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.

翻译：以准确传达意义而非准确传输符号为目标的通信已成为一个日益受到关注的研究领域。这种被称为语义通信的范式，通常利用人工智能和机器学习的最新发展来提高通信系统的效率和鲁棒性。然而，目前缺乏一个用于捕捉和量化"意义"细节的标准模型，许多主流的语义通信方法采用黑箱框架，对模型具体学习的内容知之甚少。一个解决方案是利用概念空间框架，该框架以几何方式显式建模意义。尽管先前关于概念空间语义通信的研究已展现出令人鼓舞的结果，但这些尝试涉及手工设计概念空间模型，严重限制了该方法的可扩展性和实用性。在本工作中，我们开发了一个框架，仅使用原始数据和高层属性标签来学习概念空间模型的领域。在采用MNIST和CelebA数据集的实验中，我们表明，使用该框架学习的领域能够保持语义相似性关系，并具有可解释的维度。