@inproceedings{bach-etal-2022DEXA,
 title={Automated Process Knowledge Graph Construction from BPMN models},
 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 cap
turing 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 framework 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 framework 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.},
 author={Stefan Bachhofner and Elmar Kiesling and Kate Revoredo and Philipp Waibel and Axel Polleres},
 note={Full paper},
 doi = {https://doi.org/10.1007/978-3-031-12423-5_3},
 booktitle={3rd DEXA conferences and workshops (DEXA2022)},
 year=2022,
 month=aug,
 day={22-24},
}

{"_id":"QYZGieeMLQFccfwNR","bibbaseid":"bachhofner-kiesling-revoredo-waibel-polleres-automatedprocessknowledgegraphconstructionfrombpmnmodels-2022","author_short":["Bachhofner, S.","Kiesling, E.","Revoredo, K.","Waibel, P.","Polleres, A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Automated Process Knowledge Graph Construction from BPMN models","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 cap turing 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 framework 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 framework 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.","author":[{"firstnames":["Stefan"],"propositions":[],"lastnames":["Bachhofner"],"suffixes":[]},{"firstnames":["Elmar"],"propositions":[],"lastnames":["Kiesling"],"suffixes":[]},{"firstnames":["Kate"],"propositions":[],"lastnames":["Revoredo"],"suffixes":[]},{"firstnames":["Philipp"],"propositions":[],"lastnames":["Waibel"],"suffixes":[]},{"firstnames":["Axel"],"propositions":[],"lastnames":["Polleres"],"suffixes":[]}],"note":"Full paper","doi":"https://doi.org/10.1007/978-3-031-12423-5_3","booktitle":"3rd DEXA conferences and workshops (DEXA2022)","year":"2022","month":"August","day":"22-24","bibtex":"@inproceedings{bach-etal-2022DEXA,\n title={Automated Process Knowledge Graph Construction from BPMN models},\n 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\nfar attracted limited attention in this context, despite its usefulness for cap\nturing the context in which data are generated. Such knowledge is commonly\nexpressed in diagrammatic languages and the resulting models can not readily\nbe used in knowledge graph construction. We propose BPMN2KG to address\nthis problem. BPMN2KG is a transformation framework from BPMN2.0\nprocess models into knowledge graphs. Thereby BPMN2KG creates a frame\nfor process-centric data integration and analysis with this transformation.\nWe motivate and evaluate our transformation framework with a real-world\nindustrial use case focused on quality management in plastic injection molding\nfor the automotive sector. We use BPMN2KG for process-centric integration\nof dispersed production systems data that results in an integrated knowledge\ngraph that can be queried using SPARQL, a standardized graph-pattern\nbased query language. By means of several example queries, we illustrate how\nthis knowledge graph benefits data contextualization and integrated analysis.},\n author={Stefan Bachhofner and Elmar Kiesling and Kate Revoredo and Philipp Waibel and Axel Polleres},\n note={Full paper},\n doi = {https://doi.org/10.1007/978-3-031-12423-5_3},\n booktitle={3rd DEXA conferences and workshops (DEXA2022)},\n year=2022,\n month=aug,\n day={22-24},\n}\n\n","author_short":["Bachhofner, S.","Kiesling, E.","Revoredo, K.","Waibel, P.","Polleres, A."],"key":"bach-etal-2022DEXA","id":"bach-etal-2022DEXA","bibbaseid":"bachhofner-kiesling-revoredo-waibel-polleres-automatedprocessknowledgegraphconstructionfrombpmnmodels-2022","role":"author","urls":{},"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"www.polleres.net/mypublications.bib","dataSources":["gixxkiKt6rtWGoKSh","cBfwyqsLFQQMc4Fss"],"keywords":[],"search_terms":["automated","process","knowledge","graph","construction","bpmn","models","bachhofner","kiesling","revoredo","waibel","polleres"],"title":"Automated Process Knowledge Graph Construction from BPMN models","year":2022}