# Neuro-symbolic AI for BPM

- Type: Conference paper
- URL: https://www.gyunam.com/publication/2026-bpm-nesybpm/
- Date: 2026-09-27
- Authors: Gyunam Park, Fabrizio De Santis
- Venue: Business Process Management
- DOI: https://doi.org/10.1007/978-3-032-37777-7_4
- PDF: https://www.gyunam.com/publication/2026-bpm-nesybpm/paper.pdf
- Research lines: [Neuro-symbolic process learning](https://www.gyunam.com/research-line/neuro-symbolic-process-learning/index.md)

## Abstract

Process analytics has become predominantly data-driven: deep sequence models such as LSTMs and Transformers now dominate predictive and prescriptive process monitoring and deliver strong average-case accuracy. These models are, however, opaque, require large amounts of data, and do not incorporate the process knowledge that organizations already hold in the form of regulations, declarative constraints, and reference process models. The symbolic side of Business Process Management (BPM) carries exactly this knowledge but is brittle on noisy, real-world event data. Neuro-symbolic AI (NeSy) aims to bridge these two traditions, and a growing body of BPM work has begun to use it. This tutorial gives the BPM community an accessible overview of the relationship between BPM and NeSy. It introduces Kautz's architectural taxonomy of NeSy and the Learning from Logical Constraints paradigm together with its main implementation, Logic Tensor Networks. It then traces how NeSy attaches to concrete BPM analytics tasks through a sequence of case studies spanning compliance-aware prediction, robust rule injection, healthcare outcome prediction, and prescriptive analytics. The tutorial highlights the failure modes that practitioners must avoid when injecting domain knowledge into deep process models, examines how to evaluate whether injected knowledge actually helps, and closes with open problems and a practical decision aid for when NeSy is worth adopting.

## Practical problem

Organizations that run predictive or prescriptive process analytics rely on deep sequence models that forecast next activities, outcomes or remaining times from event logs. These models are opaque, need large amounts of data, and ignore the regulations, declarative constraints and reference process models the organization already holds, so they can predict behavior that violates known rules, most sharply where compliant behavior is rare and the stakes are high. The symbolic side of business process management carries exactly that knowledge but breaks down on the noisy, incomplete logs that real systems produce. Practitioners need to understand when and how to combine the two traditions, and how to avoid injecting knowledge in ways that make a model worse than having no knowledge at all.

## Research questions

1. What is neuro-symbolic artificial intelligence and where does it attach to the predictive and prescriptive process analytics pipeline?
2. How does injecting domain knowledge into deep process models help, and how and when does it fail?
3. How can practitioners evaluate whether injected knowledge actually helps and decide whether a neuro-symbolic approach is worth adopting?

## Contributions

- A tutorial that organizes neuro-symbolic work in business process management (BPM) using Kautz's six-type architectural taxonomy and the Learning from Logical Constraints paradigm.
- An introduction to Logic Tensor Networks and their Real Logic formalism sufficient to read and write first-order axioms that compile into training signals for deep models.
- A sequence of case studies spanning compliance-aware prediction, two-stage training with rule pruning, intensive-care mortality prediction, prescriptive analytics (NeSyPA) and neuro-symbolic anomaly detection.
- An analysis of failure modes and open problems: vacuous satisfaction, the soft-compliance gap, sequential constraints under fuzzy logic, and the conditions under which knowledge utility is observable.
- A five-question decision aid (data scarcity, rule availability, regulatory exposure, tolerable opacity, deployment horizon) for judging when neuro-symbolic methods pay off.

## Key findings

- The tutorial reports no new experiments; it synthesizes published results in which output refinement improves F1 by up to 18.4% on Sepsis and 10.1% on the Business Process Intelligence Challenge 2012 log with only 4.55% compliant training traces.
- It shows that naive logic losses can backfire: an unguarded Logic Tensor Network drops F1 to 18.79% on Sepsis through vacuous satisfaction, and two-stage training with analytical rule pruning recovers 75.82%.
- It argues that Type-4 integration yields training-time soft compliance rather than runtime guarantees, that fuzzy aggregation handles atemporal but not sequential constraints, and that knowledge pays off mainly when data is scarce and reliable rules exist.