Korean Linked Data on the Web: Text to RDF
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Interlinking data coming from different sources has been a long standing goal  aiming to increase reusability, discoverability, and as a result the usefulness of information. Nowadays, Linked Open Data (LOD) tackles this issue in the context of semantic web. However, currently most of the web data is stored in relational databases and published as unstructured text. This triggers the need of (i) combining the current semantic technologies with relational databases; (ii) processing text integrating several NLP tools, and being able to query the outcome using the standard semantic web query language: SPARQL; and (iii) linking the outcome with the LOD cloud. The work presented here shows a solution for the needs listed above in the context of Korean language, but our approach can be adapted to other languages as well.
KeywordsNLP2RDF Linked Open Data Korean RDF Reasoning
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