ProcK: Machine Learning for Knowledge-Intensive Processes
This work addresses the need for more flexible and applicable predictive process monitoring in organizational databases, though it is incremental as it builds on existing methods by incorporating knowledge graphs.
The authors tackled the problem of predictive process monitoring by developing ProcK, a method that integrates sequential event logs with knowledge graphs using a hybrid sequence and graph neural network model, achieving state-of-the-art performance and improved predictive power when a knowledge graph is available.
We present a novel methodology to build powerful predictive process models. Our method, denoted ProcK (Process & Knowledge), relies not only on sequential input data in the form of event logs, but can learn to use a knowledge graph to incorporate information about the attribute values of the events and their mutual relationships. The idea is realized by mapping event attributes to nodes of a knowledge graph and training a sequence model alongside a graph neural network in an end-to-end fashion. This hybrid approach substantially enhances the flexibility and applicability of predictive process monitoring, as both the static and dynamic information residing in the databases of organizations can be directly taken as input data. We demonstrate the potential of ProcK by applying it to a number of predictive process monitoring tasks, including tasks with knowledge graphs available as well as an existing process monitoring benchmark where no such graph is given. The experiments provide evidence that our methodology achieves state-of-the-art performance and improves predictive power when a knowledge graph is available.