Abstract
This paper offers a synthesized approach of solving the shortage of the traditional similarity in ontology mapping. First, it selects high correlation concepts by Hirst-St-Onge semantic relativity algorithms, in order to reduce the complexity of the account. Then according to the characteristic of the ontology concept, we designs a synthesized method through calculating the respective similarity in name, attribute and instance of concepts, and works out weight by Sigmoid function. Experiment data indicates that it makes the better accuracy than the traditional methods.
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© 2013 Springer-Verlag Berlin Heidelberg
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Liu, Hl., Liu, Q. (2013). Synthesized Algorithms of Concept Similarity Based on the Semantic Correlation Prerequisite. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_53
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DOI: https://doi.org/10.1007/978-3-642-37502-6_53
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37501-9
Online ISBN: 978-3-642-37502-6
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