众包关联数据问题回答与水产养殖

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) Pub Date : 2021-09-01 DOI:10.1109/JCDL52503.2021.00043

Nick Collis, Ingo Frommholz

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引用次数: 0

摘要

问题回答(QA)需要通过关联数据返回复杂的自然语言问题的准确答案，通过抽象SPARQL的复杂性同时保留其表达性来提高关联数据(LD)搜索的可访问性。这项工作提出了水产养殖，一个利用众包的力量来满足这一需求的LD质量保证系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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 system which harnesses the power of crowdsourcing to meet this need.

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2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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