Measuring and avoiding information loss during concept import from a source to a target ontology

James Geller, Shmuel T. Klein, Vipina Kuttichi Keloth

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

Abstract

Comparing pairs of ontologies in the same biomedical content domain often uncovers surprising differences. In many cases these differences can be characterized as “density differences,” where one ontology describes the content domain with more concepts in a more detailed manner. Using the Unified Medical Language System across pairs of ontologies contained in it, these differences can be precisely observed and used as the basis for importing concepts from the ontology of higher density into the ontology of lower density. However, such an import can lead to an intuitive loss of information that is hard to formalize. This paper proposes an approach based on information theory that mathematically distinguishes between different methods of concept import and measures the associated avoidance of information loss.

Original languageEnglish (US)
Title of host publicationIC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
EditorsJan Dietz, David Aveiro, Joaquim Filipe
PublisherSciTePress
Pages442-449
Number of pages8
ISBN (Electronic)9789897583827
StatePublished - Jan 1 2019
Event11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019 - Vienna, Austria
Duration: Sep 17 2019Sep 19 2019

Publication series

NameIC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Volume2

Conference

Conference11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019
CountryAustria
CityVienna
Period9/17/199/19/19

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Biomedical Ontologies
  • Concept Import
  • Information Content
  • Information Loss

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