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AAAI
2015

Consistent Knowledge Discovery from Evolving Ontologies

8 years 8 months ago
Consistent Knowledge Discovery from Evolving Ontologies
Deductive reasoning and inductive learning are the most common approaches for deriving knowledge. In real world applications when data is dynamic and incomplete, especially those exposed by sensors, reasoning is limited by dynamics of data while learning is biased by data incompleteness. Therefore discovering consistent knowledge from incomplete and dynamic data is a challenging open problem. In our approach the semantics of data is captured through ontologies to empower learning (mining) with (Description Logics) reasoning. Consistent knowledge discovery is achieved by applying generic, significative, representative association semantic rules. The experiments have shown scalable, accurate and consistent knowledge discovery with data from Dublin.
Freddy Lécué, Jeff Z. Pan
Added 27 Mar 2016
Updated 27 Mar 2016
Type Journal
Year 2015
Where AAAI
Authors Freddy Lécué, Jeff Z. Pan
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