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Are linked datasets fit for open-domain question answering? A quality assessment

 
: Thakkar, H.; Endris, K.M.; Gimenez-Garcia, J.M.; Debattista, J.; Lange, C.; Auer, S.

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Association for Computing Machinery -ACM-:
6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016. Proceedings : Nîmes, France, June 13 - 15, 2016
New York: ACM, 2016
ISBN: 978-1-4503-4056-4
Art. 19
International Conference on Web Intelligence, Mining and Semantics (WIMS) <6, 2016, Nimes>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

Abstract
The current decade is a witness to an enormous explosion of data being published on the Web as Linked Data to maximise its reusability. Answering questions that users speak or write in natural language is an increasingly popular application scenario for Web Data, especially when the domain of the questions is not limited to a domain where dedicated curated datasets exist, like in medicine. The increasing use of Web Data in this and other settings has highlighted the importance of assessing its quality. While quite some work has been done with regard to assessing the quality of Linked Data, only few efforts have been dedicated to quality assessment of linked data from the question answering domain's perspective. From the linked data quality metrics that have so far been well documented in the literature, we have identified those that are most relevant for QA.

: http://publica.fraunhofer.de/dokumente/N-422289.html