Social Networks are nowadays the most relevant source of information in terms of scientific challenges and proposed computational models. This is due to the huge availability of user data, ranging from interactions, activities, and multimedia messages. The Big Data era is relatively new, and the emergence of user- and scalibility-centered solutions is particularly influenced by these novel and ever-growing data, that need to be carefully organized to remain manageable. In this contribution, we propose a novel approach to deal with social networks data representation that is able to model such complexity without affecting the flexibility of who can interact within the environment, and how. In particular, we revisit the standard methodology of computational ontologies proposing a framework where objects and agents are defined as compositions of atomic semantic information, avoiding preventive and static identification of the system's players. Our method is inspired by the work of James Gibson, who defined an ecological view of the human perception based on objects' natural affordances, in which objects spontaneously give cues about how they can be used depending on the agent who is actually interacting. The idea is that while objects and agents can potentially grow without any constraint, the spectrum of all the individual interactions can be the product of limited (and much more simple to represent) links between users and objects' atomic semantic information. In this sense, if an agent 'x' acts on an object 'y', it means that some property of 'x' are activated by the action (i.e., the user embodies a specific role), and some property of 'y' makes the action physically possible (i.e., it allows the action to be performed). In this paper we demonstrate how an interaction-based ontology view with the use of vector spaces can reduce manual efforts while preserving control of dynamic data in social networks.
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