7. The method of claim 1 , wherein the sum of the informativeness values of each monosemantic term and the semantic similarity of its meaning to all other concepts from the document's context is used as the feature.
1. A method of building a semantic model of a document, comprising: extracting an ontology comprising concepts from information sources, the information resources comprising electronic resources with hyperlinked or not hyperlinked descriptions of real-world objects; assigning each concept in the ontology an identifier that unambiguously identifies each concept; transforming hyperlinks between concept descriptions into links between concepts, if hyperlinks between concept descriptions are already present; adding hyperlinks by analyzing the descriptions and inducing term senses from ontologies extracted from hypertext encyclopedias if no hyperlink structure is initially present; transforming hyperlinks into links between concepts; saving an URL of an original concept description; setting at least one textual representation for each concept; computing a frequency of co-occurrence and informativeness for each textual representation of the concept; identifying a natural language of the textual representation; saving obtained information; searching the document to be analyzed for terms and their possible senses by correlating parts of the document's text with textual representations of concepts from a controlled vocabulary; selecting, for each term, one sense from a range of possible senses using a word-sense disambiguation algorithm and considering this sense to be the term's meaning; wherein the word-sense disambiguation algorithm is an algorithm based on supervised machine learning, wherein a feature vector is computed for each term, and the most suitable sense is identified from that vector; and wherein a usage probability of term t in given sense m i , computed as P t ⁡ ( m i ) = c ⁡ ( t , m i ) ∑ i ⁢ ⁢ c ⁡ ( t , m i ) , where c(t, m i ) is the frequency of co-occurrence of term t in sense m i , is used as the feature; ranking concepts that correspond to term senses by importance to the text; and building the semantic model of the document by determining highest ranked concepts.