The aim of the project is to analyse complex holistic business processes with semantic methods of process mining using a hybrid AI approach of machine learning. Explainable artificial intelligence (AI) methods in the form of interactive dashboards are to be used to improve the comprehensibility and transparency of the process models and to realise their refinement and correction through a human-in-the-loop approach. In detail, domain-specific background knowledge from ERP systems and from subject matter experts is to be used and explicitly represented as a knowledge graph. The underlying data model of the event log is to be enriched with semantic meta-information (semantic event log) in order to explicitly represent causal and hierarchical relationships between events and processes and to use them in process reconstruction. Based on machine learning approaches, existing process discovery methods will be adapted and developed as specialised declarative learning methods on the basis of the semantic event log. Finally, the results of the methods will be validated on the basis of business processes of partner companies with real data.
- Investigation of semantic methods for knowledge representation and inference methods for process discovery approaches.
- Semantic representation of causality relations and process properties in knowledge graphs, especially semantic event log.
- Development of machine learning methods for process discovery (white box approach) with inductive logic programming (ILP) methods.
- Explained interactive machine learning methods for intelligent dashboards with a human-in-the-loop approach.
- Conducting studies and experiments as part of a usability study to evaluate the research results.
We are always looking for students who would like to contribute to the KIGA project in the form of a thesis, a project or as a student assistant. If you are interested, please feel free to contact us.