For meaning representations in NLP, we focus our attention on thematic aspects and conceptual vectors. The learning strategy of conceptual vectors relies on a morphosyntaxic analysis of human usage dictionary definitions linked to vector propagation. This analysis currently doesn't take into account negation phenomena. This work aims at studying the antonymy aspects of negation, in the larger goal of its integration into the thematic analysis. We present a model based on the idea of symmetry compatible with conceptual vectors. Then, we define antonymy functions which allows the construction of an antonymous vector and the enumeration of its potentially antinomic lexical items. Finally, we introduce a measure which evaluates how a given word is an acceptable antonym for a term.