  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.
