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

dc.contributor.author	McGuinness, Deborah L.
dc.contributor.author	Seneviratne, Oshani
dc.contributor.author	Sikos, Leslie
dc.date.accessioned	2022-04-18T13:33:52Z
dc.date.available	2022-04-18T13:33:52Z
dc.date.issued	2021
dc.identifier.isbn	978-3-030-67680-3
dc.identifier.issn	1610-3947
dc.identifier.other	https://doi.org/10.1007/978-3-030-67681-0
dc.identifier.uri	https://doi.org/10.1007/978-3-030-67681-0
dc.description.abstract	Presents a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations to be used for information processing, management, aggregation, fusion, and visualization Illustrates how to use context-aware knowledge graphs in a variety of domains, from cybersecurity to biomedicine With the emergence of data science, several semantic web standards are discussed in this book.	en_US
dc.language.iso	en_US	en_US
dc.publisher	Springer, Cham	en_US
dc.subject.other	knowledge graph
dc.subject.other	semantic web
dc.subject.other	provenance ontology
dc.subject.other	data provenance
dc.title	Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs	en_US
dc.type	Book	en_US

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