In the paper we devise a novel algorithm related to the area of natural language processing. The algorithm is capable of building a mapping between the sets of semantic features and the words available in semantic dictionaries called wordnets. In our research we consider wordnets as ontologies, paying particular attention to hypernymy relation. The correctness of the proposal is verified experimentally based on a selected set of semantic features. plWordNet semantic dictionary is considered as a reference source, providing required information for the mapping. The algorithm is evaluated on an instance of a decision problem related to data classification. The quality measures of the classification include: false positive rate, false negative rate and accuracy. A measure of a strength of membership (SOM) in a semantic feature class is proposed and its impact on the aforementioned quality measures is evaluated.