Abstract
Since different ontology matchers do not necessarily find the same correct correspondences, usually several competing matchers are applied to the same pair of ontology entities to increase evidence towards a potential match or mismatch. How to select, combine and tune various ontology matchers, so-called ontology meta-matching, is one of the main challenges in ontology matching domain. In recent years, Evolutionary Algorithm (EA) based ontology meta-matching technique has become the state-of-the-art methodology to solve the ontology meta-matching problem, but it suffers from some defects like the slow convergence, premature convergence and the huge memory consumption. To overcome these drawbacks, in this paper, a Compact EA (CEA) based ontology meta-matching technique is proposed, which makes use of a probabilistic representation of the population to perform the optimization process. In particular, we construct an optimal model for the ontology meta-matching problem, propose a problem-specific CEA to optimize the aggregating weights of various matchers, and utilize a Cross Sum Quality Measure (CSQM) to adaptively extract the final alignment. The experimental results show that our approach outperforms other EA based ontology matching techniques and Ontology Alignment Evaluation Initiative (OAEI 2016)’s participants.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (Nos. 61503082 and 61403121), Natural Science Foundation of Fujian Province (No. 2016J05145), Scientific Research Startup Foundation of Fujian University of Technology (No. GY-Z15007), Fujian Province outstanding Young Scientific Researcher Training Project (No. GY-Z160149) and Fundamental Research Funds for the Central Universities (No. 2015B20214).
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Xue, X., Liu, S. (2018). Compact Evolutionary Algorithm Based Ontology Meta-matching. In: Pan, JS., Wu, TY., Zhao, Y., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2017. Smart Innovation, Systems and Technologies, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-319-70730-3_26
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DOI: https://doi.org/10.1007/978-3-319-70730-3_26
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