In this paper, we focus on lexical semantics, a key issue in Natural Language Processing (NLP) that tends to con- verge with conceptual Knowledge Representation (KR) and ontologies. When ontological representation is needed, hy- peronymy, the closest approximation to the is-a relation, is at stake. In this paper we describe the principles of our vec- tor model (CVM: Conceptual Vector Model), and show how to account for hyperonymy within the vector-based frame for semantics. We show how hyperonymy diverges from is-a and what measures are more accurate for hyperonymy rep- resentation. Our demonstration results in initiating a ’coop- eration’ process between semantic networks and conceptual vectors. Text automatic rewriting or enhancing, ontology mapping with natural language expressions, are examples of applications that can be derived from the functions we define in this paper.