Content-based implicit user modeling techniques usually employ a traditional term vector as a representation of the user’s interest. However, due to the problem of dimensionality in the vector space model, a simple term vector is not a sufficient representation of the user model as it ignores the semantic relations between terms. In this paper, we present a novel method to enhance a traditional term-based user model with WordNet-based semantic similarity techniques. To achieve this, we use word definitions and relationship hierarchies in WordNet to perform word sense disambiguation and employ domain-specific concepts as category labels for the derived user models. We tested our method on Windows to the Universe, a public educational website covering subjects in the Earth and Space Sciences, and performed an evaluation of our semantically enhanced user models against human judgment. Our approach is distinguishable from existing work because we automatically narrow down the set of domain specific concepts from initial domain concepts obtained from Wikipedia and because we automatically create semantically enhanced user models.