The work presented in this paper aims to combine Latent Semantic Analysis methodology, common sense and traditional knowledge representation in order to improve the dialogue capabilities of a conversational agent. In our approach the agent brain is characterized by two areas: a "rational area", composed by a structured, rule-based knowledge base, and an "associative area", obtained through a data-driven semantic space. Concepts are mapped in this space and their mutual geometric distance is related to their conceptual similarity. The geometric distance between concepts implicitly defines a sub-symbolic relationship net, which can be seen as a new "subsymbolic semantic layer" automatically added to the Cyc ontology. Users queries can also be mapped in the same conceptual space, and evoke similar ontology concepts. As a result the agent can exploit this feature, attempting to retrieve ontological concepts that are not easily reachable by means of the traditional ontology reasoning engine.