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Automated Process Knowledge Graph Construction from BPMN Models

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Database and Expert Systems Applications (DEXA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13426))

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Abstract

Enterprise knowledge graphs are increasingly adopted in industrial settings to integrate heterogeneous systems and data landscapes. Manufacturing systems can benefit from knowledge graphs as they contribute towards implementing visions of interconnected, decentralized and flexible smart manufacturing systems. Process knowledge is a key perspective which has so far attracted limited attention in this context, despite its usefulness for capturing the context in which data are generated. Such knowledge is commonly expressed in diagrammatic languages and the resulting models can not readily be used in knowledge graph construction. We propose BPMN2KG to address this problem. BPMN2KG is a transformation tool from BPMN2.0 process models into knowledge graphs. Thereby BPMN2KG creates a frame for process-centric data integration and analysis with this transformation. We motivate and evaluate our transformation tool with a real-world industrial use case focused on quality management in plastic injection molding for the automotive sector. We use BPMN2KG for process-centric integration of dispersed production systems data that results in an integrated knowledge graph that can be queried using SPARQL, a standardized graph-pattern based query language. By means of several example queries, we illustrate how this knowledge graph benefits data contextualization and integrated analysis. In a broader context, we contribute towards the vision of a process-centric enterprise Knowledge Graph (KG). BPMN2KG is available at https://short.wu.ac.at/BPMN2KG, and the sample queries and results at https://short.wu.ac.at/DEXA2022.

This research has received funding from the Teaming.AI project, which is part of the European Union’s Horizon 2020 research and innovation program under grant agreement No 957402.

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Notes

  1. 1.

    BPMN2KG is available at https://short.wu.ac.at/BPMN2KG.

  2. 2.

    http://teamingai-project.eu.

  3. 3.

    https://short.wu.ac.at/BPMN2BBO, https://short.wu.ac.at/BPMN2BBOExt, and https://short.wu.ac.at/BPMN2BBOExtANDBBO.

  4. 4.

    https://www.python.org/.

  5. 5.

    RMLMapper: https://github.com/RMLio/rmlmapper-java with commit 54bf875.

  6. 6.

    You can find the query (Q4) at https://short.wu.ac.at/DEXA2022-Q4.

  7. 7.

    https://git.ai.wu.ac.at/teaming-ai/extensible-event-stream-to-knowledge-graph.

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Acknowledgement

This work has also received funding from the Teaming.AI project in the European Union’s Horizon 2020 research and innovation program under grant agreement No 95740.

Author information

Authors and Affiliations

  1. Institute for Data, Process and Knowledge Management, Vienna University of Economics and Business, Vienna, Austria

    Stefan Bachhofner, Elmar Kiesling, Kate Revoredo, Philipp Waibel & Axel Polleres

  2. Austrian Center for Digital Production, Vienna, Austria

    Elmar Kiesling

  3. Complexity Science Hub Vienna, Vienna, Austria

    Axel Polleres

Authors
  1. Stefan Bachhofner
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  2. Elmar Kiesling
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  3. Kate Revoredo
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  4. Philipp Waibel
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  5. Axel Polleres
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Corresponding author

Correspondence to Stefan Bachhofner .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

A SPARQL Queries and Results

A SPARQL Queries and Results

Due to space constraints, we deleted all prefix statements in the following queries and completely exclude (Q2), (Q3), and (Q4) – you can find all queries with the full syntax and the results at https://short.wu.ac.at/DEXA2022.

Fig. 5.
figure 5

SPARQL query and result for (Q1) showing data flows between activities and data stores.

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Bachhofner, S., Kiesling, E., Revoredo, K., Waibel, P., Polleres, A. (2022). Automated Process Knowledge Graph Construction from BPMN Models. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-12423-5_3

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  • Online ISBN: 978-3-031-12423-5

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