Question Answering and Knowledge Graphs

  • Alessandro Moschitti
  • Kateryna Tymoshenko
  • Panos Alexopoulos
  • Andrew Walker
  • Massimo Nicosia
  • Guido Vetere
  • Alessandro Faraotti
  • Marco Monti
  • Jeff Z. Pan
  • Honghan Wu
  • Yuting Zhao
Chapter

Abstract

In the Digital and Information Age, companies and government agencies are highly digitalized, as the information exchanges happening in their processes. They store information both as natural language text and structured data, e.g., relational databases or knowledge graphs. In this scenario, methods for organizing, finding, and selecting relevant information, beyond the capabilities of classic Information Retrieval, are always active topics of research and development.

Keywords

Natural Language Processing Mean Average Precision Parse Tree Question Answering Name Entity Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Alessandro Moschitti
    • 1
  • Kateryna Tymoshenko
    • 2
  • Panos Alexopoulos
    • 3
  • Andrew Walker
    • 4
  • Massimo Nicosia
    • 1
  • Guido Vetere
    • 5
  • Alessandro Faraotti
    • 5
  • Marco Monti
    • 6
  • Jeff Z. Pan
    • 4
  • Honghan Wu
    • 7
  • Yuting Zhao
    • 6
  1. 1.University of TrentoTrentoItaly
  2. 2.Trento RISE, Povo di TrentoTrentoItaly
  3. 3.Expert SystemMadridSpain
  4. 4.University of Aberdeen, King’s CollegeAberdeenUK
  5. 5.IBM ItaliaRomeItaly
  6. 6.IBM ItaliaMilanItaly
  7. 7.King’s College LondonLondonUK

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