Augmenting Modelers with Semantic Autocompletion of Processes
This addresses the challenge for business process modelers who lack domain expertise by providing an incremental tool to improve design efficiency.
The paper tackles the problem of assisting business process modelers by developing a semantic autocompletion method that recommends next elements during process design, based on embedding sub-processes as vectors and finding semantic similarities, and shows it is accurate across various domains.
Business process modelers need to have expertise and knowledge of the domain that may not always be available to them. Therefore, they may benefit from tools that mine collections of existing processes and recommend element(s) to be added to a new process that they are constructing. In this paper, we present a method for process autocompletion at design time, that is based on the semantic similarity of sub-processes. By converting sub-processes to textual paragraphs and encoding them as numerical vectors, we can find semantically similar ones, and thereafter recommend the next element. To achieve this, we leverage a state-of-the-art technique for embedding natural language as vectors. We evaluate our approach on open source and proprietary datasets and show that our technique is accurate for processes in various domains.