• Crowdsourced linked data question answering with AQUACOLD

      Collis, Nick; Frommholz, Ingo; University of Bedfordshire; University of Wolverhampton (IEEE, 2021-12-29)
      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.