The increasing use of artificial intelligence (AI) to develop innovative value propositions and improve business processes raises concerns about the lack of transparency and explainability of AI systems. The article proposes a Knowledge graph-Based eXplainable Artificial Intelligence (XAI) approach to Process Analysis (KBXAI-PA) that addresses these challenges by providing a user-centered interface for explanation and allowing human experts to interact with the system. The hybrid XAI approach is based on a knowledge graph architecture that combines symbolic approaches for structured knowledge with sub-symbolic methods of machine learning. Algorithmic procedures are traceable in the form of decision trees, and analysis results are presented in a human-readable form. An interactive eXplanation User Interface (XUI) ensures an intuitive result representation of identified weaknesses as well as appropriate improvement measures of analyzed business processes and enables human-in-the-loop interactions to adapt learned models in a human-centric way. The article presents the design and evaluation of the knowledge graph-based XAI approach to process analysis, which demonstrates its potential to improve the acceptance and trustworthiness of AI-based analysis tools in the consulting context.