# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 4 Case Study: 3D Printing
### KG Quality Management  
The construction of KGs within the current product development phase presents unique quality challenges, primarily due to the limited availability of readily accessible data. Typically, quality concerns arise when KGs are built using automated tools. However, in the absence of sufficient data, the efficacy of automated data crawlers is significantly diminished, necessitating the manual construction of KGs. This manual approach inherently reduces the likelihood of common redundancies associated with automation, as it allows for more deliberate and controlled data integration. Nevertheless, the dynamic nature of KGs within this framework introduces potential risks of both redundancies and omissions. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Metric** & **Original GWP Values** & **Estimated GWP Values** \\ \hline Count & 637 & 637 \\ Mean & 39.13 & 45.32 \\ Standard deviation & 486.45 & 417.73 \\ Minimum & \(-\)5674.33 & \(-\)1768.63 \\ Median & 0.12 & 0.11 \\ Maximum & 19,900.00 & 6110.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics of original and estimated GWP values. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Attribute** & **Example 1: Facade Paint (Dispersion Paint) in** & **Example 2: Asphalt Base Layer in Modul D** \\ \hline \multirow{4}{*}{GPT-4o Estimation} & \multirow{4}{*}{“Given that this product is a type of paint in} & \multirow{4}{*}{“Considering that the ‘asphalt base layer’ is a type of asphalt layer used in Modul D, and noting that similar materials in end-of-life stages often have GWP values close to zero or slightly negative due to recycling credits, I estimate the GWP for the ‘asphalt base layer’ to be approximately \(-\)0.025,”} \\ \cline{1-1} \cline{2-3} Actual GWP & 0.0032 & \(-\)0.0263 \\ Estimated GWP & 0.004 & \(-\)0.025 \\ AE & 0.0008 & 0.0013 \\ REP & 25.19\% & 4.96\% \\ \hline \hline \end{tabular}
\begin{tabular}{l l} \hline \hline
**Attribute** & **Example 1: Facade Paint (Dispersion Paint) in** & **Example 2: Asphalt Base Layer in Modul D** \\ \hline \multirow{4}{*}{GPT-4o Estimation} & \multirow{4}{*}{“Given that this product is a type of paint in} & \multirow{4}{*}{“Considering that the ‘asphalt base layer’ is a type of asphalt layer used in Modul D, and noting that similar materials in end-of-life stages often have GWP values close to zero or slightly negative due to recycling credits, I estimate the GWP for the ‘asphalt base layer’ to be approximately \(-\)0.025,”} \\ \cline{1-1} \cline{2-3} Actual GWP & 0.0032 & \(-\)0.0263 \\ Estimated GWP & 0.004 & \(-\)0.025 \\ AE & 0.0008 & 0.0013 \\ REP & 25.19\% & 4.96\% \\ \hline \hline \end{tabular}
\begin{tabular}{l l} \hline \hline
**Attribute** & **Example 1: Facade Paint (Dispersion Paint) in** & **Example 2: Asphalt Base Layer in Modul D** \\ \hline \multirow{4}{*}{GPT-4o Estimation} & \multirow{4}{*}{“Given that this product is a type of paint in} & \multirow{4}{*}{“Considering that the ‘asphalt base layer’ is a type of asphalt layer used in Modul D, and noting that similar materials in end-of-life stages often have GWP values close to zero or slightly negative due to recycling credits, I estimate the GWP for the ‘asphalt base layer’ to be approximately \(-\)0.025,”} \\ \cline{1-1} \cline{2-3} Actual GWP & 0.0032 & \(-\)0.0263 \\ Estimated GWP & 0.004 & \(-\)0.025 \\ AE & 0.0008 & 0.0013 \\ REP & 25.19\% & 4.96\% \\ \hline \hline \end{tabular}
\begin{tabular}{l l} \hline \hline
**Comment** & **High relative error due to the low actual GWP** \\ \multicolumn{1}{c}{} & **Influative error shows a close match between the estimate and the actual GWP value.** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: GPT-4o GWP estimations and validation results for two examples. As the KG evolves over time, there is an inherent risk of inadvertently duplicating existing information or omitting previously incorporated data, which can adversely affect the consistency and integrity of the KG. Furthermore, temporal aspects introduce significant quality challenges.