• DocumentCode
    3303733
  • Title

    Intelligent semantic question answering system

  • Author

    Najmi, Erfan ; Hashmi, Khayyam ; Khazalah, Fayez ; Malik, Zaki

  • Author_Institution
    Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA
  • fYear
    2013
  • fDate
    13-15 June 2013
  • Firstpage
    255
  • Lastpage
    260
  • Abstract
    The volume of information available on the World Wide Web and the rate of its growth requires new techniques to handle and organize this data. Ontologies are becoming the pivotal methodology to represent domain-specific conceptual knowledge and hence help in providing solutions for Question Answering (QA) systems. This paper introduces an approach for enhancing the capabilities of QA systems using semantic technologies. We implemented an approach to convert the natural language user queries to Resource Description Framework (RDF) triples and find relevant answers. The experiment results show that the proposed technique works very well for single word answers. We believe that with some modifications this approach can be expanded to a wider scale.
  • Keywords
    natural language processing; ontologies (artificial intelligence); question answering (information retrieval); RDF triples; World Wide Web; domain specific conceptual knowledge; intelligent semantic question answering system; natural language user queries; ontologies; resource description framework; semantic technologies; Google; Natural languages; Ontologies; Organizations; Resource description framework; Search engines; Semantics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cybernetics (CYBCONF), 2013 IEEE International Conference on
  • Conference_Location
    Lausanne
  • Type

    conf

  • DOI
    10.1109/CYBConf.2013.6617431
  • Filename
    6617431