@inproceedings{511f7e58a1c04212827479159fa53d13,
title = "Measuring and avoiding information loss during concept import from a source to a target ontology",
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.",
keywords = "Biomedical Ontologies, Concept Import, Information Content, Information Loss",
author = "James Geller and Klein, {Shmuel T.} and Keloth, {Vipina Kuttichi}",
year = "2019",
month = jan,
day = "1",
language = "English (US)",
series = "IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
publisher = "SciTePress",
pages = "442--449",
editor = "Jan Dietz and David Aveiro and Joaquim Filipe",
booktitle = "IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
note = "11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019 ; Conference date: 17-09-2019 Through 19-09-2019",
}