SciKG: Tutorial on Building Scientific Knowledge Graphs from Data, Data Dictionaries, and Codebooks
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Authors
Santos, Henrique
Pinheiro, Paulo
McCusker, Jamie
Rashid, Sabbir M.
McGuinness, Deborah L.
Issue Date
2023-05-28
Type
Other
Language
Keywords
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Abstract
Data from scientific studies are published in datasets, typically accompanied by data dictionaries and
codebooks to support data understanding. To conduct rigorous analysis, data users need to leverage this
documentation to correctly interpret the data. While this process can be burdensome for new data users,
it is also prone to errors even for seasoned users. A computational formal model of the knowledge that
was used to create the study can facilitate better understanding and thus improved usage of the study data.
Knowledge graphs can be used effectively to capture this study knowledge. The SciKG tutorial aimed to
introduce participants to the basics of knowledge graph construction using data, data dictionaries, and
codebooks from scientific studies. It used the Center for Disease Control and Prevention’s (CDC) National
Health and Nutrition Examination Surveys (NHANES) data as a testbed and introduce standardized
terminology, novel and established techniques, and resources such as scientific/biomedical ontologies,
semantic data dictionaries, and knowledge graph frameworks in both lecture and practical sessions.
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Publisher
CUER