Knowledge Obtention Combining Information Extraction Techniques with Linked Data

Today, we can find a vast amount of textual information stored in proprietary data stores. The experience of searching information in these systems could be improved in a remarkable manner if we combine these private data stores with the information supplied by the Internet, merging both data sources to get new knowledge. In this paper, we propose an architecture with the goal of automatically obtaining knowledge about entities (e.g., persons, places, organizations, etc.) from a set of natural text documents, building smart data from raw data. We have tested the system in the context of the news archive of a real Media Group.