Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams

TL;DR

This paper presents a neuro-symbolic method combining argumentation frameworks and machine learning to efficiently interpret low-level process event streams, especially under data scarcity.

Contribution

It introduces a data-efficient approach that refines sequence tagging with an argumentation-based reasoner, leveraging prior knowledge to improve interpretation accuracy.

Findings

The approach improves interpretation accuracy with limited annotated data.

The method effectively leverages prior knowledge to compensate for scarce training data.

Experimental results confirm the effectiveness of the neuro-symbolic approach.

Abstract

Monitoring and analyzing process traces is a critical task for modern companies and organizations. In scenarios where there is a gap between trace events and reference business activities, this entails an interpretation problem, amounting to translating each event of any ongoing trace into the corresponding step of the activity instance. Building on a recent approach that frames the interpretation problem as an acceptance problem within an Abstract Argumentation Framework (AAF), one can elegantly analyze plausible event interpretations (possibly in an aggregated form), as well as offer explanations for those that conflict with prior process knowledge. Since, in settings where event-to-activity mapping is highly uncertain (or simply under-specified) this reasoning-based approach may yield lowly-informative results and heavy computation, one can think of discovering a sequence-tagging model, trained to suggest highly-probable candidate event interpretations in a context-aware way. However, training such a model optimally may require using a large amount of manually-annotated example traces. We then propose a data-efficient neuro-symbolic approach to the problem, where the candidate interpretations returned by the example-driven sequence tagger is refined by the AAF-based reasoner. This allows us to also leverage prior knowledge to compensate for the scarcity of example data, as confirmed by experimenftal results.