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The theoretical benefits of semantics as well as their potential impact on IT are well known concepts, extensively discussed in literature. As more and more systems are currently using or referring semantic technologies, the challenging third version of the web (Semantic Web or Web 3.0) is progressively taking shape. On the other hand, apart from the relatively limited capabilities in terms of expressiveness characterizing current concrete semantic technologies, theoretical models and research prototypes are actually overlooking a significant number of practical issues including, among others, consolidated mechanisms to manage and maintain vocabularies, shared notations systems and support to high scale systems (Big Data). Focusing on the OWL model as the current reference technology to specify web semantics, in this paper we will discuss the problem of approaching the knowledge engineering exclusively according to a deterministic model and excluding a priori any kind of probabilistic semantic. Those limitations determine that most knowledge ecosystems including, at some level, probabilistic information are not well suited inside OWL environments. Therefore, despite the big potential of OWL, a consistent number of applications are still using more classic data models or unnatural hybrid environments. But OWL, even with its intrinsic limitations, reflects a model flexible enough to support extensions and integrations. In this work we propose a simple statistical extension for the model that can significantly spread the expressiveness and the purpose of OWL

An Individual-centric Probabilistic Extension for OWL: Modelling the Uncertainness