2. The method of claim 1 , wherein the word-sense disambiguation algorithm is an algorithm that selects the most frequently used sense by determining the frequency of co-occurrence of the term with all related concepts, and selecting a concept with the highest co-occurrence frequency as the term's meaning.
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.