Provenance in data science : from data models to context-aware knowledge graphs / Leslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness, editors.
Sikos, Leslie F., editor; Seneviratne, Oshani W., editor; McGuinness, Deborah L., editor
2021
Q387
Available Online

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Title Provenance in data science : from data models to context-aware knowledge graphs / Leslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness, editors.
ISBN 9783030676810 (electronic bk.)
3030676811 (electronic bk.)
3030676803
9783030676803
DOI https://doi.org/10.1007/978-3-030-67681-0
Published Cham, Switzerland : Springer, [2021]
Language English
Description 1 online resource (xi, 110 pages) : illustrations
Other Standard Identifiers 10.1007/978-3-030-67681-0 doi
Call Number Q387
Dewey Decimal Classification 006.3/3
Summary 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 maps that 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. This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed May 7, 2021).
Added Author Sikos, Leslie F., editor
Seneviratne, Oshani W., editor
McGuinness, Deborah L., editor
Series Advanced information and knowledge processing, 1610-3947
Available in Other Form Print version: 9783030676803
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
The Evolution of Context-Aware RDF Knowledge Graphs
Data Provenance and Accountability on the Web
The Right (Provenance) Hammer for the Job: a Comparison of Data Provenance Instrumentation
Contextualized Knowledge Graphs in Communication Network and Cyber-Physical System Modeling
ProvCaRe: A Large-Scale Semantic Provenance Resource for Scientific Reproducibility
Graph-Based Natural Language Processing for the Pharmaceutical Industry.

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