Process consistency checking (PCC), an interdiscipline of natural language processing (NLP) and business process management (BPM), aims to quantify the degree of (in)consistencies between graphical and textual descriptions of a process. However, previous studies heavily depend on a great deal of complex expert-defined knowledge such as alignment rules and assessment metrics, thus suffer from the problems of low accuracy and poor adaptability when applied in open-domain scenarios. To address the above issues, this paper makes the first attempt that uses deep learning to perform PCC. Specifically, we proposed TraceWalk, using semantic information of process graphs to learn latent node representations, and integrates it into a convolutional neural network (CNN) based model called TraceNet to predict consistencies. The theoretical proof formally provides the PCC's lower limit and experimental results demonstrate that our approach performs more accurately than state-of-the-art baselines.
Many organizations maintain textual process descriptions alongside graphical process models. The purpose is to make process information accessible to various stakeholders, including those who are not familiar with reading and interpreting the complex execution logic of process models. Despite this merit, there is a clear risk that model and text become misaligned when changes are not applied to both descriptions consistently. For organizations with hundreds of different processes, the effort required to identify and clear up such conflicts is considerable. To support organizations in keeping their process descriptions consistent, we present an approach to automatically identify inconsistencies between a process model and a corresponding textual description. Our approach detects cases where the two process representations describe activities in different orders and detect process model activities not contained in the textual description. A quantitative evaluation with 53 real-life model-text pairs demonstrates that our approach accurately identifies inconsistencies between model and text. HighlightsWe propose an approach to detect conflicts between textual and model-based process descriptions.The approach is fully automatic based on tailored natural language processing techniques.Quantitative evaluation demonstrates the applicability of the approach on real-life data. With the aim of having individuals from different backgrounds and expertise levels examine the operations in an organization, different representations of business processes are maintained. To have these different representations aligned is not only a desired feature, but also a real challenge due to the contrasting nature of each process representation. In this paper we present an efficient technique for aligning a textual description and a graphical model of a process. The technique is grounded on using natural language processing techniques to extract linguistic features of each representation, and encode the search as a mathematical optimization encoded using Integer Linear Programming (ILP) whose resolution ensures an optimal alignment between both descriptions. The technique has been implemented and the experiments witness the significance of the approach with respect to the state-of-the-art technique for the same task.  Text-based and model-based process descriptions have their own particular strengths and, as such, appeal to different stakeholders. For this reason, it is not unusual to find within an organization descriptions of the same business processes in both modes. When considering that hundreds of such descriptions may be in use in a particular organization by dozens of people, using a variety of editors, there is a clear risk that such models become misaligned. To reduce the time and effort needed to repair such situations, this paper presents the first approach to automatically identify inconsistencies between a process model and a corresponding textual description. Our approach leverages natural language processing techniques to identify cases where the two process representations describe activities in different orders, as well as model activities that are missing from the textual description. A quantitative evaluation with 46 real-life model-text pairs demonstrates that our approach allows users to quickly and effectively identify those descriptions in a process repository that are inconsistent.