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Authors: James Geller 1 ; Shmuel T. Klein 2 and Vipina Kuttichi Keloth 1

Affiliations: 1 Dept. of Computer Science, New Jersey Institute of Technology and U.S.A. ; 2 Dept. of Computer Science, Bar Ilan University, Ramat Gan 52900 and Israel

Keyword(s): Biomedical Ontologies, Concept Import, Information Content, Information Loss.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Engineering ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Ontology Engineering ; Ontology Matching and Alignment ; Symbolic Systems

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.

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Paper citation in several formats:
Geller, J.; Klein, S. and Keloth, V. (2019). Measuring and Avoiding Information Loss During Concept Import from a Source to a Target Ontology. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KEOD; ISBN 978-989-758-382-7; ISSN 2184-3228, SciTePress, pages 442-449. DOI: 10.5220/0008354904420449

@conference{keod19,
author={James Geller. and Shmuel T. Klein. and Vipina Kuttichi Keloth.},
title={Measuring and Avoiding Information Loss During Concept Import from a Source to a Target Ontology},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KEOD},
year={2019},
pages={442-449},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008354904420449},
isbn={978-989-758-382-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KEOD
TI - Measuring and Avoiding Information Loss During Concept Import from a Source to a Target Ontology
SN - 978-989-758-382-7
IS - 2184-3228
AU - Geller, J.
AU - Klein, S.
AU - Keloth, V.
PY - 2019
SP - 442
EP - 449
DO - 10.5220/0008354904420449
PB - SciTePress

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