Semantic Coordination in Systems of Autonomous Agents: the Approach and an Implementation

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous semantic models, is one of the key issues in the development of the open agent-based systems. In this paper, we propose a new algorithm for discovering semantic mappings across autonomous agents’ local data models based on a new approach to semantic coordination. This approach shifts the problem of semantic coordination from the problem of computing structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different models. We show why this is a significant improvement on previous approaches, and how it can be applied to a specific type of models, that is hierarchical classifications.