國立高雄大學圖資館 |

Language: English

Back

Provenance in data sciencefrom data ...

McGuinness, Deborah L.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Provenance in data scienceedited by Leslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness.
Reminder of title:	from data models to context-aware knowledge graphs /
other author:	Sikos, Leslie F.
Published:	Cham :Springer International Publishing :2021.
Description:	xi, 110 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Knowledge representation (Information theory)
Online resource:	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

Provenance in data sciencefrom data models to context-aware knowledge graphs /
LDR:03339nmm a2200337 a 4500 001 597590
003 DE-He213
005 20210426103224.0
006 m d
007 cr nn 008maaau
008 211019s2021 sz s 0 eng d
020 $a 9783030676810$q(electronic bk.)
020 $a 9783030676803$q(paper)
024 7 $a 10.1007/978-3-030-67681-0 $2 doi
035 $a 978-3-030-67681-0
040 $a GP $c GP
041 0 $a eng
050 4 $a Q387 $b .P76 2021
072 7 $a UYQE $2 bicssc
072 7 $a COM025000 $2 bisacsh
072 7 $a UYQE $2 thema
082 0 4 $a 006.33 $2 23
090 $a Q387 $b .P969 2021
245 0 0 $a Provenance in data science $h [electronic resource] : $b from data models to context-aware knowledge graphs / $c edited by Leslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xi, 110 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advanced information and knowledge processing, $x 1610-3947
505 0 $a 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.
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.
650 0 $a Knowledge representation (Information theory) $3 226484
650 0 $a Data mining. $3 184440
650 0 $a Data structures (Computer science) $3 183917
650 0 $a Machine learning. $3 188639
650 1 4 $a Knowledge based Systems. $3 835019
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Data Structures and Information Theory. $3 825714
650 2 4 $a Machine Learning. $3 833608
700 1 $a Sikos, Leslie F. $3 545201
700 1 $a Seneviratne, Oshani W. $3 890950
700 1 $a McGuinness, Deborah L. $3 495504
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Advanced information and knowledge processing. $3 560385
856 4 0 $u https://doi.org/10.1007/978-3-030-67681-0
950 $a Computer Science (SpringerNature-11645)