An important task of ontology learning is to enrich the vocabulary for domain ontologies using different sources of information. WordNet, an online lexical database covering many domains, has been widely used as a source from which to mine new vocabulary for ontology enrichment. However, since each word submitted to WordNet may have several different meanings (senses), existing approaches still face the problem of semantic disambiguation in order to select the correct sense for the new vocabulary to be added. In this paper, we present a similarity computation method that allows us to efficiently select the correct WordNet sense for a concept-word in a given ontology. Once the correct sense is identified, we can then enrich the concept's vocabularly using nearby words in WordNet. Experimental results using an amphibian ontology show that the similarity computation method reach a good average accuracy and our approach is able to enrich the vocabulary of each concept with words mined from WordNet synonyms and hypernyms.