• DocumentCode
    741424
  • Title

    Question Answering over Knowledge Bases

  • Author

    Liu, Kang ; Zhao, Jun ; He, Shizhu ; Zhang, Yuanzhe

  • Author_Institution
    Institute of Automation, Chinese Academy of Sciences
  • Volume
    30
  • Issue
    5
  • fYear
    2015
  • Firstpage
    26
  • Lastpage
    35
  • Abstract
    Question answering over knowledge bases is a challenging task for next-generation search engines. The core of this task is to understand the meaning of questions and translate them into structured language-based queries. Previous research has focused on a specific knowledge base with a constrained domain, but with the increase in the size and domain of existing knowledge bases, fulfilling this aim is even more challenging. This article introduces the mainstream methods for question answering over knowledge bases, describing typical semantic meaning representation models and state-of-the-art systems for converting questions to predefined logical forms. It also puts a particular focus on the approaches for question answering over a large-scale knowledge base and multiple heterogeneous knowledge bases.
  • Keywords
    Adaptation models; Grammar; Knowledge based systems; Knowledge discovery; Natural languages; Semantics; Syntactics; NLP; intelligent systems; knowledge base; natural language processing; question answering; semantic parsing;
  • fLanguage
    English
  • Journal_Title
    Intelligent Systems, IEEE
  • Publisher
    ieee
  • ISSN
    1541-1672
  • Type

    jour

  • DOI
    10.1109/MIS.2015.70
  • Filename
    7243222