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Books > Computing & IT > Applications of computing > Artificial intelligence > Knowledge-based systems / expert systems
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Provenance in Data Science - From Data Models to Context-Aware Knowledge Graphs (Paperback, 1st ed. 2021)
Loot Price: R4,177
Discovery Miles 41 770
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Provenance in Data Science - From Data Models to Context-Aware Knowledge Graphs (Paperback, 1st ed. 2021)
Series: Advanced Information and Knowledge Processing
Expected to ship within 10 - 15 working days
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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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