| Description: |
In a network of information agents, the problem of how these agents keep accurate models of each other becomes critical. Due to the dynamic nature of information and the autonomy of the agents, the models that an agent has of its information sources may not reflect their actual contents. In this paper, we propose an approach to automatically reconcile agent models. First, we showhow an agent can revise its models to account for the disparities with its information sources. Second, we showhow to learn concise descriptions of the new classes of information that arise. Third, we showhow the refined models improve both the accuracy of the knowledge of an agentandtheefficiency of its query processing. A prototype for model reconciliation has been implemented using a SIMS mediator that accesses relational databases. Introduction With the current explosion of data, the problem of how to combine distributed, heterogeneous information sources becomes more and more critical. Age. |