Optimizing ontology alignments through a Memetic Algorithm using both MatchFmeasure and Unanimous Improvement Ratio

• Published in: Artificial intelligence Vol. 223; pp. 65 - 81
• Main Authors: Xue, Xingsi, Wang, Yuping
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2015
• Subjects: Algorithms; Alignment; Approximation; Bias; Expert systems; Knowledge representation; MatchFmeasure; Matching; Memetic Algorithm; Ontology alignment; Optimization; Unanimous Improvement Ratio; MatchFmeasure; Unanimous Improvement Ratio; Memetic Algorithm; Ontology alignment

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...