2020
DOI: 10.1016/j.csbj.2020.05.017
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Constructing knowledge graphs and their biomedical applications

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Cited by 160 publications
(110 citation statements)
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“…Hogan 2020;Hitzler 2021). Comparing with the KG construction and application in biology and biomedical studies (e.g., Ashburner et al, 2000;Gene Ontology Consortium, 2019;Nicholson and Greene, 2020), most existing geoscience KGs focus on lightweight semantics, and their applications are limited to data harmonization and integration. Computer scientists can see the potential of deeper applications of KGs in geosciences, but geoscientists would like to see a list of KG technologies that can guide them from simple to sophisticated applications ( 4D Initiative, 2018;Gil et al, 2019;NASEM, 2020;Wang et al, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…Hogan 2020;Hitzler 2021). Comparing with the KG construction and application in biology and biomedical studies (e.g., Ashburner et al, 2000;Gene Ontology Consortium, 2019;Nicholson and Greene, 2020), most existing geoscience KGs focus on lightweight semantics, and their applications are limited to data harmonization and integration. Computer scientists can see the potential of deeper applications of KGs in geosciences, but geoscientists would like to see a list of KG technologies that can guide them from simple to sophisticated applications ( 4D Initiative, 2018;Gil et al, 2019;NASEM, 2020;Wang et al, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…Knowledge graphs (KGs) would be a suitable approach to facilitate this goal by unifying disparate datasets into a human queryable framework. KGs have already been widely adopted in biomedical research to unravel the complex relationship between biological changes and disease phenotypes [ 6 , 7 , 8 , 9 , 10 ].…”
Section: Introductionmentioning
confidence: 99%
“…A prominent example is the Gene Ontology 3 that organizes gene functions, but hundreds of other ontologies provide infrastructure to coordinate databases and power bioinformatic applications 1 . In parallel to the adoption of ontologies, advances in machine-learning (ML) have demonstrated the possibilities of creating representations of knowledge from labeled datasets or even from raw data 46 . The overlap between curated structures and automated pattern discovery raises the need to clarify the relation between those components.…”
Section: Introductionmentioning
confidence: 99%