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Ontology construction and mapping of multi-source heterogeneous data based on hybrid neural network and autoencoder

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Abstract

In big data era, multi-source heterogeneous data become the biggest obstacle to data sharing due to its high dimension and inconsistent structure. Using text classification to solve the ontology construction and mapping problem of multi-source heterogeneous data can not only reduce manual operation, but also improve the accuracy and efficiency. This paper proposes an ontology construction and mapping scheme based on hybrid neural network and autoencoder. Firstly, the proposed text classification method uses the multi-core convolutional neural network to capture local features and uses the improved Bidirectional Long Short-Term Memory network to compensate for the shortcomings of the convolutional neural network that cannot obtain context-related information. Secondly, a similarity matching method is used for ontology mapping, which integrate autoencoder to improve anti-interference ability. We have carried out several sets of experiments to test the validity of the proposed ontology construction and mapping scheme.

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Funding

The authors acknowledge the National Natural Science Foundation of China (61373160), the Natural Science Foundation of Hebei Province (F2021210003), the research project of Hebei Province Education Department (QN2020197) and the research project of Hebei science and technology information processing laboratory.

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Authors and Affiliations

  1. School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, Hebei, China

    Wenbin Zhao, Zijian Fu, Tongrang Fan & Jiaqi Wang

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  1. Wenbin Zhao
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  2. Zijian Fu
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  3. Tongrang Fan
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  4. Jiaqi Wang
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Correspondence to Tongrang Fan.

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Zhao, W., Fu, Z., Fan, T. et al. Ontology construction and mapping of multi-source heterogeneous data based on hybrid neural network and autoencoder. Neural Comput & Applic 35, 25131–25141 (2023). https://doi.org/10.1007/s00521-023-08373-8

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  • DOI: https://doi.org/10.1007/s00521-023-08373-8

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