EXK-SC: A Semantic Communication Model Based on Information Framework Expansion and Knowledge Collision
Gangtao Xin, Pingyi Fan

TL;DR
This paper introduces a novel semantic communication model based on an information framework that incorporates semantic expansion and knowledge collision, aiming to improve the expression and measurement of meaning in transmitted messages.
Contribution
It is the first to explore semantic expansion and knowledge collision within a semantic information framework, establishing theoretical relationships with transmission information rate.
Findings
Relationship between semantic expansion and transmission rate established
First theoretical discussion of semantic expansion and knowledge collision
Proposes a new paradigm for semantic communication based on semantic information theory
Abstract
Semantic communication is not focused on improving the accuracy of transmitted symbols, but is concerned with expressing the expected meaning that the symbol sequence exactly carries. However, the measurement of semantic messages and their corresponding codebook generation are still open issues. Expansion, which integrates simple things into a complex system and even generates intelligence, is truly consistent with the evolution of the human language system. We apply this idea to the semantic communication system, quantifying semantic transmission by symbol sequences and investigating the semantic information system in a similar way as Shannon's method for digital communication systems. This work is the first to discuss semantic expansion and knowledge collision in the semantic information framework. Some important theoretical results are presented, including the relationship between…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCognitive Computing and Networks · Cognitive Science and Education Research · Neural Networks and Applications
