Conference paper Open Access

Open Data Empowerment of Digital Humanities by Wikipedia/DBpedia Gamification and Crowd Curation –WiQiZi's Challenges with APIs and SPARQL

Sugimoto Go

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        <foaf:name>Sugimoto Go</foaf:name>
            <foaf:name>Austrian Centre for Digital Humanities</foaf:name>
    <dct:title>Open Data Empowerment of Digital Humanities by Wikipedia/DBpedia Gamification and Crowd Curation –WiQiZi's Challenges with APIs and SPARQL</dct:title>
    <dct:issued rdf:datatype="">2019</dct:issued>
    <dcat:keyword>Digital Humanities</dcat:keyword>
    <dcat:keyword>Application Programming Interfaces</dcat:keyword>
    <dcat:keyword>Linked Open Data</dcat:keyword>
    <dcat:keyword>crowd sourcing</dcat:keyword>
    <dcat:keyword>data curation</dcat:keyword>
    <dct:issued rdf:datatype="">2019-09-30</dct:issued>
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    <dct:description>&lt;p&gt;Digital Humanities (DH) enjoys a wealth of Open Data published by cultural heritage institutions and academic researchers. In particular, Linked Open Data (LOD) offers an excellent opportunity to publish, share, and connect a broad array of structured data in the distributed web ecosystem. However, a real break-through in humanities research as well as its societal impact has not been visible, due to several challenging obstacles including lack of awareness, expertise, technology, and data quality. In order to remove such barriers, this article outlines an experimental case study of Application Programming Interfaces (APIs) and SPARQL. WiQiZi project employs a gamification technique to develop a simple quiz application to guess the age of a randomly selected image from Wikipedia/DBpedia. The project demonstrates a potential of gamification of Open Data not only for edutainment for the public, but also for an inspirational source of DH research. In addition, a face detection API based on an Artificial Intelligence is included for hint function, which would increase both the public and academic interests of new technology for DH. Moreover, the project provides a possibility for crowd data curation for which the users are encouraged to check and improve the data quality, when the application fails to calculate the answer. This method seems to create a win-win scenario for the Wikipedia/DBpedia community, the public, and academia.&lt;/p&gt;</dct:description>
    <dct:description xml:lang="">This is an extended version of the article found at</dct:description>
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