Optimizing Ontology Alignment by Using Compact Genetic Algorithm
Xingsi Xue, Jianhua Liu, Pei‐Wei Tsai, Xianyin Zhan, Aihong Ren · 2015
In this paper, we propose a novel approach based on Compact Genetic Algorithm (CGA) to address the problem of optimizing the aggregation of three different basic similarity measures (Syntactic Measure, Linguistic Measure and Taxonomy-based Measure), and get a single similarity metric in the process of ontology matching. Comparing with conventional Genetic Algorithm (GA), the proposed method is able to dramatically reduce the time and memory consumption while at the same time ensures the correctness and completeness of the alignments. Experiment results show that the proposed approach is effective.