# A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication
## 4 Performance Analysis  
This paper analyzed the accuracy and validity of corpus collection and entity extraction. For its corpus, this paper adopted the 50 items collated by DeepSeek. For entity extraction and relationship analysis, 50 corpora are processed with different LLMs and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{Input} & \multicolumn{3}{c}{DeepSeek} & \multicolumn{2}{c}{ERNIE Bot} & \multicolumn{2}{c}{iFLTKE} \\ \cline{2-7}  & Buildings & 10 & Houses & 10 & Houses & 10 \\  & Roads & 9 & Roads & 10 & Roads & 8 \\ Urban & Personnel & 10 & Personnel & 8 & Bridges & 7 \\ Explosion & Power facilities & 8 & Bridges & 9 & Power facilities & 9 \\ Emergency & Bridge fracture & Power & Power & & 8 & Personnel & 9 \\ Rescue & points & 8 & facilities & & & Buildings & 8 \\  & Smoke sources & 7 & & & & & \\  & Flames & 7 & & & & & \\  & Infrastructure & 7 & & & & & \\ \hline \multirow{3}{*}{Output} & Roads & 10 & Roads & 10 & Roads & 10 \\  & Bridges & 9 & Bridges & 8 & Bridges & 8 \\  & Freezing Rain & Vehicles & 7 & Power lines & 9 & Vehicles & 7 \\ Traffic & Power lines & 8 & & & & Trees & 6 \\ Management & Electric towers & 7 & & & & Infrastructure & 7 \\  & Infrastructure & 7 & & & & Power lines & 7 \\  & Road ice zones & 9 & & & & & \\ \hline \multirow{3}{*}{Blizzard} & Roads & 10 & Houses & 10 & Houses & 10 \\  & Buildings & 9 & Roads & 10 & Roads & 9 \\  & Personnel & 9 & Bridges & 9 & Bridges & 8 \\ \cline{1-1}  & Emergency & Power facilities & 8 & Power facilities & 8 & Power facilities & 6 \\ Rescue & Vehicles & 7 & Personnel & 9 & Personnel & 8 \\  & Trees & 7 & & & & Houses & 8 \\  & Civilians & 9 & & & & & \\  & Wind turbine & 6 & & & & \\  & units & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of task inference based on LLMs. different clue templates. Accuracy, recall and F1-scores are used to evaluate the performance of the program. \[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{2}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{3}\]  
\[\text{F1-scores}=\frac{2\text{PR}}{\text{P}+\text{R}} \tag{4}\]  
The meanings of TP, FP and FN are as follows:  
TP: the number of correct triples  
FP: the number of omissive triples  
FN: the number of incorrect triples  
It is critical to note that this paper employed manually processed triples as the ground truth, which serves as the criterion for evaluating the correctness of triples extracted by LLMs. TP denotes the number of triples output by the LLMs that match the ground truth; FN represents the number of triples generated by the LLMs but absent in the ground truth (i.e., spurious triples); FP indicates the number of triples missing in the LLM's output compared to the ground truth. The experimental results are presented in Table 6.  
Experimental evaluations demonstrate distinct performance characteristics across LLMs in entity extraction and relation analysis tasks. While DeepSeek and ERNIE Bot successfully extracted comprehensive triplets through direct instruction, achieving baseline accuracy exceeding 75% with 87% recall rates, iFLTTEK initially failed to meet fundamental task requirements. However, the implementation of few-shot learning methodologies substantially enhanced all models' capabilities, particularly enabling iFLTTEK not only to complete core extraction tasks but also exhibit superior Chinese linguistic processing proficiency. iFLTTEK achieved baseline accuracy exceeding 85% with a 91% recall rate. Overall, the experimental results shown in Table 6 fully illustrate the accuracy and reliability of the scheme in extracting entities. This framework demonstrates robust extraction reliability, with the obtained entities showing precise semantic alignment while maintaining comprehensive corpus coverage. The methodology's effectiveness is further reinforced by using real-world data spanning complete domain representations from verified internet sources.