INTEGRATING LARGE-SCALE ONTOLOGIES FOR ECONOMIC AND FINANCIAL SYSTEMS VIA ADAPTIVE CO-EVOLUTIONARY NSGA-II

Abstract

The identification, prediction, management, and control of economic and financial systems render extremely challenging tasks, which require comprehensively integrating the knowledge of different expert systems. Ontology, as a state-of-the-art knowledge modeling technique, has been extensively applied in the domain of economics and finance. However, due to ontology engineers’ subjectivity, ontology suffers from the heterogeneity issue, which hampers the co-operation among the intelligent expert system based on them. To address this issue, ontology matching for finding heterogeneous concept pairs between two ontologies has been rapidly developed. It is difficult to find the perfect ontology alignment that satisfies the needs of all decision-makers. Therefore, Multi-Objective Evolutionary Algorithm, such as Non-dominated Sorting Genetic Algorithm (NSGA-II), attracts many researchers’ attention. However, when facing large-scale ontology matching problems, NSGA-II tends to fall into local optimal solutions due to the large search space. To effectively address this drawback, we model the large-scale ontology problem as a nonlinear optimization problem, and propose an Adaptive Co-Evolutionary NSGA-II (ACE-NSGA-II) to deal with it. Compared with NSGA-II, ACE-NSGA-II introduces a co-evolutionary mechanism to increase the diversity of populations in order to decrease the probability of premature convergence. In particular, ACE-NSGA-II uses an adaptive population maintenance strategy to assign more resources toward the dominant ones in order to improve the solution efficiency for solving large-scale ontology matching. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark and anatomy track to test the effectiveness of ACE-NSGA-II, and the resulting experiment demonstrated that compared to NSGA-II and OAEI’s participants, ACE-NSGA-II is able to find better alignment.