Within a MAS, individual agents interact in order to perform set of
given tasks; for example buying a product, retrive information, and so
on. Open MAS place weaker constraints on the agents that can take part
in the interactions: often it is not possible to ensure that all agents
share the same ontology. Mapping in advance all the possible
combinations of ontologies may not be feasible. My proposal is to model
the dialogues, as they take place: these models are used to
increase
the efficiency of standard ontology mapping methods. Mapping takes
place when a new term appears in a received message, and the
corresponding (or the closest) term in the agent's ontology is found.
The model is used to predict the set of possible corresponding terms,
reducing the number of computationally expensive comparisons between
the received term and the terms in the agent's ontology.