WhatTheySaid: enriching UK Parliament debates with Semantic Web
WhatTheySaid: enriching UK Parliament debates with Semantic Web
To improve the transparency of politics, the UK Parliament Debate archives have been published online for a long time. However there is still a lack of efficient way to deeply analysis the debate data. WhatTheySaid is an initiative to solve this problem by applying natural language processing and semantic Web technologies to enrich UK Parliament Debate archives and publish them as linked data. It also provides various data visualisations for users to compare debates over years.
Li, Yunjia
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Ding, Chaohai
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Wald, Mike
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5 October 2014
Li, Yunjia
3a0d988e-b5e3-43c9-a268-dc14b5313547
Ding, Chaohai
d07f64ec-a5c8-4167-9d34-9a3913e4dd1a
Wald, Mike
90577cfd-35ae-4e4a-9422-5acffecd89d5
Li, Yunjia, Ding, Chaohai and Wald, Mike
(2014)
WhatTheySaid: enriching UK Parliament debates with Semantic Web.
the 13th International Semantic Web Conference: Post and Demo Session, Riva del Garda, Italy.
19 - 23 Oct 2014.
4 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
To improve the transparency of politics, the UK Parliament Debate archives have been published online for a long time. However there is still a lack of efficient way to deeply analysis the debate data. WhatTheySaid is an initiative to solve this problem by applying natural language processing and semantic Web technologies to enrich UK Parliament Debate archives and publish them as linked data. It also provides various data visualisations for users to compare debates over years.
Text
iswc2014pd.pdf
- Accepted Manuscript
More information
Published date: 5 October 2014
Venue - Dates:
the 13th International Semantic Web Conference: Post and Demo Session, Riva del Garda, Italy, 2014-10-19 - 2014-10-23
Organisations:
Web & Internet Science
Identifiers
Local EPrints ID: 369998
URI: http://eprints.soton.ac.uk/id/eprint/369998
PURE UUID: 8067af58-cb9e-4c96-b949-bae4492ac7b8
Catalogue record
Date deposited: 17 Oct 2014 14:41
Last modified: 14 Mar 2024 18:10
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Contributors
Author:
Yunjia Li
Author:
Chaohai Ding
Author:
Mike Wald
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