# Reliable Semantic Communication System Enabled by Knowledge Graph
## 4 Experimental Results
### Experimental Result Analysis  
#### 4.2.1 Performance of the Proposed Semantic Communication System  
First, we investigated the effects of the number of triplets on the semantic performance under different SNRs. Here, we considered three strategies, one strategy was to send the first triplet (named \"Send the 1 st triplet\"), and the other two schemes involved sending 50% triplets (named \"Send 50% triplets\") and 100% triplets (named \"Send 100% triplets\"), respectively. Moreover, we compared these three strategies with the benchmark and an end-to-end deep learning-based communication system proposed in [23] (named DeepNN). Figure 6 shows the performance of the semantic similarity versus the SNR in this experiment. From Figure 6, \"Send the 1 st triplet\" has the best semantic similarity under a low SNR because it uses the most resources to protect the first triplet. With the SNR becoming better, \"Send 50% triplets\" has better performance because \"Send the 1 st triplet\" transmits limited semantics, and the accuracy of the scheme \"Send 100% triplets\" cannot be guaranteed due to the channel distortion. The semantic similarity of \"Send 100% triplets\" is above the others at a high SNR, which is reasonable due to the error-free transmission when the channel quality is good. Meanwhile, all three strategies outperformed the benchmark and DeepNN in their superior SNR regions. According to Figure 6, it is reasonable to send the most important triplet in the low SNR region, send 50% triplets in the medium SNR region, and send 100% triplets in the high SNR region. Figure 7 demonstrates the relationship between the SNR and the BLEU score under the AWGN channel. From Figure 7, the proposed model performs better under a low SNR in terms of the 1-gram BLEU score or 2-gram BLEU score due to the protection of important triplets. Moreover, after converting the received triplets into sentences by using the proposed semantic restoration method, \"Proposed model without AT\" outperforms \"Proposed model without AT and SR\" for all SNR regimes. However, the performance of the proposed model is inferior to the traditional communication system in the high SNR region in Figure 7. This is because the proposed semantic restoration method attempts to recover the same semantic rather than the same sentence structure. For example, the transmitted sentence is \"[PERSON] was the founder of Apple\", and the restored sentence is \"[PERSON] founded Apple\". Although the two sentences are semantically consistent, the BLEU score of the proposed scheme is poor. Figure 6: Semantic similarity versus the SNR under the AWGN channel, with send the 1 st triplet; send 50% triplets; send 100% triplets; Huffman + LDPC; DeepNN. Figure 8 shows the relationship between the SNR and the BLEU score under the Rayleigh fading channel. All scores in Figure 8 are lower than the scores in Figure 7 because of the severe impacts of Rayleigh fading. However, the proposed model significantly improves performance compared to the benchmark. From Figure 8, the proposed model outperforms the benchmark across the SNR range over the Rayleigh fading channel, either the 1-gram BLEU score or the 2-gram BLEU score. It reflects that our proposed model is more robust to complex communication environments. Meanwhile, since \"Proposed model without AT\" and \"Send 100% triplets\" are identical in the high SNR region, the results of the proposed model and \"Proposed model without AT\" are the same when the SNR is higher than 2 dB. Since BLEU is an evaluation metric that calculates scores based on word matching, sentence sizes can affect the performance of our proposed model. To research this, we divided the transmitted sentences into three groups--sentence length between 0 and 15, sentence length between 15 and 30, and sentence length greater than 30. Figure 9 shows the relationship between the SNR and the (1-gram) BLEU score under the AWGN channel and the Rayleigh fading channel, respectively.