Augmenting Modelers with Semantic Autocompletion of Processes

TL;DR

This paper introduces a semantic autocompletion method for business process modeling that recommends process elements based on textual similarity, aiding modelers with limited domain knowledge.

Contribution

It presents a novel process autocompletion approach using natural language embeddings to recommend process elements during design time.

Findings

High accuracy in process element recommendation across domains

Effective use of natural language embeddings for process similarity

Applicable to both open source and proprietary datasets

Abstract

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.