Scaling alignment of large ontologies

: In recent years, the number of ontologies shared on the Web has increased dramatically, supporting a growing set of applications such as biological knowledge sharing, enhanced search and discovery, and decision support. This proliferation of Web knowledge sources is resulting in an increasing need for integration and enrichment of these knowledge sources. Automated solutions to aligning ontologies are emerging that address this growing need with 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 improves efficiency without significant reduction in precision. We apply the algorithm in conjunction with our approach that includes modeling ontology alignment in a Support Vector Machine.

• Type: Journal
• Year: 2010
• Where: IJBRA
• Authors: Suzette Stoutenburg, Jugal Kalita, Kaily Ewing, Lisa Hines
• Keywords: IJBRA 2010, Ontologies, Ontology Alignment, Support Vector Machine