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dc.contributor.authorMcGuinness, Deborah
dc.contributor.authorSeneviratne, Oshani
dc.contributor.authorSikos, Leslie
dc.date.accessioned2022-04-18T13:33:52Z
dc.date.available2022-04-18T13:33:52Z
dc.date.issued2021
dc.identifier.isbn978-3-030-67680-3
dc.identifier.issn1610-3947
dc.identifier.otherhttps://doi.org/10.1007/978-3-030-67681-0
dc.identifier.urihttps://doi.org/10.1007/978-3-030-67681-0
dc.description.abstractPresents 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.isoen_USen_US
dc.publisherSpringer, Chamen_US
dc.subject.otherknowledge graph
dc.subject.othersemantic web
dc.subject.otherprovenance ontology
dc.subject.otherdata provenance
dc.titleProvenance in Data Science: From Data Models to Context-Aware Knowledge Graphsen_US
dc.typeBooken_US


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