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Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering

12th International Summer School 2016, Aberdeen, UK, September 5-9, 2016, Tutorial Lectures

  • Jeff Z. Pan
  • Diego Calvanese
  • Thomas Eiter
  • Ian Horrocks
  • Michael Kifer
  • Fangzhen Lin
  • Yuting Zhao

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9885)

Also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 9885)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Jeff Z. Pan, Nico Matentzoglu, Caroline Jay, Markel Vigo, Yuting Zhao
    Pages 1-26
  3. Elena Botoeva, Boris Konev, Carsten Lutz, Vladislav Ryzhikov, Frank Wolter, Michael Zakharyaschev
    Pages 27-89
  4. Juan L. Reutter, Domagoj Vrgoč
    Pages 90-123
  5. Wouter Beek, Laurens Rietveld, Filip Ilievski, Stefan Schlobach
    Pages 124-155
  6. Meghyn Bienvenu, Camille Bourgaux
    Pages 156-202
  7. Umberto Straccia, Fernando Bobillo
    Pages 203-240
  8. Back Matter
    Pages 259-259

About this book

Introduction

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 reuse ontologies (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.

Keywords

artificial intelligence database systems description logics fuzzy logic graph databases knowledge graphs knowledge representation linked data Linked Open Data (LOD) OWL2-DL query answering Resource Description Framework (RDF) semantic networks semantic web SPARQL Web Ontology Language (OWL)

Editors and affiliations

  • Jeff Z. Pan
    • 1
  • Diego Calvanese
    • 2
  • Thomas Eiter
    • 3
  • Ian Horrocks
    • 4
  • Michael Kifer
    • 5
  • Fangzhen Lin
    • 6
  • Yuting Zhao
    • 7
  1. 1.University of Aberdeen AberdeenUnited Kingdom
  2. 2.Free University of Bozen-Bolzano BolzanoItaly
  3. 3.University of Technology Vienna ViennaAustria
  4. 4.University of Oxford OxfordUnited Kingdom
  5. 5.Stony Brook University Stony BrookUSA
  6. 6.University of Science and Technology Hong KongChina
  7. 7.University of Aberdeen AberdeenUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-49493-7
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-49492-0
  • Online ISBN 978-3-319-49493-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site