Process Trace Querying using Knowledge Graphs and Notation3

TL;DR

This paper presents a method for process trace querying by converting event logs into Knowledge Graphs using RDF and N3, enabling expressive, flexible, and extensible log exploration.

Contribution

It introduces a novel approach to process mining log exploration by leveraging semantic Knowledge Graphs and general-purpose query languages, enhancing expressivity and flexibility.

Findings

Supports complex trace constraints in N3

Enables flexible serialization of event logs

Offers extensible framework for log querying

Abstract

In process mining, a log exploration step allows making sense of the event traces; e.g., identifying event patterns and illogical traces, and gaining insight into their variability. To support expressive log exploration, the event log can be converted into a Knowledge Graph (KG), which can then be queried using general-purpose languages. We explore the creation of semantic KG using the Resource Description Framework (RDF) as a data model, combined with the general-purpose Notation3 (N3) rule language for querying. We show how typical trace querying constraints, inspired by the state of the art, can be implemented in N3. We convert case- and object-centric event logs into a trace-based semantic KG; OCEL2 logs are hereby "flattened" into traces based on object paths through the KG. This solution offers (a) expressivity, as queries can instantiate constraints in multiple ways and arbitrarily constrain attributes and relations (e.g., actors, resources); (b) flexibility, as OCEL2 event logs can be serialized as traces in arbitrary ways based on the KG; and (c) extensibility, as others can extend our library by leveraging the same implementation patterns.