Knowledge Enhanced Semantic Communication Receiver
This work addresses a specific bottleneck in semantic communication for researchers, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of insufficient prior knowledge use and receiver-side decoding in semantic communication by proposing a knowledge-enhanced framework where the receiver actively utilizes a knowledge base for semantic reasoning, achieving superior performance on the WebNLG dataset.
In recent years, with the rapid development of deep learning and natural language processing technologies, semantic communication has become a topic of great interest in the field of communication. Although existing deep learning-based semantic communication approaches have shown many advantages, they still do not make sufficient use of prior knowledge. Moreover, most existing semantic communication methods focus on the semantic encoding at the transmitter side, while we believe that the semantic decoding capability of the receiver should also be concerned. In this paper, we propose a knowledge enhanced semantic communication framework in which the receiver can more actively utilize the facts in the knowledge base for semantic reasoning and decoding, on the basis of only affecting the parameters rather than the structure of the neural networks at the transmitter side. Specifically, we design a transformer-based knowledge extractor to find relevant factual triples for the received noisy signal. Extensive simulation results on the WebNLG dataset demonstrate that the proposed receiver yields superior performance on top of the knowledge graph enhanced decoding.