Learning in Presence of Ontology Mapping Errors

8 years 3 months ago
Learning in Presence of Ontology Mapping Errors
The widespread use of ontologies to associate semantics with data has resulted in a growing interest in the problem of learning predictive models from data sources that use different ontologies to model the same underlying domain (world of interest). Learning from such semantically disparate data sources involves the use of a mapping to resolve semantic disparity among the ontologies used. Often, in practice, the mapping used to resolve the disparity may contain errors and as such the learning algorithms used in such a setting must be robust in presence of mapping errors. We reduce the problem of learning from semantically disparate data sources in the presence of mapping errors to a variant of the problem of learning in the presence of nasty classification noise. This reduction allows us to transfer theoretical results and algorithms from the latter to the former. Keywords-semantically disparate data sources ; ontology mapping errors; nasty classification noise; PAC;
Neeraj Koul, Vasant Honavar
Added 15 Feb 2011
Updated 15 Feb 2011
Type Journal
Year 2010
Where WEBI
Authors Neeraj Koul, Vasant Honavar
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