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001436192 001__ 1436192
001436192 003__ OCoLC
001436192 005__ 20230309004014.0
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001436192 019__ $$a1249474057
001436192 020__ $$a9783030676810$$q(electronic bk.)
001436192 020__ $$a3030676811$$q(electronic bk.)
001436192 020__ $$z3030676803
001436192 020__ $$z9783030676803
001436192 0247_ $$a10.1007/978-3-030-67681-0$$2doi
001436192 035__ $$aSP(OCoLC)1249076000
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001436192 049__ $$aISEA
001436192 050_4 $$aQ387
001436192 08204 $$a006.3/3$$223
001436192 24500 $$aProvenance in data science :$$bfrom data models to context-aware knowledge graphs /$$cLeslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness, editors.
001436192 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001436192 300__ $$a1 online resource (xi, 110 pages) :$$billustrations
001436192 336__ $$atext$$btxt$$2rdacontent
001436192 337__ $$acomputer$$bc$$2rdamedia
001436192 338__ $$aonline resource$$bcr$$2rdacarrier
001436192 4901_ $$aAdvanced information and knowledge processing,$$x1610-3947
001436192 504__ $$aIncludes bibliographical references.
001436192 5050_ $$aThe Evolution of Context-Aware RDF Knowledge Graphs -- Data Provenance and Accountability on the Web -- The Right (Provenance) Hammer for the Job: a Comparison of Data Provenance Instrumentation -- Contextualized Knowledge Graphs in Communication Network and Cyber-Physical System Modeling -- ProvCaRe: A Large-Scale Semantic Provenance Resource for Scientific Reproducibility -- Graph-Based Natural Language Processing for the Pharmaceutical Industry.
001436192 506__ $$aAccess limited to authorized users.
001436192 520__ $$aRDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack maps that aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues. This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
001436192 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 7, 2021).
001436192 650_0 $$aKnowledge representation (Information theory)
001436192 650_0 $$aData mining.
001436192 650_0 $$aData structures (Computer science)
001436192 650_0 $$aMachine learning.
001436192 650_6 $$aReprésentation des connaissances.
001436192 650_6 $$aExploration de données (Informatique)
001436192 650_6 $$aStructures de données (Informatique)
001436192 650_6 $$aApprentissage automatique.
001436192 655_0 $$aElectronic books.
001436192 7001_ $$aSikos, Leslie F.,$$eeditor$$1https://orcid.org/0000-0003-3368-2215
001436192 7001_ $$aSeneviratne, Oshani W.,$$eeditor$$1https://orcid.org/0000-0001-8518-917X
001436192 7001_ $$aMcGuinness, Deborah L.,$$eeditor$$1https://orcid.org/0000-0001-7037-4567
001436192 77608 $$iPrint version:$$z9783030676803$$w(OCoLC)1227273302
001436192 830_0 $$aAdvanced information and knowledge processing,$$x1610-3947
001436192 852__ $$bebk
001436192 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-67681-0$$zOnline Access$$91397441.1
001436192 909CO $$ooai:library.usi.edu:1436192$$pGLOBAL_SET
001436192 980__ $$aBIB
001436192 980__ $$aEBOOK
001436192 982__ $$aEbook
001436192 983__ $$aOnline
001436192 994__ $$a92$$bISE