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The increasing adoption of artificial intelligence requires accurateforecasts and means to understand the reasoning of artificial intelligencemodels behind such a forecast. Explainable Artificial Intelligence (XAI) aimsto provide cues for why a model issued a certain prediction. Such cues are ofutmost importance to decision-making since they provide insights on thefeatures that influenced most certain forecasts and let the user decide if theforecast can be trusted. Though many techniques were developed to explainblack-box models, little research was done on assessing the quality of thoseexplanations and their influence on decision-making. We propose an ontology andknowledge graph to support collecting feedback regarding forecasts, forecastexplanations, recommended decision-making options, and user actions. This way,we provide means to improve forecasting models, explanations, andrecommendations of decision-making options. We tailor the knowledge graph forthe domain of demand forecasting and validate it on real-world data.

XAI-KG: knowledge graph to support XAI and decision-making in manufacturing