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 the new obtained structured data 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.