Knowledge Obtention Combining Information Extraction Techniques with Linked Data

Abstract

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.

NEREA: Named Entity Recognition and Disambiguation Exploiting Local Document Repositories

“…If we place these works in the context of creating infoboxes in a local document database, more control over customizing the construction of these infoboxes, or even the definition of templates, is missing. Moreover, the generation of infoboxes should also benefit from the use of the local resources for classifying and tagging, like taxonomies, thesaurus, or ontologies, as we have proposed in previous proposals such as [22] and [23]. Finally, it is also necessary to use information extraction tools able to obtain data concerning the entities, both from the local repository and from open and global repositories located on the Web.…”