Learning in presence of ontology mapping errors

Neeraj Koul, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Pages291-296
Number of pages6
DOIs
StatePublished - Dec 13 2010
Event2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010 - Toronto, ON, Canada
Duration: Aug 31 2010Sep 3 2010

Publication series

NameProceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Volume1

Other

Other2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
CountryCanada
CityToronto, ON
Period8/31/109/3/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

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    Koul, N., & Honavar, V. (2010). Learning in presence of ontology mapping errors. In 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010 (pp. 291-296). [5616569] (Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010; Vol. 1). https://doi.org/10.1109/WI-IAT.2010.138