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

arXiv: https://arxiv.org/abs/2105.01929 · HTML: https://mlsanity.com/paper/2105.01929/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 4 of 4)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Open the Black Box Data-Driven Explanation of Black Box Decision Systems](https://mlsanity.com/paper/1806.09936/) (2018)
- [Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review](https://mlsanity.com/paper/2012.09049/) (2020)
- [Actionable Cognitive Twins for Decision Making in Manufacturing](https://mlsanity.com/paper/2103.12854/) (2021)

## Built on by (most cited first, 5 of 5)

- [Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications](https://mlsanity.com/paper/2203.10794/) (2022)
- [A Review of Explainable Artificial Intelligence in Manufacturing](https://mlsanity.com/paper/2107.02295/) (2021)
- [Enriching Artificial Intelligence Explanations with Knowledge Fragments](https://mlsanity.com/paper/2204.05579/) (2022)
- [Automated Knowledge Graph Learning in Industrial Processes](https://mlsanity.com/paper/2407.02106/) (2024)
- [Knowledge Modelling and Active Learning in Manufacturing](https://mlsanity.com/paper/2107.02298/) (2021)

## Abstract (arXiv)

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