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Ontologies provide a structural organizational knowledge to support the exchange and sharing of information. Ontology learning techniques from text have emerged as a set of techniques to get ontologies from unstructured information. An important task in ontology learning is to get the taxonomy. For building a taxonomy, the identification of hypernymy/hyponymy relations between terms is imperative. Previous work have used specific lexical patterns or they have focused on identifying new patterns. Recently, the use of the Web as source of collective knowledge seems a good option for finding appropriate hypernyms. This paper introduces an approach to find hypernymy relations between terms belonging to a specific knowledge domain. This approach combines WordNet synsets and context information for building an extended query set. This query set is sent to a web search engine in order to retrieve the most representative hypernym for a term.