Integrating Cross-lingual Ontologies through Co-Evolutionary Algorithm
Lili Huang, Leong Ko · Mobile Information Systems · 2022
To support the collaborations among intelligent applications, it is necessary to integrate various ontologies which are developed and maintained by different organizations. One of the challenges is that different ontologies in the same application domain might use different languages to describe the same concept, which yields the cross-lingual heterogeneity problem, i.e., how to map two identical entities in different languages. To address this problem, this work proposes a problem-specific co-evolutionary algorithm (CoEA)-based matching technique. In particular, we first propose a parallel aggregating framework to aggregate different SMs and then construct a continuous optimization model for defining the problem of cross-lingual ontology integration. To better trade off the algorithm’s exploitation and exploration, we use two competitive subpopulations to, respectively, execute the exploitation and exploration. The experiment utilizes OAEI’s multifarm track for testing purpose, and the experimental results show that CoEA is able to effectively integrate various cross-lingual ontologies.