Terminological reasoning is inherently intractable (research note)
Artificial Intelligence
Improvements to propositional satisfiability search algorithms
Improvements to propositional satisfiability search algorithms
TABLEAUX '98 Proceedings of the International Conference on Automated Reasoning with Analytic Tableaux and Related Methods
The description logic handbook
Decomposition-based Reasoning for Large Knowledge Bases in Description Logics
Proceedings of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering
On the semantics of linking and importing in modular ontologies
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
A qualitative agent-based approach to power quality monitoring and diagnosis
Integrated Computer-Aided Engineering - Multi-Agent Systems for Energy Management
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Integrated Computer-Aided Engineering
Construction of ontologies from object-oriented database models
Integrated Computer-Aided Engineering
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Reasoning in a Knowledge Base (KB) is one of the most important applications of Description Logic (DL) reasoners. The execution time and storage space requirements are both significant factors that directly influence the performance of a reasoning algorithm. In this paper, we investigate a new technique for optimizing DL reasoning in order to minimize the above two factors as much as possible. This technique is applied to speed up TBox and ABox reasoning, especially for large TBoxes. The incorporation of this technique with previous optimization techniques in current DL systems can effectively solve intractable inferences. Our technique is called "overlap ontology decomposition", in which the decomposition of a given ontology into many sub-ontologies is implemented such that the semantics and inference services of the original ontology are preserved. We are concerned about how to reason effectively with multiple KBs and how to improve the efficiency of reasoning over component ontologies.