Abstract
The end-to-end model has a wide range of applications in the fields of image recognition, natural language processing and speech recognition. The main benefit of this model is that the structure is a black box, and industrial users need only amend the inputs and outputs to obtain better performance for various applications. Nevertheless, in some contexts (e.g., medical-related tasks), the results of model generation require interpretability; furthermore, it is difficult to obtain the rich relationships of the hierarchical structure of the input, for the kind of data that are common in real life. In order to deal with these issues, this paper proposes an ontology-based deep belief network model to extend neural networks, using an approach that can be applied to hierarchical data. Multi-parameter data with hierarchical semantics are used in an experiment, and the results show that this model has the ability to handle hierarchical data and provide a certain level of interpretability.
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Liu, X., Chen, R., Tong, Q. et al. An ontology-based deep belief network model. Computing 104, 1017–1032 (2022). https://doi.org/10.1007/s00607-021-01021-w
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DOI: https://doi.org/10.1007/s00607-021-01021-w