Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs

Ava Swevels; Remco M. Dijkman; Dirk Fahland

doi:10.1007/978-3-031-41620-0_11

Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs

Ava Swevels, Remco M. Dijkman, Dirk Fahland

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

2 Downloads (Pure)

Abstract

Complete event data is essential to perform rich analysis. However, real-life systems might fail in recording the (correct) case identifiers the system has operated on, resulting in incomplete event data. We aim to infer missing case identifiers of events by considering the physical constraints of the process which previous work has failed to do. We extended Event Knowledge Graphs (EKGs) with concepts for context and rule-based inference. We use the extended EKGs to model event data in its physical context and define five inference rules to infer identifiers of physical objects in a process. We evaluate the effectiveness of the rules on data from the IC manufacturing industry using conformance checking. Initially, none of the traces were complete. Our method inferred a case identifier for 95% of the events resulting in 88% complete traces.

Original language	English
Title of host publication	Business Process Management
Subtitle of host publication	21st International Conference, BPM 2023, Utrecht, The Netherlands, September 11–15, 2023, Proceedings
Editors	Chiara Di Francescomarino, Andrea Burattin, Christian Janiesch, Shazia Sadiq
Place of Publication	Cham
Publisher	Springer
Pages	180-197
Number of pages	18
ISBN (Electronic)	978-3-031-41620-0
ISBN (Print)	978-3-031-41619-4
DOIs	https://doi.org/10.1007/978-3-031-41620-0_11
Publication status	Published - 2023
Event	21st International Conference on Business Process Management, BPM 2023 - Utrecht, Netherlands Duration: 11 Sept 2023 → 15 Sept 2023

Publication series

Name	Lecture Notes in Computer Science (LNCS)
Volume	14159
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Business Process Management, BPM 2023
Abbreviated title	BPM 2023
Country/Territory	Netherlands
City	Utrecht
Period	11/09/23 → 15/09/23

Funding

The research underlying this paper was partially supported by NXP Semiconductors and by AutoTwin EU GA n. 101092021.

Funders	Funder number
NXP Semiconductors	101092021

Keywords

Contextual Information
Event Knowledge Graph
Inference Rule
Log repair
Modeling
Physical Constraints

Access to Document

10.1007/978-3-031-41620-0_11

Cite this

Swevels, A., Dijkman, R. M., & Fahland, D. (2023). Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs. In C. Di Francescomarino, A. Burattin, C. Janiesch, & S. Sadiq (Eds.), Business Process Management: 21st International Conference, BPM 2023, Utrecht, The Netherlands, September 11–15, 2023, Proceedings (pp. 180-197). (Lecture Notes in Computer Science (LNCS); Vol. 14159). Springer. https://doi.org/10.1007/978-3-031-41620-0_11

Swevels, Ava ; Dijkman, Remco M. ; Fahland, Dirk. / Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs. Business Process Management: 21st International Conference, BPM 2023, Utrecht, The Netherlands, September 11–15, 2023, Proceedings. editor / Chiara Di Francescomarino ; Andrea Burattin ; Christian Janiesch ; Shazia Sadiq. Cham : Springer, 2023. pp. 180-197 (Lecture Notes in Computer Science (LNCS)).

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abstract = "Complete event data is essential to perform rich analysis. However, real-life systems might fail in recording the (correct) case identifiers the system has operated on, resulting in incomplete event data. We aim to infer missing case identifiers of events by considering the physical constraints of the process which previous work has failed to do. We extended Event Knowledge Graphs (EKGs) with concepts for context and rule-based inference. We use the extended EKGs to model event data in its physical context and define five inference rules to infer identifiers of physical objects in a process. We evaluate the effectiveness of the rules on data from the IC manufacturing industry using conformance checking. Initially, none of the traces were complete. Our method inferred a case identifier for 95% of the events resulting in 88% complete traces.",

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Swevels, A , Dijkman, RM & Fahland, D 2023, Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs. in C Di Francescomarino, A Burattin, C Janiesch & S Sadiq (eds), Business Process Management: 21st International Conference, BPM 2023, Utrecht, The Netherlands, September 11–15, 2023, Proceedings. Lecture Notes in Computer Science (LNCS), vol. 14159, Springer, Cham, pp. 180-197, 21st International Conference on Business Process Management, BPM 2023, Utrecht, Netherlands, 11/09/23. https://doi.org/10.1007/978-3-031-41620-0_11

Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs. / Swevels, Ava ; Dijkman, Remco M.; Fahland, Dirk.
Business Process Management: 21st International Conference, BPM 2023, Utrecht, The Netherlands, September 11–15, 2023, Proceedings. ed. / Chiara Di Francescomarino; Andrea Burattin; Christian Janiesch; Shazia Sadiq. Cham: Springer, 2023. p. 180-197 (Lecture Notes in Computer Science (LNCS); Vol. 14159).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs

AU - Swevels, Ava

AU - Dijkman, Remco M.

AU - Fahland, Dirk

PY - 2023

Y1 - 2023

N2 - Complete event data is essential to perform rich analysis. However, real-life systems might fail in recording the (correct) case identifiers the system has operated on, resulting in incomplete event data. We aim to infer missing case identifiers of events by considering the physical constraints of the process which previous work has failed to do. We extended Event Knowledge Graphs (EKGs) with concepts for context and rule-based inference. We use the extended EKGs to model event data in its physical context and define five inference rules to infer identifiers of physical objects in a process. We evaluate the effectiveness of the rules on data from the IC manufacturing industry using conformance checking. Initially, none of the traces were complete. Our method inferred a case identifier for 95% of the events resulting in 88% complete traces.

AB - Complete event data is essential to perform rich analysis. However, real-life systems might fail in recording the (correct) case identifiers the system has operated on, resulting in incomplete event data. We aim to infer missing case identifiers of events by considering the physical constraints of the process which previous work has failed to do. We extended Event Knowledge Graphs (EKGs) with concepts for context and rule-based inference. We use the extended EKGs to model event data in its physical context and define five inference rules to infer identifiers of physical objects in a process. We evaluate the effectiveness of the rules on data from the IC manufacturing industry using conformance checking. Initially, none of the traces were complete. Our method inferred a case identifier for 95% of the events resulting in 88% complete traces.

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T3 - Lecture Notes in Computer Science (LNCS)

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A2 - Di Francescomarino, Chiara

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Swevels A , Dijkman RM , Fahland D. Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs. In Di Francescomarino C, Burattin A, Janiesch C, Sadiq S, editors, Business Process Management: 21st International Conference, BPM 2023, Utrecht, The Netherlands, September 11–15, 2023, Proceedings. Cham: Springer. 2023. p. 180-197. (Lecture Notes in Computer Science (LNCS)). doi: 10.1007/978-3-031-41620-0_11

Inferring Missing Entity Identifiers from Context Using Event Knowledge Graphs

Abstract

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Keywords

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