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Crowdsourced linked data question answering with AQUACOLD

  • Nicholas Collis
  • , Ingo Frommholz
  • University of Bedfordshire
  • University of Wolverhampton

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

There is a need for Question Answering (QA) to return accurate answers to complex natural language questions over Linked Data, improving the accessibility of Linked Data (LD) search by abstracting the complexity of SPARQL whilst retaining its expressiveness. This work presents AQUACOLD, a LD QA system which harnesses the power of crowdsourcing to meet this need.
Original languageEnglish
Title of host publicationProceedings - 2021 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2021
EditorsJ. Stephen Downie, Dana McKay, Hussein Suleman, David M. Nichols, Faryaneh Poursardar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages297-298
Number of pages2
ISBN (Electronic)9781665417709
DOIs
Publication statusPublished - 29 Dec 2021
EventACM/IEEE Joint Conference on Digital Libraries (JCDL) - Champaign, IL
Duration: 27 Sept 202130 Sept 2021

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
Volume2021-September
ISSN (Print)1552-5996

Conference

ConferenceACM/IEEE Joint Conference on Digital Libraries (JCDL)
CityChampaign, IL
Period27/09/2130/09/21
OtherACM/IEEE Joint Conference on Digital Libraries (JCDL) (27/09/2021-30/09/2021, Champaign, IL)

Keywords

  • Linked Data
  • Crowdsourcing
  • SPARQL
  • Question Answering
  • Natural Language

ASJC Scopus subject areas

  • General Engineering

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