In this paper we tackle the problem of semantic heterogeneity in multi-agent communication, i.e., when agents in a multi-agent system use different vocabularies for message passing, or might interpret shared vocabulary in varying ways. The problem of achieving meaningful communication in such semantically heterogeneous multi-agent interactions has been mainly tackled either by using ontology alignments to translate vocabularies, or by using methods that learn an alignment by observing how the utterance of particular terms affects the unfolding of an interaction. We propose solutions that combine these approaches and study how agents can use external alignments with possibly incomplete or erroneous mappings when communicating with each other in the context of a multi-agent interaction. We further show experimentally that with the experience gained through repeated interactions and by using simple learning techniques agents can find and repair those mappings of an ontology alignment that lead to unsuccessful interactions, thus improving the success rate of their future interactions.