# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 7 Future Work  
Based on the findings and acknowledging the limitations of this study, several avenues for future research and development emerge that could improve the robustness, applicability, and ease of use of the methodology. First, regular maintenance of the KG is vital due to the dynamic nature of product life cycles. Future research could explore the implementation of automated update mechanisms using continuous integration and deployment (CI/CD) pipelines. These systems could detect changes in data sources or industry standards and update the KG accordingly. Developing community-driven platforms where multiple stakeholders contribute to the KG's upkeep could distribute the maintenance workload and keep the KG current. Second, future work could focus on integrating real-time data acquisition methods, such as sensors and Internet of Things (IoT) technologies, to collect empirical data during the production process. This integration would enable the dynamic updating of the KG and more precise LCA calculations. Third, incorporating uncertainty quantification methods into the KG analysis could provide insight into the confidence levels of the results, helping decision-makers in risk assessment. Furthermore, to address the uncertainty associated with using advanced language models for estimating missing environmental data, future work could focus on fine-tuning these models using domain-specific datasets to create specialized estimation tools. By tailoring language models to specific industries or materials through fine-tuning, we can significantly enhance their accuracy in generating environmental impact estimations. This process involves training the models on specialized datasets that reflect the unique characteristics and nuances of the target domain, thereby improving their contextual understanding and predictive capabilities. To make advanced analytical applications more accessible, the development of user-friendly interfaces and visualization tools is key. Future initiatives could include creating dashboards and graphical tools that allow non-expertsto perform complex analyses without deep technical knowledge. Educational programs and training workshops could help build the necessary expertise within organizations, promoting the wider adoption of these advanced techniques. Expanding the applicability of the methodology beyond the 3 DP domain is another important direction. Conducting more case studies in diverse industries such as automotive, aerospace, consumer electronics, and sustainable energy systems would test the methodology's adaptability and identify industry-specific challenges. Such cross-sector analyses could lead to the development of standardized schemas and best practices that facilitate a wider implementation. Enhancing the accuracy of environmental impact metrics is essential. Future research must emphasize not only robust standards and practices for KG construction, validation, and refinement but also the development of systematic metrics to evaluate graph quality, coverage, and scalability. In line with the strategy proposed by [33], integrating LLMs into the workflow can provide a more powerful means of reducing redundancy and improving completeness. LLMs can identify and correct inconsistencies and duplicates--going beyond simple query-based validation--while also enriching the KG with additional information. This ensures a more accurate, coherent, and comprehensive dataset, ultimately increasing the KG's overall utility. Lastly, investigating the ethical considerations and aspects of data privacy associated with the use of language models and KGs in LCA is important. In forthcoming research endeavors, it is imperative to scrutinize the ramifications associated with data sharing, safeguarding proprietary information, and adhering to legal frameworks such as the General Data Protection Regulation (GDPR).