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        <identifier>oai:dcpapers.dublincore.org:952137580</identifier>
        <datestamp>2017-11-27</datestamp>
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      <dc:title>Aggregating Metadata from Heterogeneous Pop Culture Resources  on the Web</dc:title>
      <dc:creator>Kiryakos, Senan</dc:creator>
      <dc:creator>Sugimoto, Shigeo</dc:creator>
      <dc:creator>Nagamori, Mitsuharu</dc:creator>
      <dc:creator>Mihara, Tetsuya</dc:creator>
      <dc:subject>metadata</dc:subject>
      <dc:subject>pop culture resources</dc:subject>
      <dc:subject>Manga</dc:subject>
      <dc:subject>metadata aggregation model</dc:subject>
      <dc:subject>OAI-ORE</dc:subject>
      <dc:description>Japanese pop culture resources, such as manga, anime, and
                    video games, have recently experienced an increase in both their consumption,
                    and appreciation for their cultural significance. Traditionally seen as solely
                    recreational resources, the level of bibliographic description by cultural
                    heritage institutions has not kept up with the needs of users. In seeking to
                    remedy this, we propose the aggregation of institutional data, and rich hobbyist
                    data sourced from the web. Focusing on manga, a form of Japanese comic, this
                    paper discusses classification and aggregation, with the goal of improving
                    bibliographic description through the use of fan created data. Bibliographic
                    metadata for manga was collected from the Japanese Agency for CulturalAffairs
                    media arts database, along with several Englishlanguage manga fan websites.The
                    datawas organized into classesto enable property matching across data providers,
                    and then tested with existing ontologies and aggregation models,namely Europeana
                    and the Open Archives Initiative’s Object Reuse and Exchange, to determine their
                    suitability in working with these unique resources. The results show that
                    existing ontologies may be suitable for use with pop culture materials, but that
                    new vocabulary terms may need to be created if there is an abundance of granular
                    data that existing ontologies fail to properly describe. In addition, the
                    OAI-OREn aggregation method proved to be more promising than EDM when examining
                    the aggregation of related pop culture resources. The paper discusses these
                    issues, as well as recommendations for addressing them moving
                    forward.</dc:description>
      <dc:publisher>Dublin Core Metadata Initiative</dc:publisher>
      <dc:date>2017-11-27</dc:date>
      <dc:type>Text</dc:type>
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      <dc:identifier>https://doi.org/10.23106/dcmi.952137580</dc:identifier>
      <dc:identifier>https://dcpapers.dublincore.org/article/952137580</dc:identifier>
      <dc:source>Dublin Core Metadata Initiative Conference Proceedings</dc:source>
      <dc:language>en</dc:language>
      <dc:relation>https://www.wikidata.org/wiki/Q120636669</dc:relation>
      <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
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