Crowdsourced Linked Data Question Answering with AQUACOLD

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 syst...

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Vydáno v:2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) s. 297 - 298
Hlavní autoři: Collis, Nicholas, Frommholz, Ingo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2021
Témata:
Complexity theory
Crowdsourcing
Knowledge discovery
Libraries
Linked data
Natural Language
Natural languages
Question Answering
SPARQL
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Popis
Shrnutí: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.
DOI:10.1109/JCDL52503.2021.00043