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

James Geller; Shmuel T. Klein; Vipina Kuttichi Keloth

doi:10.5220/0008354904420449

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

James Geller, Shmuel T. Klein, Vipina Kuttichi Keloth

Department of Computer Science

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Editors	Jan Dietz, David Aveiro, Joaquim Filipe
Publisher	SciTePress
Pages	442-449
Number of pages	8
ISBN (Electronic)	9789897583827
DOIs	https://doi.org/10.5220/0008354904420449
State	Published - 2019
Event	11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019 - Vienna, Austria Duration: 17 Sep 2019 → 19 Sep 2019

Publication series

Name	IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Volume	2

Conference

Conference	11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019
Country/Territory	Austria
City	Vienna
Period	17/09/19 → 19/09/19

Bibliographical note

Publisher Copyright:
Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Keywords

Biomedical Ontologies
Concept Import
Information Content
Information Loss

Access to Document

10.5220/0008354904420449

Cite this

Geller, J., Klein, S. T., & Keloth, V. K. (2019). Measuring and avoiding information loss during concept import from a source to a target ontology. In J. Dietz, D. Aveiro, & J. Filipe (Eds.), IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 442-449). (IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management; Vol. 2). SciTePress. https://doi.org/10.5220/0008354904420449

Geller, James ; Klein, Shmuel T. ; Keloth, Vipina Kuttichi. / Measuring and avoiding information loss during concept import from a source to a target ontology. IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. editor / Jan Dietz ; David Aveiro ; Joaquim Filipe. SciTePress, 2019. pp. 442-449 (IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management).

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Geller, J, Klein, ST & Keloth, VK 2019, Measuring and avoiding information loss during concept import from a source to a target ontology. in J Dietz, D Aveiro & J Filipe (eds), IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, vol. 2, SciTePress, pp. 442-449, 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019, Vienna, Austria, 17/09/19. https://doi.org/10.5220/0008354904420449

Measuring and avoiding information loss during concept import from a source to a target ontology. / Geller, James; Klein, Shmuel T.; Keloth, Vipina Kuttichi.
IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. ed. / Jan Dietz; David Aveiro; Joaquim Filipe. SciTePress, 2019. p. 442-449 (IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Geller J, Klein ST, Keloth VK. Measuring and avoiding information loss during concept import from a source to a target ontology. In Dietz J, Aveiro D, Filipe J, editors, IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. SciTePress. 2019. p. 442-449. (IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management). doi: 10.5220/0008354904420449

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

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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