# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 5 Experimentation and Verification of the Proposed Method
### Method Assessment
#### 5.3.2 Algorithm Performance Evaluation  
This section evaluates the domain knowledge classification algorithm that implements the mapping of the space station's operational data atlas to the mission-planning ontology network, comparing the metrics of recall, accuracy, and F-value of the method with the edit distance algorithm, the Jaccard coefficient algorithm, and the Euclidean algorithm, as defined in Equations (7)-(9).  
\[\text{Precision: }P=\frac{TP}{FP}\times 100\% \tag{7}\]  
\[\text{Recall rate: }R=\frac{TP}{TP+FN}\times 100\% \tag{8}\]  
\[\text{Harmonic mean of exact and recall: }F=\frac{2\times P\times R}{P+R}\times 100\% \tag{9}\]  
where TP is the correct number of matches, FP is all matches found by the method, and FN is the number of matches recommended by the method.  
Figure 14 is the comparison result of the domain knowledge classification algorithm based on Ullman subgraph matching with the edit distance, Jaccard coefficient algorithm, and Euclidean metric algorithm. The results show that the accuracy P, recall R, and F measurements of the domain knowledge categorization algorithm based on Ullman subgraph matching are above 90%, exceeding the results of the other three algorithms. This is because the algorithm synthesizes the intrinsic semantic features and extrinsic structural attribute features of the knowledge nodes to improve the accuracy of domain knowledge recognition and categorization.  
In order to further investigate the performance of the algorithm, especially the effect of subgraph size on the domain knowledge categorization results, we design comparative experiments to explore the trends of accuracy, recall, and F-value of the domain knowledge categorization algorithm based on Ullman subgraph matching under different subgraph sizes. The core of the Ullmann-subgraph-matching algorithm is matching by edges, so we use the number of attribute edges to measure the size of the graph. The results of the comparison test are shown in Figure 15.  
The results show that as the number of attribute edges increases, the accuracy, recall, and F-value of the domain knowledge classification algorithm based on Ullman subgraph matching all show an upward trend. When the number of attribute edges increases to a certain extent, the increase in these indicators gradually slows down and finally stabilizes at a high level. This trend shows that as the scale of the graph increases, the performance of the algorithm in domain knowledge classification gradually optimizes, but when the number of attribute edges reaches a certain scale, the performance tends to saturate. In addition, since the computational complexity of the algorithm also grows exponentially,  
Figure 14: Comparison of algorithms for domain knowledge categorization.  
the larger the scale of the graph, the longer the required calculation time. Therefore, in specific applications, the two factors of accuracy and computational complexity should be comprehensively considered to make the scale of the subgraph template as reasonable as possible to achieve the effect of balancing performance and efficiency.