Semantic-Enhanced Learning (SEL) on Artificial Neural Networks Using the Example of Semantic Location Prediction

Recent machine learning models find a widespread use whether in respect of data mining and forecasting or in the classification domain. However, real-world situations comprise complex estimation tasks that carry a certain semantic load and bring a certain degree of fuzziness with them. This is a fuzziness which humans, due to their common sense knowledge and their personal experience, can easily understand by linking the underlying concepts together, while machines may from scratch not. A vast amount of both training data and time are necessary in order for a computational model to be capable of learning such kind of relations and adapting to new situations. In this work, we show that letting explicit semantic knowledge flow into a predictive model leads to an improved performance with regard to training time, accuracy and robustness. In particular, we propose adding an auxiliary semantic layer to the model, whose role is to provide it with information about the semantic interrelation of the treated classes creating in this way shortcuts and saving valuable training time while improving its quality at the same time. We explore several versions of our approach and we illustrate their functionality in a semantic location prediction scenario using 2 different real-world datasets.