Learning from Ontology Streams with Semantic Concept Drift

Abstract : Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic em-beddings. The experiments show accurate prediction with data from Dublin and Beijing.
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Jiaoyan Chen, Freddy Lecue, Jeff Pan, Huajun Chen. Learning from Ontology Streams with Semantic Concept Drift. Twenty-Sixth International Joint Conference on Artificial Intelligence, Aug 2017, Melbourne, France. ⟨10.24963/ijcai.2017/133⟩. ⟨hal-01934901⟩

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