Abstract
While the Web of Data, the Web of Documents and Natural Language Processing are well researched individual fields, approaches to combine all three are fragmented and not yet well aligned. This chapter analyzes current efforts in collaborative knowledge extraction to uncover connection points between the three fields. The special focus is on three prominent RDF data sets (DBpedia, LinkedGeoData and Wiktionary2RDF), which allow users to influence the knowledge extraction process by adding another crowd-sourced layer on top. The recently published NLP Interchange Format (NIF) provides a way to annotate textual resources on the Web through the assignment of URIs with fragment identifiers. We will show how this formalism can easily be extended to encompass new annotation layers and vocabularies.
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Notes
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More data sets can be explored here: http://thedatahub.org/tag/published-by-third-party
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http://factforge.net or http://lod.openlinksw.com provide SPARQL interfaces to query billions of aggregated facts.
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For DBpedia Live see http://live.dbpedia.org/
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See http://en.wiktionary.org/wiki/semantic for a simple example page
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For English see http://en.wiktionary.org/wiki/Wiktionary:ELE
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for example http://wiktionary.dbpedia.org/resource/dog
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Note that with ‘/’ the identifier is sent to the server during a request (e.g. Linked Data), while everything after ‘#’ can only be processed by the client.
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for the resolution of prefixes, we refer the reader to http://prefix.cc
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Acknowledgements
We would like to thank our colleagues from AKSW research group and the LOD2 project for their helpful comments during the development of NIF. Especially, we would like to thank Christian Chiarcos for his support while using OLiA and Jonas Brekle for his work on Wiktionary2RDF. This work was partially supported by a grant from the European Union’s 7th Framework Programme provided for the project LOD2 (GA no. 257943).
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Hellmann, S., Auer, S. (2013). TowardsWeb-Scale Collaborative Knowledge Extraction. In: Gurevych, I., Kim, J. (eds) The People’s Web Meets NLP. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35085-6_11
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