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Provenance in data sciencefrom data ...

McGuinness, Deborah L.

Provenance in data sciencefrom data models to context-aware knowledge graphs /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Provenance in data scienceedited by Leslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness.
其他題名:	from data models to context-aware knowledge graphs /
其他作者:	Sikos, Leslie F.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xi, 110 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Knowledge representation (Information theory)
電子資源:	https://doi.org/10.1007/978-3-030-67681-0
ISBN:	9783030676810$q(electronic bk.)

Provenance in data sciencefrom data models to context-aware knowledge graphs /
Provenance in data sciencefrom data models to context-aware knowledge graphs /[electronic resource] :edited by Leslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness. - Cham :Springer International Publishing :2021. - xi, 110 p. :ill., digital ;24 cm. - Advanced information and knowledge processing,1610-3947. - Advanced information and knowledge processing..

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.

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.

ISBN: 9783030676810$q(electronic bk.)

Standard No.: 10.1007/978-3-030-67681-0doiSubjects--Topical Terms:

226484
Knowledge representation (Information theory)

LC Class. No.: Q387 / .P76 2021

Dewey Class. No.: 006.33

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520 $a 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.
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