# Reliable Semantic Communication System Enabled by Knowledge Graph
## 2 Related Work  
### Semantic Communication Development  
Due to technical limitations in the early stage of communication development, researchers have focused on solving engineering problems at the technical layer and postponed the study at the semantic layer. However, this does not mean that the research on semantic communication will be shelved. With the advancements in technology, the semantic problem has become an urgent problem that needs to be solved in the communication field [17].  
In terms of theoretical research, [PERSON] et al. (1954) first proposed the concept of the semantic information theory to supplement the classical information theory [18]. They thought that the semantic information contained in the sentence should be defined based on the logical probability of the content of the sentence. [PERSON] (2004) proposed a theory of strongly semantic information [19] and pointed out the problem that sentence contradictions will have infinite information. [PERSON] et al. (2011) put forward a general model of semantic communication, using a factual statement in the propositional logic form to represent semantics [20]. Moreover, the semantic entropy, semantic noise, and semantic channel capacity were defined in [20]. Based on the literature [20], [PERSON] et al. (2012) provided a detailed explanation of the relationship between semantic entropy and information entropy, and they defined the concepts of semantic ambiguity and semantic redundancy [21]. In [22], [PERSON] et al. (2021) proposed that semantic communication can be divided into human-to-human, human-to-machine, and machine-to-machine sub-areas, which broadened the scope of semantic communication.  
On the other hand, the rapid development of neural networks and artificial intelligence technology promotes the progress of technical research in semantic communication. In terms of semantic coding, the authors of [23] proposed a joint source-channel coding for semantic information with a bidirectional long short-term memory model (BILSTM). As an extension of the literature [23], [PERSON] et al. (2018) presented a variable-length joint source-channel coding of semantic information [24]. In [25], [PERSON] et al. (2022) proposed a semantic encoding strategy based on parts-of-speech and context-based decoding strategies, which enhanced communication reliability from the semantic level. Based on the semantic communication framework, [PERSON] et al. (2021) proposed a deep learning-based semantic communication model [26], which used word embedding technology to map text to semantic space and then performed source-channel joint encoding for semantic information by using the transformer framework [27]. Furthermore, the authors of [28] proposed a lightweight distributed semantic communication system for the application scenario of the internet of things (IoT), which reduced the cost of IoT devices. The authors of [29] proposed a semantic communication model based on reinforcement learning to investigate the impact of noisy environments on semantic information. In different information forms, [PERSON] et al. (2021) proposed a semantic communication model for speech transmission [30]. In [31], [PERSON] et al. (2022) proposed a robust end-to-end semantic communication system to combat the semantic noise for image transmission. Moreover, a semantic communication model based on multi-information modalities was developed in [32]. Regarding semantic representation, [PERSON] et al. (2022) used the transformer for semantic extraction and semantic restoration [33].