# Knowledge Enhanced Semantic Communication Receiver

arXiv: https://arxiv.org/abs/2302.07727 · HTML: https://mlsanity.com/paper/2302.07727/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Wireless Semantic Communications for Video Conferencing](https://mlsanity.com/paper/2204.07790/) (2022)
- [Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ](https://mlsanity.com/paper/2106.03009/) (2021)
- [Semantic Communication with Adaptive Universal Transformer](https://mlsanity.com/paper/2108.09119/) (2021)
- [Robust Semantic Communications Against Semantic Noise](https://mlsanity.com/paper/2202.03338/) (2022)
- [Cognitive Semantic Communication Systems Driven by Knowledge Graph](https://mlsanity.com/paper/2202.11958/) (2022)
- [Adaptive Bit Rate Control in Semantic Communication with Incremental Knowledge-based HARQ](https://mlsanity.com/paper/2203.06634/) (2022)
- [Life-long Learning for Reasoning-based Semantic Communication](https://mlsanity.com/paper/2202.01952/) (2022)
- [A Unified Approach to Semantic Information and Communication based on Probabilistic Logic](https://mlsanity.com/paper/2205.00621/) (2022)

## Built on by (most cited first, 3 of 3)

- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [Interplay of Semantic Communication and Knowledge Learning](https://mlsanity.com/paper/2402.03339/) (2024)
- [Evolving Token Communication with Parametric Memory Network](https://mlsanity.com/paper/2605.01869/) (2026)

## Abstract (arXiv)

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.