Local models semantics, or contextual reasoning = locality + compatibility
Artificial Intelligence
KEx: A Peer-to-Peer Solution for Distributed Knowledge Management
PAKM '02 Proceedings of the 4th International Conference on Practical Aspects of Knowledge Management
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Ontologies for Enterprise Knowledge Management
IEEE Intelligent Systems
The role of ontologies in autonomic computing systems
IBM Systems Journal
A framework for handling inconsistency in changing ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
DRAGO: distributed reasoning architecture for the semantic web
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Bounded Ontological Consistency for Scalable Dynamic Knowledge Infrastructures
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
Domain ontology learning and consistency checking based on TSC approach and racer
RR'07 Proceedings of the 1st international conference on Web reasoning and rule systems
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In today's world there is a need for knowledge infrastructures that can support several autonomous knowledge bases all using different ontologies and constantly adapting these to their changing local needs. Moreover, these different knowledge bases are expressing their unique points of view and constitute different local contexts. At the same time interoperability is needed in order to connect these semantically dispersed knowledge bases, and we formalized this as a type of consistency. Both these aspects are included in our definition of semantic autonomy. We present a layered framework that shows how to design a scalable system having this property. In our approach both ontology and mapping evolution take place, at the same time as the whole system is kept coherent using lightweight methods for maintaining global consistency. However, in order to achieve this several restrictions are necessary and the logical language used by the individual ontologies is kept simple. Finally, we present some experimental results that demonstrate the scalability of our approach.