Abstract
On the basis of our former work based on Compact Evolutionary Algorithm (CEA), in this paper, we introduce parallel technology into Compact Evolutionary Algorithm (CEA), and design an Parallel Compact Evolutionary Algorithm (PCEA) based ontology matching technology to further improve the efficiency of solving the ontology meta-matching problem. Comparing with CEA based approach, our approach is able to further reduce the time and memory consumption while at the same time ensures the correctness and completeness of the alignments. The Experiment is carried out on the OAEI 2015 benchmark, and the results show that our approach is able to reduce the executing time and main memory consumption of the tuning process while at the same time ensures the quality of the alignment.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (No. 61503082) and Natural Science Foundation of Fujian Province (No. 2016J05145 and 2016J05146).
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Xue, X., Tsai, PW., Zhang, LL. (2017). Using Parallel Compact Evolutionary Algorithm for Optimizing Ontology Alignment. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_19
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DOI: https://doi.org/10.1007/978-3-319-48490-7_19
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