The objective of this thesis is to propose a framework for the automatic collection and analysis of data about blackouts, based on an ontological approach and Natural Language Processing (NLP), which extracts information from human-written text, allowing researchers to better focus on the entities that play a major role in power outages and the relations among them. After automatically retrieving news articles from Google News, an ontology suitable for blackout analysis, the real core of this work, was designed and different experiments were conducted to study its impact on the NLP model for Named Entity Recognition (NER) and Relationship Extraction (RE).