Crowdsourced Linked Data Question Answering with AQUACOLD. Collis, N. & Frommholz, I. In Procedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2021), 2021. ACM. Paper abstract bibtex 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.
@inproceedings{Collis2021,
title = {Crowdsourced {Linked} {Data} {Question} {Answering} with {AQUACOLD}},
copyright = {All rights reserved},
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.},
booktitle = {Procedings of the {ACM}/{IEEE} {Joint} {Conference} on {Digital} {Libraries} ({JCDL} 2021)},
publisher = {ACM},
author = {Collis, Nicholas and Frommholz, Ingo},
year = {2021},
url_paper={https://api.zotero.org/users/9984690/publications/items/6MEECPDC/file/view}
}
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