One of the intuitive ways to perform this task is to provide hand-written regular expressions (REs) like [59,60]. The results are promising but the number of manually-written REs (165 REs for a 9-concept ontology [59]) makes it hard to handle. More, their approach does not focus on scalability unlike [61,40] who propose a REs pattern-based tool named OnTeA. OnTeA takes advantage of Hadoop MapReduce to scale. More and more, automatic approaches had been proposed. It is the case of KNOWITALL [62] and TextRunner. The former uses predefined patterns and rule templates to populate classes in a given ontology. Though automatic, KNOWITALL does not scale: a webdocument is processed several times for patterns matching and many web-queries are done to assign a probability to a concept, etc. Thus, TextRunner which implements the new extraction paradigm of Open Information Extraction (OIE) had been introduced. In OIE, we are not limited in a set of triples but try to extract all of them [8,47]. More recently, following REVERB, [63] present OLLIE. Unlike REVERB, OLLIE can extract relation not mediated by verb and in certain case can provide the context of a relation (e.g: “If he wins five key states, Romney will be elected President.” −→(the wining of key states determines the election fact)).