Knowledge-Driven Semantic Communication Enabled by the Geometry of Meaning

TL;DR

This paper introduces a knowledge-driven semantic communication method based on the geometry of meaning, achieving significant data rate reductions and offering an interpretable, efficient alternative to traditional approaches.

Contribution

It presents a novel semantic communication framework utilizing the conceptual space model, bounding semantic error probability and demonstrating substantial rate efficiency improvements.

Findings

Achieves up to 99.9% reduction in communication rate.

Provides bounds on semantic error probability.

Demonstrates effectiveness through simulations inspired by metaverse applications.

Abstract

As our world grows increasingly connected and new technologies arise, global demands for data traffic continue to rise exponentially. Limited by the fundamental results of information theory, to meet these demands we are forced to either increase power or bandwidth usage. But what if there was a way to use these resources more efficiently? This question is the main driver behind the recent surge of interest in semantic communication, which seeks to leverage increased intelligence to move beyond the Shannon limit of technical communication. In this paper we expound a method of achieving semantic communication which utilizes the conceptual space model of knowledge representation. In contrast to other popular methods of semantic communication, our approach is intuitive, interpretable and efficient. We derive some preliminary results bounding the probability of semantic error under our framework, and show how our approach can serve as the underlying knowledge-driven foundation to higher-level intelligent systems. Taking inspiration from a metaverse application, we perform simulations to draw important insights about the proposed method and demonstrate how it can be used to achieve semantic communication with a 99.9% reduction in rate as compared to a more traditional setup.