Supporting Business Process Modeling Using RNNs for Label Classification

Abstract

Business Process Models describe the activities of a company in an abstracted manner. Typically, the labeled nodes of a process model contain only sparse textual information. The presented approach uses an LSTM network to classify the labels contained in a business process model. We first apply a Word2Vec algorithm to the words contained in the labels. Afterwards, we feed the resulting data into our LSTM network. We train and evaluate our models on a corpus consisting of more than 24,000 labels of business process models. Using our trained classification model, we are able to distinguish different constructs of a process modeling language based on their label. Our experimental evaluation yields an accuracy of 95.71% on the proposed datasets.