Proceedings-Artikel

Supporting Business Process Modeling Using RNNs for Label Classification

Philip Hake; Manuel Zapp; Peter Fettke; Peter Loos
In: Flavius Frasincar; Ashwin Ittoo; Le Minh Nguyen; Elisabeth Métais (Hrsg.). Natural Language Processing and Information Systems. International Conference on Applications of Natural Language to Information Systems (NLDB-2017), 22nd International Conference on Applications of Natural Language to Information Systems, June 21-23, Liège, Belgium, Pages 283-286, Lecture Notes in Computer Science (LNCS), Vol. 10260, Springer International Publishing, 2017.

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.

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