ProcK: Machine Learning for Knowledge-Intensive Processes

TL;DR

ProcK introduces a hybrid machine learning approach combining sequence models and knowledge graphs to improve predictive process monitoring by leveraging static and dynamic organizational data.

Contribution

The paper presents ProcK, a novel end-to-end method that integrates knowledge graphs with sequence models for enhanced predictive process monitoring.

Findings

Achieves state-of-the-art performance on benchmark tasks.

Improves predictive accuracy with knowledge graph integration.

Demonstrates flexibility across different process monitoring scenarios.

Abstract

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.