A Textbook of Belief Dynamics: Solutions to Exercises
A Textbook of Belief Dynamics: Solutions to Exercises
A consistency-based approach for belief change
Artificial Intelligence
Semantic integration: a survey of ontology-based approaches
ACM SIGMOD Record
Prime Implicate-based Belief Revision Operators
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Reasoning Support for Mapping Revision
Journal of Logic and Computation
Web Semantics: Science, Services and Agents on the World Wide Web
Implementing iterated belief change via prime implicates
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Reasoning under inconsistency: A forgetting-based approach
Artificial Intelligence
Knowledge compilation for belief change
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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In semantic integration scenarios, the integration of an assertion from some sender into the knowledge base (KB) of a receiver may be hindered by inconsistencies due to ambiguous use of symbols; hence a revision of the KB is needed to preserve its consistency. This paper analyses the new family of implication based revision operators, which exploit the idea of revising hypotheses on the semantic relatedness of the receiver's and sender's symbols. In order to capture the specific inconsistency resolution strategy of these operators, the novel concept of uniform sets, which are based on prime implicates, is elaborated. According to two main results of this paper these operators lend themselves to practical use in systems for semantic integration: First, the operators are finitely representable. Second, the non-sceptical versions of these operators can be axiomatically characterised by postulates, which provide a full specification of the operators' effects.