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        Knowledge Graphs and Big Data Processing

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        Contributor(s)
        Janev, Valentina (editor)
        Graux, Damien (editor)
        Jabeen, Hajira (editor)
        Sallinger, Emanuel (editor)
        Collection
        European Research Council (ERC); EU collection
        Language
        English
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        Abstract
        This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
        URI
        https://library.oapen.org/handle/20.500.12657/41294
        Keywords
        Database Management; Information Systems Applications (incl. Internet); Logic in AI; Computer Appl. in Administrative Data Processing; Business Information Systems; Computer and Information Systems Applications; Computer Application in Administrative Data Processing; artificial intelligence; big data; data analytics; data handling; data integration; data mining; databases; digital storage; domain knowledge; graph theory; information management; information technology; integrated data; internet; knowledge management; knowledge-based system; ontologies; semantics; Databases; Database programming; Information retrieval; Internet searching; Artificial intelligence; Public administration; Information technology: general issues; Business mathematics & systems
        DOI
        10.1007/978-3-030-53199-7
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        2020
        Grantor
        • H2020 European Research Council
        Imprint
        Springer
        Series
        Lecture Notes in Computer Science; Information Systems and Applications, incl. Internet/Web, and HCI, 12072
        Classification
        Databases
        Information retrieval
        Artificial intelligence
        Public administration
        Business mathematics and systems
        Pages
        209
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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