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Process Trace Querying using Knowledge Graphs and Notation3

William Van Woensel

TL;DR

The paper addresses expressive log exploration in process mining by converting event logs into a semantic knowledge graph (ELKG) built on RDF, with Notation3 (N3) rules for trace querying. It handles both CCEL and OCEL2, introducing perspective-based trace extraction from OCEL2 via object-path reasoning and flattening to CCEL when needed. Trace constraints—such as activity-occurrence and sequential relations—are implemented as recursive N3 backward-chaining rules, enabling flexible, rule-driven queries over traces. A preliminary performance evaluation demonstrates conversion times, trace extraction scale, and query responsiveness, illustrating the practical viability and potential for extensibility in semantic log querying for process mining.

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.

Process Trace Querying using Knowledge Graphs and Notation3

TL;DR

The paper addresses expressive log exploration in process mining by converting event logs into a semantic knowledge graph (ELKG) built on RDF, with Notation3 (N3) rules for trace querying. It handles both CCEL and OCEL2, introducing perspective-based trace extraction from OCEL2 via object-path reasoning and flattening to CCEL when needed. Trace constraints—such as activity-occurrence and sequential relations—are implemented as recursive N3 backward-chaining rules, enabling flexible, rule-driven queries over traces. A preliminary performance evaluation demonstrates conversion times, trace extraction scale, and query responsiveness, illustrating the practical viability and potential for extensibility in semantic log querying for process mining.

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.
Paper Structure (16 sections, 1 figure)

This paper contains 16 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Case- and Object-Centric Event Logs
  4. Resource Description Framework (RDF) and Notation3 (N3)
  5. Converting Event Logs into ELKG
  6. Case-Centric Event Logs (CCEL)
  7. Object-Centric Event Logs (OCEL2)
  8. Querying Process Traces
  9. Types of trace constraints
  10. Implementing trace constraints in N3
  11. Activity Occurrence.
  12. Sequential Relations.
  13. Querying Entities using Rules and Other Constraints
  14. Preliminary Performance Evaluation
  15. Related Work
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Object types and event activities for a P2P use case. Object types are rectangles with O2O links between them; event activities are dashed ellipses with E2O links to object types. Red arrows represent an object path that collects all relevant events.