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Knowledge Graph-Based Explainable Artificial Intelligence for Business Process Analysis.
- Source :
- International Journal of Semantic Computing; Jun2023, Vol. 17 Issue 2, p173-197, 25p
- Publication Year :
- 2023
-
Abstract
- For critical operational decisions (e.g. consulting services), explanations and interpretable results of powerful Artificial Intelligence (AI) systems are becoming increasingly important. Knowledge graphs possess a semantic model that integrates heterogeneous information sources and represents knowledge elements in a machine-readable form. The integration of knowledge graphs and machine learning methods represents a new form of hybrid intelligent systems that benefit from each other's strengths. Our research aims at an explainable system with a specific knowledge graph architecture that generates human-understandable results even when no suitable domain experts are available. Against this background, the interpretability of a knowledge graph-based explainable AI approach for business process analysis is focused. We design a framework of interpretation, show how interpretable models are generated by a single case study and evaluate the applicability of our approach by different expert interviews. Result paths on weaknesses and improvement measures related to a business process are used to produce stochastic decision trees, which improve the interpretability of results. This can lead to interesting consulting self-services for clients or be applied as a device for accelerating classical consulting projects. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1793351X
- Volume :
- 17
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- International Journal of Semantic Computing
- Publication Type :
- Academic Journal
- Accession number :
- 164246355
- Full Text :
- https://doi.org/10.1142/S1793351X23600024