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@inproceedings{2326198,
author = {Walsh, Brian and Mohamed, Sameh K and Nováček, Vít},
address = {New YorkNYUnited States},
booktitle = {Proceedings of the 29th ACM International Conference on Information & Knowledge Management},
doi = {http://dx.doi.org/10.1145/3340531.3412776},
editor = {Mathieu d'Aquin, Stefan Dietze, Claudia Hauff, Edward Curry, Philippe Cudre Mauroux},
keywords = {knowledge graphs; biomedical knowledge integration},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New YorkNYUnited States},
isbn = {978-1-4503-6859-9},
pages = {3173-3180},
publisher = {Association for Computing Machinery},
title = {Biokg: A knowledge graph for relational learning on biological data},
url = {https://dl.acm.org/doi/abs/10.1145/3340531.3412776},
year = {2020}
}
TY - JOUR
ID - 2326198
AU - Walsh, Brian - Mohamed, Sameh K - Nováček, Vít
PY - 2020
TI - Biokg: A knowledge graph for relational learning on biological data
PB - Association for Computing Machinery
CY - New YorkNYUnited States
SN - 9781450368599
KW - knowledge graphs
KW - biomedical knowledge integration
UR - https://dl.acm.org/doi/abs/10.1145/3340531.3412776
N2 - 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.
ER -
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. \textit{Proceedings of the 29th ACM International Conference on Information \&{}amp; 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.
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