Datamining with ontologies

Robert Hoehndorf; Georgios V. Gkoutos; Paul N. Schofield

doi:10.1007/978-1-4939-3572-7_19

Datamining with ontologies

Robert Hoehndorf, Georgios V. Gkoutos, Paul N. Schofield

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

7 Scopus citations

Abstract

The use of ontologies has increased rapidly over the past decade and they now provide a key component of most major databases in biology and biomedicine. Consequently, datamining over these databases benefits from considering the specific structure and content of ontologies, and several methods have been developed to use ontologies in datamining applications. Here, we discuss the principles of ontology structure, and datamining methods that rely on ontologies. The impact of these methods in the biological and biomedical sciences has been profound and is likely to increase as more datasets are becoming available using common, shared ontologies.

Original language	English (US)
Title of host publication	Methods in Molecular Biology
Publisher	Humana Press Inc.
Pages	385-397
Number of pages	13
DOIs	https://doi.org/10.1007/978-1-4939-3572-7_19
State	Published - Aug 1 2016

Publication series

Name	Methods in Molecular Biology
Volume	1415
ISSN (Print)	1064-3745

Bibliographical note

Publisher Copyright:
© Springer Science+Business Media New York 2016.

Keywords

Automated reasoning
Data integration
Enrichment
Graph algorithms
Ontology
Semantic Web
Semantic similarity
Web Ontology Language (OWL)

ASJC Scopus subject areas

Genetics
Molecular Biology

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10.1007/978-1-4939-3572-7_19

Cite this

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AU - Schofield, Paul N.

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AB - The use of ontologies has increased rapidly over the past decade and they now provide a key component of most major databases in biology and biomedicine. Consequently, datamining over these databases benefits from considering the specific structure and content of ontologies, and several methods have been developed to use ontologies in datamining applications. Here, we discuss the principles of ontology structure, and datamining methods that rely on ontologies. The impact of these methods in the biological and biomedical sciences has been profound and is likely to increase as more datasets are becoming available using common, shared ontologies.

KW - Automated reasoning

KW - Data integration

KW - Enrichment

KW - Graph algorithms

KW - Ontology

KW - Semantic Web

KW - Semantic similarity

KW - Web Ontology Language (OWL)

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ER -

Datamining with ontologies

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

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