Abstract
Ontology, as an efficient semantic model, is widely used in various fields of engineering science and semantic similarity. Calculation is the core of the ontology algorithm. Ontology similarity calculation via sparse vector, which can be used for high-dimensional data and big data processing. This paper two experiment results showed algorithm by using squared loss function to express the error term, Finally, two experiment results showed the effective of our ontology sparse vector learning algorithm for ontology similarity computation in specific engineering applications.
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Acknowledgements
This work was supported by the General project of social science and technology development in Dongguan 2017, number: 2017507154412, Scientific research project of Guangdong University Science & Technology, number: GKY-2016KYYB-10.
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Huang, X.X., Gong, S. (2018). Ontology Sparse Vector Learning Algorithm. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_3
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DOI: https://doi.org/10.1007/978-981-13-1648-7_3
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