Ontology alignment using artificial neural network for large-scale ontologies

Abstract

Achieving high match accuracy for a large variety of ontologies, considering a single matcher is often not sufficient for high match quality. Therefore, combining the corresponding weights for different semantic aspects, reflecting their different importance (or contributions) becomes unavoidable for ontology matching. Combining multiple measures into a single similarity metric has been traditionally solved using weights determined manually by an expert, or calculated through general methods (e.g. average or sigmoid function), however this does not provide a flexible and self-configuring matching tool. In this paper, an intelligent combination using Artificial Neural Network (ANN) as a machine learning-based method to ascertain how to combine multiple similarity measures into a single aggregated metric with the final aim of improving the ontology alignment quality is proposed. XMap++ is applied to benchmark and anatomy tests at OAEI campaign 2012. Results show that neural network boosts the performance in most cases, and that the proposed novel approach is competitive with top-ranked system.