# Knowledge Management for Injection Molding Defects by a Knowledge Graph
## 3 Results and Discussion
### Search Efficiency  
This study used the F1_Score as the search efficiency evaluation index. The F1_Score consists of the measurement score calculated by considering the TP, FP, and FN of the search results, as shown in Equation (1). Table 1 shows the search performance comparison results of the search engine constructed in this study and Neo4j's Cypher in different search depths. According to the performance of Neo4j's Cypher in different search depths, the search method, using Neo4j's Cypher, could not have a satisfactory performance in the different search depths, because the injection molding knowledge was composed of an unfixed quantity of triples, thus, it was impossible to obtain the complete knowledge by setting a fixed search length. If the search depth was increased, while the complete injection molding knowledge might be found, the search result would contain excessive non-associated nodes, leading to a low search performance. The F1_Score of the search engine constructed in this study was 1.0 in most search tests, which proves that the search engine constructed in this study can filter non-associated nodes, and find the complete injection molding knowledge.  
\[\text{F1\_Score}=\text{TP}/(\text{TP}+1/2\text{ (FP}+\text{FN)}) \tag{1}\]  
TP: Number of associated nodes in the search results.  
FP: Number of non-associated nodes in the search results.  
FN: Number of associated nodes not in the search results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Node Type**} & \multirow{2}{*}{**Node Name**} & \multirow{2}{*}{**Total Number of**} & \multicolumn{3}{c}{**Comparison**} \\  & & **Associated Nodes** & & **F1\_Score of the** & **F1\_Score of the** & **F1\_Score of the** \\  & & & **Rule Based** & **Neo4j Cypher** & **Neo4j Cypher** \\  & & & **Search Engine** & **(Search Depth = 3)** & **(Search Depth = 5)** \\ \hline Problem & Flash & 26 & 1.000 & 0.721 & 0.366 \\ Problem & Splay & 51 & 1.000 & 0.649 & 0.415 \\ Problem & Short shot & 87 & 1.000 & 0.492 & 0.697 \\ Phenomenon & Reduce the flow & 30 & 1.000 & 0.565 & 0.282 \\  & resistance & & & & \\ Phenomenon & Reduce the plastic & 36 & 1.000 & 0.458 & 0.310 \\ Phenomenon & Increase the venting & & 17 & 1.000 & 0.415 & 0.159 \\  & capacity of the mold & & & & \\ Action & Decrease the mold & & & & \\  & temperature & & & & \\ Action & Increase the coolant & & & & \\  & flow rate & & & & \\ Action & Increase the nozzle & & & & \\  & temperature & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search performance comparison.  
Figure 14: Action node search result.  
In addition to using the search engine constructed in this study to find the complete injection molding knowledge, and to filter the non-associated nodes, the Neo4j Cypher should be combined with manual filtering. Therefore, this study used the search engine and Neo4j's Cypher + manual filtering to find all of the associated nodes, and the required time of the two methods was compared.  
Table 2 shows the comparison results. It was observed that using a search engine to retrieve the graph can save a lot of time in comparison to Neo4j's Cypher + manual filtering, and the knowledge retrieval efficiency was increased.