Relation Extraction for Semantic Intranet Annotations

We present an approach for ontology driven extraction of relations from texts aimed mainly to produce enriched semantic annotations for the Semantic Web. The approach exploits linguistic and empirical strategies, by means of a pipeline method involving processes such as a parser, part-of-speech tagger, named entity recognition system, and pattern-based classification, and resources including ontology, knowledge and lexical databases. A preliminary evaluation with 25 sentences showed that the use of knowledge intensive resources and strategies together with corpus-based techniques to process the input data allows identifying and discovering relevant relations between known and new entity pairs mentioned in the text. Besides semantic web annotations, the system can be used for other tasks, including ontology population, since it identifies new instantiations of existent relations and entities, and ontology learning, since it discovers new relations, which are not part of the ontology.