# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 6 Limitations  
Despite the promising results and the comprehensive nature of the proposed methodology, several limitations must be acknowledged to contextualize the findings and guide future research. Firstly, the construction of the KG demands significant domain expertise and meticulous attention to detail in the schema definition. The process involves identifying and modeling a wide array of entities, relationships, and properties, which can be time-consuming and complex. This requirement may pose a barrier for organizations or researchers with limited resources or expertise in both domain and graph database technologies. The same challenges occur for the maintenance of the KG. The dynamic nature of the product life cycles and environmental impact factors means that the KG must be regularly updated to remain relevant and accurate. This maintenance requires ongoing effort and resources, which may not be feasible for all organizations, particularly smaller entities or those operating in rapidly changing industries. Furthermore, ensuring semantic consistency and interoperability with existing standards requires ongoing efforts for construction and maintenance, especially as new data become available or as industry standards evolve. Secondly, data integration from sources like the Okobau.dat database, although beneficial, comes with challenges related to data quality and compatibility. Discrepancies in data formats, units of measurement, or varying levels of data granularity can lead to integration problems. Incomplete or outdated data can also affect the robustness of the KG and, consequently, the reliability of the analytical results derived from it. In addition, reliance on advanced language models such as GPT-4 to estimate missing environmental data introduces a degree of uncertainty. Although these models are powerful tools capable of generating plausible estimates based on vast amounts of training data, they may not always capture the specific nuances of novel or highly specialized materials and processes. Estimates are inherently probabilistic and may lack precision when applied to unique cases without sufficient historical data. This limitation can impact the accuracy of the LCA, particularly in industries like 3 DP, where innovation is rapid and new materials or methods are continually emerging. Furthermore, while Cypher queries offer an accessible means for users to interact with the KG and retrieve valuable insights with relative ease, the other analytical applications, such as the Graph Data Science algorithms, Graph RAG, and Causal AI, are more complex and require specialized technical expertise. These advanced analyses involve sophisticated concepts in graph analysis, machine learning, and artificial intelligence. Organizations may face challenges in acquiring or developing the expertise required to fully leverage these tools, which could limit the practical implementation of the methodology in real-world settings. The steep learning curve associated with these advanced techniques may hinder their adoption, particularly in organizations without dedicated data science teams. The case study focused exclusively on 3 DP, a domain that, while illustrative, may limit the generalizability of the methodology. The unique characteristics of 3 DP, such as its diverse materials, processes, and rapid innovation cycles, mean that the findings may not directly translate to other industries or sectors. There is a need for further comprehensive investigations to implement and verify the methodology across diverse contexts, aiming to evaluate its flexibility and efficacy on a broader scale. Lastly, the overall quality, comprehensiveness, and size of the constructed KGs can significantly influence the efficacy and interpretability of this methodology. Insufficient domain coverage, ambiguous or poorly validated relationships, and inconsistencies in taxonomy or schema may undermine the reliability of derived insights.