Ontology-driven enhancement of process mining with domain knowledge
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2023
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Fill, Hans-Georg
Gerber, Aurona
Lenat, Doug
Stolle, Reinhard
Harmelen, Frank van
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023)
Themenheft
DOI der Originalpublikation
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
Sun SITE, Informatik V, RWTH Aachen
Verlagsort / Veranstaltungsort
Aachen
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Process mining is a technique used to analyze and understand business processes. It uses as input the event log, a type of data used to represent the sequence of activities occurring within a business process. An event log typically contains information such as the case ID, the performed activity’s name, the activity’s timestamp, and other data associated with the activity. By analyzing event logs, organizations can gain a deeper understanding of their business processes, identify areas for improvement, and make data-driven decisions to optimize their operations. However, as the event logs contain data collected from different systems involved in the process, such as ERP, CRM, or WfMS systems, they often lack the necessary context and knowledge to analyze and fully comprehend business processes. By extending the event logs with domain knowledge, organizations can gain a more complete and accurate insight into their business processes and make more informed decisions about optimizing them. This paper presents an approach for enhancing process mining with domain knowledge preserved in domain-specific OWL ontologies. Event logs are typically stored in structured form in relational databases. This approach first converts the process data into an event log which is then mapped with ontology concepts. The ontology contains classes and individuals representing background knowledge of the domain, which supports the understanding of the data. A class for the specific activities forms the link between the event log and the ontology. In this manner, it is possible to map the domain knowledge to a particular case and activity. This allows to determine conditions that must be satisfied for executing tasks and to prune discovered process models if they are too complex. This approach is demonstrated using data from the student admission process at FHNW and has been implemented in Protégé.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
27.03.2023
Enddatum der Konferenz
29.03.2023
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Diamond
Lizenz
Zitation
EICHELE, Simon, Knut HINKELMANN und Maja SPAHIC, 2023. Ontology-driven enhancement of process mining with domain knowledge. In: Andreas MARTIN, Hans-Georg FILL, Aurona GERBER, Knut HINKELMANN, Doug LENAT, Reinhard STOLLE und Frank van HARMELEN (Hrsg.), Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023). Aachen: Sun SITE, Informatik V, RWTH Aachen. 2023. Verfügbar unter: https://doi.org/10.26041/fhnw-7371