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Record Nr. |
UNINA9910163028403321 |
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Titolo |
Exploiting Linked Data and Knowledge Graphs in Large Organisations [[electronic resource] /] / edited by Jeff Z. Pan, Guido Vetere, Jose Manuel Gomez-Perez, Honghan Wu |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XVIII, 266 p. 59 illus., 44 illus. in color.) |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Data mining |
Application software |
Management information systems |
Artificial Intelligence |
Data Mining and Knowledge Discovery |
Information Systems Applications (incl. Internet) |
Business Information Systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Part I Knowledge Graph Foundations & Architecture -- Part II Constructing, Understanding and Consuming Knowledge Graphs -- Part III Industrial Applications and Successful Stories. |
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Sommario/riassunto |
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This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and “standard” data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps. It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main |
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phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs. Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions. |
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