Optimizing ontology alignments through a Memetic Algorithm using both MatchFmeasure and Unanimous Improvement Ratio - Xue, Xingsi; Wang, Yuping (Artificial intelligence, 2015)
There are three main drawbacks of current evolutionary approaches for determining the weights of ontology matching system. The first drawback is that it is difficult to simultaneously deal with several pairs of ontologies, i.e. finding a universal weight configuration that can be used for different ontology pairs without adjustment. The second one is that a reference alignment between two ontologies to be aligned should be given in advance which could be very expensive to obtain especially when the scale of ontologies is considerably large. The last one arises from f-measure, a generally used evaluation metric of the alignment's quality, which may cause the bias improvement of the solution. To overcome these three defects, in this paper, we propose to use both MatchFmeasure, a rough evaluation metric on no reference alignment to approximate f-measure, and Unanimous Improvement Ratio (UIR), a measure that complements MatchFmeasure, in the process of optimizing the ontology alignments by Memetic Algorithm (MA). The experimental results have shown that the MA using both MatchFmeasure and UIR is effective to simultaneously align multiple pairs of ontologies and avoid the bias improvement caused by MatchFeasure. Moreover, the comparison with state-of-the-art ontology matching systems further indicates the effectiveness of the proposed method.