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    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs

Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs

Sikos, Leslie F.
Seneviratne, Oshani W.
McGuinness, Deborah L.

Hardcover

Series: Advanced Information and Knowledge Processing

DatabasesGeneral Computers

ISBN10: 3030676803
ISBN13: 9783030676803
Publisher: Springer Nature
Published: Apr 27 2021
Pages: 110
Weight: 0.78
Height: 0.31 Width: 6.14 Depth: 9.21
Language: English
RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack mapsthat aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues.

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