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Predictive Process Monitoring (PPM) aims at leveraging historic processexecution data to predict how ongoing executions will continue up to theircompletion. In recent years, PPM techniques for the prediction of the nextactivities have matured significantly, mainly thanks to the use of NeuralNetworks (NNs) as a predictor. While their performance is difficult to beat inthe general case, there are specific situations where background processknowledge can be helpful. Such knowledge can be leveraged for improving thequality of predictions for exceptional process executions or when the processchanges due to a concept drift. In this paper, we present a Symbolic[Neuro]system that leverages background knowledge expressed in terms of a proceduralprocess model to offset the under-sampling in the training data. Morespecifically, we make predictions using NNs with attention mechanism, anemerging technology in the NN field. The system has been tested on severalreal-life logs showing an improvement in the performance of the predictiontask.

Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction