Learning in the presence of concept drift and hidden contexts
Machine Learning
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Pattern Recognition Letters
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This paper studies concept drift over time. We first define the meaning of a concept in terms of intension, extension and label. Then we study concept drift over time using two theories: one based on concept identity and one based on concept morphing. A qualitative toolkit for analysing concept drift is proposed to detect concept shift and stability when concept identity is available, and concept split and strength of morphing chain if using the morphing theory. We apply our framework in four case-studies: a political vocabulary in SKOS, the DBpedia ontology in RDFS, the LKIF-Core ontology in OWL and a few biomedical ontologies in OBO. We describe ways of identifying interesting changes in the meaning of concept within given application contexts. These case-studies illustrate the feasibility of our framework in analysing concept drift in knowledge organisation schemas of varying expressiveness.