Abstract
In many real life applications, the volume of available data is insufficient for training deep neural networks. One of the approaches to overcome this obstacle is to introduce symbolic knowledge to assist machine-learning models based on neural networks. In this paper, the problem of enterprise model classification by neural networks is considered to study the potential of the approach mentioned above. A number of experiments are conducted to analyze what level of accuracy can be achieved, how much training data is required and how long the training process takes, when the neural network-based model is trained without symbolic knowledge vs. when different architectures of embedding symbolic knowledge into neural networks are used.
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The research is funded by the Russian Science Foundation (project 22-11-00214).
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Smirnov, A., Shilov, N., Ponomarev, A. (2023). Facilitating Enterprise Model Classification via Embedding Symbolic Knowledge into Neural Network Models. In: Conte, D., Fred, A., Gusikhin, O., Sansone, C. (eds) Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science, vol 1875. Springer, Cham. https://doi.org/10.1007/978-3-031-39059-3_18
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