Ontologies and Machine Learning
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We address the problem of constructing light-weight ontology from social network data. As an example we use social network of a mid size research institution obtained based on e-mail communication. The main contribution is an architecture consisting from five major steps that enable transformation of the data from a given e-mail transactions recordings to an ontology estimating the structure of the organization. Once having a set of sparse vectors, we apply an approach to semi-automated ontology construction as implemented in the OntoGen tool. The experiments and illustrative evaluation show that our approach is useful and applicable in real life situations where the goal is to model social structures based on communication records.
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