Autoencoder-Based Domain Learning for Semantic Communication with
  Conceptual Spaces
Dylan Wheeler Balasubramaniam Natarajan
Communication with the goal of accurately conveying meaning, rather than
accurately transmitting symbols, has become an area of growing interest. This
paradigm, termed semantic communication, typically leverages modern
developments in artificial intelligence and machine learning to improve the
efficiency and robustness of communication systems. However, a standard model
for capturing and quantifying the details of "meaning" is lacking, with many
leading approaches to semantic communication adopting a black-box framework
with little understanding of what exactly the model is learning. One solution
is to utilize the conceptual spaces framework, which models meaning explicitly
in a geometric manner. Though prior work studying semantic communication with
conceptual spaces has shown promising results, these previous attempts involve
hand-crafting a conceptual space model, severely limiting the scalability and
practicality of the approach. In this work, we develop a framework for learning
a domain of a conceptual space model using only the raw data with high-level
property labels. In experiments using the MNIST and CelebA datasets, we show
that the domains learned using the framework maintain semantic similarity
relations and possess interpretable dimensions.