Most of the proposed approaches in automatic service selection assume the existence of a common ontology among communicating agents. However, this assumption becomes difficult to support in environments, where the agents' ontologies can evolve independently based on their individual experiences. In this paper, we propose an approach through which agents can cooperatively update their ontologies and teach one another concepts from their ontologies. This leads to a society of agents with different but overlapping ontologies. Our simulation results show that mutually accepted concepts emerge based on the interactions of the agents. Further, agents learn and use concepts that are created by other to express their own service needs.