Presentation 2014-08-20
Making Legacy Open Data Machine Readable by Crowdsourcing
Satoshi OYAMA, Yukino BABA, Ikki OHMUKAI, Hiroaki DOKOSHI, Hisashi KASHIMA,
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Abstract(in English) Despite recent open data initiatives in many countries, not a few of those countries provide the data in non-machine-readable formats like an image format rather than in a machine-readable electronic format, thereby restricting their usability. An approach is described for converting legacy statistical data in an image format into a machine-readable and reusable format by using crowdsourcing. Requesting crowd workers not only to extract tables from graph images but also to reconstruct them in spreadsheets can reduce the number of errors compared to simple extraction and, at the same time, produces structures including attribute names and values as properties of the reconstructed graph objects. Experimental results using the White Paper on Tourism published by the Japan Tourism Agency demonstrated that the proposed approach is effective.
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Keyword(in English) crowdsourcing / human computation / open data
Paper # AI2014-11,SC2014-8
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Committee AI
Conference Date 2014/8/13(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Making Legacy Open Data Machine Readable by Crowdsourcing
Sub Title (in English)
Keyword(1) crowdsourcing
Keyword(2) human computation
Keyword(3) open data
1st Author's Name Satoshi OYAMA
1st Author's Affiliation Graduate School of Information Science and Technology, Hokkaido University()
2nd Author's Name Yukino BABA
2nd Author's Affiliation National Institute of Informatics
3rd Author's Name Ikki OHMUKAI
3rd Author's Affiliation National Institute of Informatics
4th Author's Name Hiroaki DOKOSHI
4th Author's Affiliation Graduate School of Information Science and Technology, Hokkaido University
5th Author's Name Hisashi KASHIMA
5th Author's Affiliation Graduate School of Informatics, Kyoto University
Date 2014-08-20
Paper # AI2014-11,SC2014-8
Volume (vol) vol.114
Number (no) 181
Page pp.pp.-
#Pages 6
Date of Issue