Overview
- 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
Part of the book series: Advanced Information and Knowledge Processing (AI&KP)
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Table of contents (6 chapters)
Keywords
- Knowledge graph
- Contextualized Knowledge graph
- Data science
- Data provenance
- Provenance ontology
- Scientific reproducibility
- Cyber-knowledge representation
- Cyberthreat intelligence
- Cyber-physical system modeling
- Pharmacovigilance
- Clinical decision support
- RDF provenance
- Semantic Web
- Structured data
- Knowledge discovery
- Formal knowledge representation
- Automated reasoning
- Explainable AI
About this book
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 mapsthat 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.
Editors and Affiliations
About the editors
Dr. Oshani W. Seneviratne is the Director of Health Data Research at the Institute for Data Exploration and Applications at the Rensselaer Polytechnic Institute (Rensselaer IDEA). She obtained her Ph.D. in Computer Science from Massachusetts Institute of Technology in 2014 under the supervision of Sir Tim Berners-Lee, the inventor of the World Wide Web. During her Ph.D., Oshani researched Accountable Systems for the Web. She invented a novel web protocol called HTTPA (HyperText Transfer Protocol with Accountability), and a novel provenance tracking mechanism called the Provenance Tracking Network. This work was demonstrated to be effective in several domains including electronic health care records transfer, and intellectual property protection in Web-based decentralized systems. At Rensselaer IDEA, Oshani leads the Smart Contracts Augmented with Analytics Learning and Semantics (SCALeS) project. The goal of this project is to predict, detect, and fix initially unforeseen situations in smart contracts utilizing novel combinations of machine learning, program analysis, and semantic technologies. Oshani is also involved in the Health Empowerment by Analytics, Learning, and Semantics (HEALS) Project. In HEALS she oversees the research operations targeted at the characterization and analysis of computational medical guidelines for chronic diseases such as diabetes, and the modeling of guideline provenance. Before Rensselaer, Oshani worked at Oracle specializing in distributed systems, provenance and healthcare-related research. She is the co-inventor of two enterprise provenance patents.
Prof. Deborah L. McGuinness is the Tetherless World Senior Constellation Chair and Professor of Computer, Cognitive, and Web Sciences at RPI. She is also the founding director of the Web Science Research Center and the CEO of McGuinness Associates Consulting. Deborah has been recognized with awards as a fellow of the American Association for the Advancement of Science (AAAS) for contributions to the Semantic Web, knowledge representation, and reasoning environments and as the recipient of the Robert Engelmore Award from the Association for the Advancement of Artificial Intelligence (AAAI) for leadership in Semantic Web research and in bridging Artificial Intelligence (AI) and eScience, significant contributions to deployed AI applications, and extensive service to the AI community. Deborah leads a number of large diverse data intensive resource efforts and her team is creating next-generation ontology-enabled research infrastructure for work in large interdisciplinary settings. Prior to joining RPI, Deborah was the acting director of the Knowledge Systems, Artificial Intelligence Laboratory and Senior Research Scientist in the Computer Science Department of Stanford University, and previous to that she was at AT\&T Bell Laboratories. Deborah consults with numerous large corporations as well as emerging startup companies wishing to plan, develop, deploy, and maintain semantic web and/or AI applications. Some areas of recent work include data science, next generation health advisors, ontology design and evolution environments, semantically enabled virtual observatories, semantic integration of scientific data, context-aware mobile applications, search, eCommerce, configuration, and supply chain management. Deborah holds a Bachelor of Math and Computer Science from Duke University, a Master of Computer Science from University of California at Berkeley, and a Ph.D. in Computer Science from Rutgers University.
Bibliographic Information
Book Title: Provenance in Data Science
Book Subtitle: From Data Models to Context-Aware Knowledge Graphs
Editors: Leslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness
Series Title: Advanced Information and Knowledge Processing
DOI: https://doi.org/10.1007/978-3-030-67681-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-67680-3Published: 27 April 2021
Softcover ISBN: 978-3-030-67683-4Published: 28 April 2022
eBook ISBN: 978-3-030-67681-0Published: 26 April 2021
Series ISSN: 1610-3947
Series E-ISSN: 2197-8441
Edition Number: 1
Number of Pages: XI, 110
Number of Illustrations: 24 b/w illustrations
Topics: Knowledge based Systems, Data Mining and Knowledge Discovery, Data Structures and Information Theory, Machine Learning