Journal of Data Processing Ali Benafia, Smaine Mazouzi, Ramdane Maamri, Zaidi Sahnoun, Sara Benafia 7 4 2017 http://www.dline.info/jdp/fulltext/v7n4/jdpv7n4_3.pdf This paper presents a novel approach of information extraction for building ontologies covering an extensive range of applications from corpora. Our goal is to propose a method that is independent of domains and based on a distributional analysis of semantic units to bring out all the candidates informative elements (concepts, entities, semantic relations, named entities ...).This method is based on a pipeline of four main stages allowing to refine the extraction information from unstructured text in the form of a suite of decomposable representations (sentences in triplets, ‘argumental structure’…) until to get a consistent final ontology. We applied the pipeline defined in the context of a repeated sampling of 100 articles randomly drawn from text corpus (‘Le Monde’ with annual version ‘2013’). For the evaluation results of the trial implementation of our system , we have achieved a level of accuracy at which was up to 74% . We believe from the results obtained that our methodology is quite generic, and can be easily adapted to any new domain.