ACM Transactions on Database Systems (TODS)
Integration of weighted knowledge bases
Artificial Intelligence
Reasoning About Data Provided By Federated Deductive Databases
Journal of Intelligent Information Systems
Combining Multiple Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Logic-based approaches to information fusion
Information Fusion
Proof Theory for Distributed Knowledge
Computational Logic in Multi-Agent Systems
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Strategies for distributing goals in a team of cooperative agents
ESAW'04 Proceedings of the 5th international conference on Engineering Societies in the Agents World
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The general context of this work is the problem of merging data provided by several sources which can be contradictory. Focusing on the case when the information sources do not contain any disjunction, this paper first defines a propositional modal logic for reasoning with data obtained by merging several information sources according to a majority approach. Then it defines a theorem prover to automatically deduce these merged data. Finally, it shows how to use this prover to implement a query evaluator which answers queries addressed to several databases. This evaluator is such that the answer to a query is the one that could be computed by a classical evaluator if the query was addressed to the merged databases. The databases we consider are made of an extensional part, i.e. a set of positive or negative ground literals, and an intensional part i.e. a set of first order function-free clauses. A restriction is imposed to these databases in order to avoid disjunctive data.