There are different approaches to modeling a computational system, each providing a different semantics. We present a comparison between different approaches to semantics and aim at identifying which peculiarities are needed to provide a system with a uniquely interpretable semantics. We discuss different approaches, namely, Description Logics, Artificial Neural Networks, and relational database management systems. We identify classification (the process of building a taxonomy) as common trait. However, in this paper we also argue that classification is not enough to provide a system with a Semantics, which emerges only when relations between classes are established and used among instances. Our contribution also analyses additional features of the formalisms that distinguish the approaches: closed vs. open world assumption, dynamic vs. static nature of knowledge, the management of knowledge, and the learning process.
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