# Reliable Semantic Communication System Enabled by Knowledge Graph
## 3 System Model
### System Process  
In this section, we introduce the overall process of the proposed semantic communication system. Let \(S=[w_{1},w_{2},\cdots,w_{m}]\) be the transmitted sentence, where \(w_{m}\) is the \(m\)th word in the sentence. As shown in Figure 5, with the help of the proposed semantic extraction method \(S2G_{\theta}(\bullet)\), the transmitter converts the transmitted sentence \(S\) to the knowledge graph \(G\), which can be represented as \(G=S2G_{\theta}(S)\). The knowledge graph \(G\) consists of \(n\) triplets and it can be formulated as \(G=[g_{1},g_{2},\cdots,g_{n}]\). Using the proposed semantic extraction method, the transmitted sentence is converted into a series of triplets. In this process, the semantics of the transmitted sentence are extracted without losing semantics [13]. During transmission, these triplets are independent of each other, which means that errors in some triplets will not affect other triplets. However, in Markov models, once there is a transmission error, the whole transmitted sentence will be affected. Therefore, the proposed semantic communication system is more robust under a low SNR. Moreover, different semantic basic symbols (triplets) have semantic importance in semantic communication, unlike bits or symbols that are treated equally in traditional communication, such as longer-range models and Markov chain-based probabilistic models. These triplets (with semantic importance) should be treated differently. The triplets with important semantics should be allocated with many time slots and bandwidth resources. When the channel quality is extremely poor, instead of transmitting all triplets, which cannot be guaranteed by the channel, it is better to ensure that the most important triplet can be transmitted correctly. When the channel quality is better, the system can adjust the sending content according to semantic importance. Motivated by the different triplets with semantic importance, we sort these triplets according to their semantic similarity scores:  
\[sim_{\text{v}}(s,g_{i})=\frac{\mathbf{v}(s)\cdot(\mathbf{v}(g_{i}))^{T}}{\parallel\bm {v}(s)\parallel\cdot\parallel\mathbf{v}(g_{i})\parallel} \tag{17}\]  
where \(g_{i}\) denotes the \(i\)th triplet in \(G\). Table 1 shows an example of semantic importance. From Table 1, \"< [PERSON] - founder-Apple>\" is more important than \"< [PERSON] - graduate-Reed College >\", which is also in line with human perception. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sentence** & **Triplets of Knowledge Graph** & **Semantic Similarity** \\ \hline [PERSON], a graduate of Reed & [PERSON] – graduate-Reed College & 0.56 \\ College, is the founder of Apple & [PERSON] – founder-Apple & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An example of semantic importance. Figure 5: The overall process of the proposed semantic communication system based on the knowledge graph, combining the proposed semantic extraction method, the proposed semantic restoration method, and the traditional communication architecture. Based on the sorted triplets, we can adaptively adjust the number of transmitted triplets according to the channel quality. When the channel quality is extremely poor, we only transmit the most significant triplet and use the communication resources of triplets not transmitted to protect it. As the channel quality improves, we increase the number of transmitted triplets. After the transmitted knowledge graph \(G\) is obtained, the transmitter first maps it into a binary bit stream \(B=T(G)\), and then feeds the binary bit stream into the channel encoder to cope with the effects of channel noise and distortion. Therefore, the whole process of the transmitter can be represented as  
\[X=C(T(G)) \tag{18}\]  
where \(T(\bullet)\) and \(C(\bullet)\) denote the source encoder and the channel encoder, respectively.