Enhancing Deep Learning with Semantics: an application to manufacturing time series analysis
Xin Yuan Huang, Cécilia Zanni-Merk, Bruno Crémilleux · Procedia Computer Science · 2019
Manufacturing enterprises are engaged in implementing new technologies to enhance their manufacturing lines in a smart way. These new technologies give manufacturing enterprises the knowledge, understanding, insight and foresight to improve products, processes and decisions, thereby creating a competitive advantage. In this paper, we explore the use of semantics to enhance deep learning models. We propose an ontology-based LSTM neural network, in which the deep architecture is designed with an ontology to extract high-level cognitive features and stacked LSTM layers for learning temporal dependencies. Our model is applied to a real manufacturing data set with multivariate time series for classification problems. The experiments show that our model can improve performance compared with conventional methods.