanced search and discovery, and decision support. This proliferation of new Web knowledge sources is resulting in a growing need for integration and enrichment of these sources. Automated and semi-automated solutions to aligning ontologies are emerging that address this growing need with very promising results. However, only very recently, solutions for scalability of ontology alignment have begun to emerge. The goal of this research is to investigate scalability issues in alignment of large-scale ontologies. We present an alignment algorithm that bounds processing by selecting optimal subtrees to align and show that this significantly improves efficiency without major reduction in precision. We apply the algorithm in conjunction with our approach that includes modeling ontology alignment in a Support Vector Machine.