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Annotating korean text documents with linked data resources

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Abstract

Semantic annotation approaches link entities from a knowledge base to mentions of entities in text to provide additional content-related information. Recently increasing use of resources from the Linked Open Data (LOD) Cloud has been made to annotate text documents thanks to the network of machine-understandable, interlinked data. While existing approaches to semantic annotation in the LOD context have been proven to be well performing with the English language, many other languages in general and the Korean language in particular are still underrepresented. We investigate the applicability of existing semantic annotation approaches to the Korean language by adapting two popular approaches in the semantic annotation field and evaluating those approaches on an English-Korean bilingual sense-tagged corpus. Further, general challenges in internationalization of annotation approaches are summarized.

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Notes

  1. http://lod2.eu

  2. http://thedatahub.org/group/lodcloud

  3. http://dbpedia.org

  4. Example sentence taken from washingtonpost.com

  5. http://dbpedia.org/spotlight

  6. Source: www.etoday.co.kr (March 22, 2012)

  7. http://ko.dbpedia.org

  8. http://ko.dbpedia.org

  9. http://lucene.apache.org

  10. http://sourceforge.net/projects/lucenekorean

  11. http://alias-i.com/lingpipe/docs/api/com/aliasi/dict/ExactDictionaryChunker.html

  12. http://dumps.wikimedia.org/kowiki

  13. http://opennlp.apache.org

  14. http://corpora.uni-leipzig.de

  15. http://www.cs.waikato.ac.nz/ml/weka

  16. http://www.ted.com

  17. http://en.wikipedia.org/wiki/Wikipedia:Manual_of_Style/Linking

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Acknowledgements

This research was conducted by the International Collaborative Research and Development Program (Creating Knowledge out of Interlinked Data) and funded by the Korean Ministry of Knowledge Economy.

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Authors and Affiliations

  1. Karlsruhe Institute of Technology, Karlsruhe, Germany

    David Müller

  2. Department of Knowledge Service Engineering, KAIST, Daejeon, Korea

    Mun Yong Yi

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  1. David Müller
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  2. Mun Yong Yi
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Correspondence to Mun Yong Yi.

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Müller, D., Yi, M.Y. Annotating korean text documents with linked data resources. Multimed Tools Appl 68, 413–427 (2014). https://doi.org/10.1007/s11042-012-1339-y

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