Knowledge Graphs (KGs) offer a robust solution for integrating heterogeneous sustainability data and enhancing decision-making transparency. However, current KG-based sustainability assessments predominantly focus on carbon-related indicators and rarely address uncertainty and data completeness. This limitation restricts their ability to support holistic and robust sustainability evaluations. This paper presents an extended sustainability knowledge graph framework that integrates environmental, water-related, and social indicators while explicitly modeling uncertainty and completeness at the indicator level.Building upon an existing KG architecture, the proposed methodology incorporates water footprint assessment in accordance with ISO 14046 principles and social sustainability indicators derived from Social Life Cycle Assessment frameworks. Uncertainty is modeled using indicator-specific distributions and propagated through Monte Carlo simulation, enabling uncertainty-aware sustainability ranking. The methodology is demonstrated through a case study in polymer-based Additive Manufacturing. The results show that water and social indicators can significantly influence sustainability rankings and that uncertainty may reduce the robustness of conclusions derived from deterministic assessments. By enabling integrated, uncertainty-aware sustainability analysis, the proposed framework supports more informed decision-making in sustainable manufacturing.
Hauck et al. (2026) studied this question.