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Herausgeber: 
  • Ian Horrocks
  • Thomas Eiter
  • Michael Kifer
  • Diego Calvanese
  • Jeff Z. Pan
  • Fangzhen Lin
  • Yuting Zhao
  • Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering - 12th International Summer School 2016, Aberdeen, UK, September 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

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    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  Februar 2017  
    Genre:  EDV / Informatik 
    ISBN:  9783319494920 
    EAN-Code: 
    9783319494920 
    Verlag:  Springer International Publishing 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Information Systems and Applications, incl. Internet/Web, and HCI
    #9885 - Lecture Notes in Computer Science  
    Dimensionen:  H 235 mm / B 155 mm / D 16 mm 
    Gewicht:  423 gr 
    Seiten:  276 
    Zus. Info:  Paperback 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This volume contains some lecture notes of the 12th Reasoning Web Summer School (RW 2016), held in Aberdeen, UK, in September 2016.In 2016, the theme of the school was ¿Logical Foundation of Knowledge Graph Construction and Query Answering¿. The notion of knowledge graph has become popular since Google started to use it to improve its search engine in 2012. Inspired by the success of Google, knowledge graphs are gaining momentum in the World Wide Web arena. Recent years have witnessed increasing industrial take-ups by other Internet giants, including Facebook's Open Graph and Microsoft's Satori. The aim of the lecture note is to provide a logical foundation for constructing and querying knowledge graphs. Our journey starts from the introduction of Knowledge Graph as well as its history, and the construction of knowledge graphs by considering both explicit and implicit author intentions. The book will then cover various topics, including how to revise and reuseontologies (schema of knowledge graphs) in a safe way, how to combine navigational queries with basic pattern matching queries for knowledge graph, how to setup a environment to do experiments on knowledge graphs, how to deal with inconsistencies and fuzziness in ontologies and knowledge graphs, and how to combine machine learning and machine reasoning for knowledge graphs.

      
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