Extracting Business Objects and Activities from Labels of German Process Models

Abstract

To automatically analyze and compare elements of process models, investigating the natural language contained in the labels of the process models is inevitable. Therefore, the adaption of well-established techniques from the field of natural language processing to Business Process Management has recently experienced a growth. Our work contributes to the field of natural language processing in business process models by providing a word dependency-based technique for the extraction of business objects and activities from German labeled process models. Furthermore, we evaluate our approach by implementing it in the RefMod-Miner toolset and measuring the quality of the information extraction in business process models. In three different evaluation scenarios, we show the strengths of the dependency-based approach and give an outlook on how further research could benefit from the approach.