Biokg: A knowledge graph for relational learning on biological data

WALSH, Brian, Sameh K MOHAMED and Vít NOVÁČEK. Biokg: A knowledge graph for relational learning on biological data. Online. In Mathieu d'Aquin, Stefan Dietze, Claudia Hauff, Edward Curry, Philippe Cudre Mauroux. Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New YorkNYUnited States: Association for Computing Machinery, 2020, p. 3173-3180. ISBN 978-1-4503-6859-9. Available from: https://dx.doi.org/10.1145/3340531.3412776.

Basic information

Original name

Biokg: A knowledge graph for relational learning on biological data

Authors

WALSH, Brian, Sameh K MOHAMED and Vít NOVÁČEK (203 Czech Republic, guarantor, belonging to the institution)

Edition

New YorkNYUnited States, Proceedings of the 29th ACM International Conference on Information & Knowledge Management, p. 3173-3180, 8 pp. 2020

Publisher

Association for Computing Machinery

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Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

Organization unit

Faculty of Informatics

ISBN

978-1-4503-6859-9

DOI

http://dx.doi.org/10.1145/3340531.3412776

UT WoS

000749561303020

Keywords in English

knowledge graphs; biomedical knowledge integration

Tags

International impact, Reviewed

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Abstract

V originále

Knowledge graphs became a popular means for modeling complex biological systems where they model the interactions between biological entities and their effects on the biological system. They also provide support for relational learning models which are known to provide highly scalable and accurate predictions of associations between biological entities. Despite the success of the combination of biological knowledge graph and relation learning models in biological predictive tasks, there is a lack of unified biological knowledge graph resources. This forced all current efforts and studies for applying a relational learning model on biological data to compile and build biological knowledge graphs from open biological databases. This process is often performed inconsistently across such efforts, especially in terms of choosing the original resources, aligning identifiers of the different databases, and assessing the quality of included data. To make relational learning on biomedical data more standardised and reproducible, we propose a new biological knowledge graph which provides a compilation of curated relational data from open biological databases in a unified format with common, interlinked identifiers. We also provide a new module for mapping identifiers and labels from different databases which can be used to align our knowledge graph with biological data from other heterogeneous sources. Finally, to illustrate the practical relevance of our work, we provide a set of benchmarks based on the presented data that can be used to train and assess the relational learning models in various tasks related to pathway and drug discovery.