Generative adversarial learning for optimizing ontology alignment
Xingsi Xue, Qihan Huang
Abstract
Abstract The edge computing in knowledge‐defined network (KDN) is a kind of distributed computing architecture, and the format of edge resources stored in different edge computing nodes are different, which yields the data heterogeneity problem and hampers the interaction between edge nodes. Ontology is considered as the solution of data heterogeneity on Semantic Web, and matching ontologies is a high‐efficiency method of addressing the data heterogeneity problem. Ontology meta‐matching investigates how to determine the optimal weights to aggregate multiple similarity measures to achieve high‐quality ontology alignment, which is a challenge about nonlinear mathematical problem in ontology matching domain. To face this challenge, unsupervised learning method such as generative adversarial network (GAN) becomes an effective methodology. GAN consists of two models of different targets that are opposed to each other in training to produce the final best result. To improve the GAN's efficiency, this work further proposes a GAN with simulated annealing algorithm (SA‐GAN), where the stagnation counter is introduced to accelerate GAN's the convergence speed. The experiment uses the famous benchmark in the ontology domain, and the comparisons with the advanced ontology matching systems shows that SA‐GAN is able to find high‐quality alignments to help build bridges between edge nodes on edge computing.