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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Veröffentlicht in:2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) S. 297 - 298
Hauptverfasser: Collis, Nicholas, Frommholz, Ingo
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2021
Schlagworte:
Complexity theory
Crowdsourcing
Knowledge discovery
Libraries
Linked data
Natural Language
Natural languages
Question Answering
SPARQL
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Zusammenfassung: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