Knowledge Graph-Based Explainable Artificial Intelligence for Business Process Analysis
Anne Füßl, Volker Nissen, Stefan Horst Heringklee
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.