Aggregating Multiple Ontology Similarity Based on IOWA Operator
Different ontology matching approaches which utilize diverse semantic information to bridge heterogenous ontologies perform different adaptability and use value on the same task. Usually, combination of a set of different matching approaches can achieve higher accuracy than single approach. Therefore, a method based on induced ordered weighted averaging operator (IOWA) is proposed in this paper to aggregate the similarities computed by multiple ontology matching methods. It first predicts the confidence of similarity, and then takes it as induce value to assign weight to similarity and sums up all the weighted similarities about a given element pair. Experiment shows the validity and practicability of this method.