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Search is among the most frequent activities on the Web. However, the search activity still requires extra efforts in order to get satisfactory results. One of the reasons is heterogeneous information resources and exponential growth of information. In this paper we try to tackle these issues. We elaborate on an approach to construction of semantic-linguistic feature vectors (FV) that are used in search. These FVs are built based on domain semantics encoded in an ontology and enhanced by relevant terminology from Web documents. The value of this approach is twofold. First, it captures relevant semantics from an ontology, and, second, it accounts for statistically significant collocations of other terms and phrases in relation to the ontology entities. In this paper, we elaborate on the extended FV construction process and evaluate the FV quality with respect to a set of heterogeneous ontologies. The evaluation shows that ranking of entities is significant neither for FV quality nor FV construction process. However, the results demonstrate that the construction process is most sensitive to taxonomy type of ontologies while usage of advanced and rich ontologies produces better quality FVs.