A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
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Fundamenta Informaticae - Special issue: rough sets
Uncertainly measures of rough set prediction
Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
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Machine Learning
Rough-Neuro-Computing: Techniques for Computing with Words
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Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
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Reasoning with rough description logics: An approximate concepts approach
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Reasoning within intuitionistic fuzzy rough description logics
Information Sciences: an International Journal
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RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
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AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Reasoning with the finitely many-valued Łukasiewicz fuzzy Description Logic SROIQ
Information Sciences: an International Journal
Generalized fuzzy rough description logics
Information Sciences: an International Journal
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
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Currently, there is a great deal of interest in developing tools for the generation and use of ontologies on the WWW. These knowledge structures are considered essential to the success of the semantic web, the next phase in the evolution of the WWW. Much recent work with ontologies assumes that the concepts used as building blocks are crisp as opposed to approximate. It is a premise of this paper that approximate concepts and ontologies will become increasingly more important as the semantic web becomes a reality. We propose a framework for specifying, generating and using approximate ontologies. More specifically, (1) a formal framework for defining approximate concepts, ontologies and operations on approximate concepts and ontologies is presented. The framework is based on intuitions from rough set theory; (2) algorithms for automatically generating approximate ontologies from traditional crisp ontologies or from large data sets together with additional knowledge are presented. The knowledge will generally be related to similarity measurements between individual objects in the data sets, or constraints of a logical nature which rule out particular constellations of concepts and dependencies in generated ontologies.The techniques for generating approximate ontologies are parameterizable. The paper provides specific instantiations and examples.