In the article authors proposed the method of problem-oriented knowledge elements search and similarity estimation in subject area ontology given in a form of semantic net. The knowledge relevance is estimated by closeness to a certain similarity estimation measure between concepts included in integrated ontology elements meta-descriptions of intellectual information systems interdisciplinary software environment. To calculate knowledge elements semantic closeness and coherence authors developed a combined model of semantic similarity estimation involving a set of interpreted measure of taxonomical and associative dependences represented in meta-descriptions. The methodology is based on relative position of ontology graph concepts in common hierarchy and on measures of similarity between properties in high-dimensional attribute space. Authors developed an algorithm to calculate parameters values of semantic similarity estimation model on the basis of evolutionary procedures and genetic optimum search. The proposed algorithm is based on the usage of evolutionary processes of reproduction, crossover, mutation and natural selection analogues. To analyze the developed method a set of experiments was carried out. The obtained data shows theoretical significance and prospects of such method and allows us to determine optimal values of algorithm parameters.
