In this paper the important problem of ontology clustering is considered with the

purpose of optimization of intelligent data processing in conditions of uncertainty caused by

inaccuracy or incompleteness of data in the subject area. The clustering of ontologies is the

process of automatic splitting of a set of ontologies into groups (clusters) based on their

similarity degree. For the resolution of this problem it is necessary to adopt the set of measures

for the affinity of ontologies, to choose or develop an algorithm of clusterization and to execute

the thorough interpretation of clusterization results.

For the clustering of ontologies in conditions of uncertainty, it is proposed to use a

stochastic game method. A repetitive stochastic game consists in the implementation of a

controlled random process for selecting clusters of ontologies. To this effect, the intelligent

agents, assigned to ontology, randomly, simultaneously and independently choose one of the

clusters at discrete moments of time. For agents that have selected a cluster, the current

measure of similarity of ontologies is calculated, which takes into account the proximity of

concepts, attributes, and relationships between concepts. This measure is used to adapt the

recalculation of mixed player strategies. Thus, the probability of selection is increased for

clusters having the composition, which led to the growth of the ontologies similarity degree.

During the repetitive game, agents will form vectors of mixed strategies that will maximize the

averaged measures of similarity to clusters of ontologies.

To solve the problem of game clusterization for ontologies, an adaptive Markovian

recurrent method was developed based on stochastic approximation of a modified

complementary slackness condition, valid at the points of the Nash equilibrium. The proposed

game method has filtering properties for spikes in the input data and practically does not

depend on the law of distribution of random noises.

The computer modeling confirmed the possibility of using a stochastic game model for

clustering ontologies, taking into account uncertainty factors. Convergence of the game

method is ensured by observing the fundamental conditions and restrictions of stochastic

optimization. The reliability of experimental studies is confirmed by the repeatability of results

obtained for various sequences of random variables.

The results of the work could be used to solve the problems of intellectual data analysis,

to eliminate duplication of information in knowledge bases, to reduce uncertainty within the

cluster of ontologies, to identify the novelty of information, to organize high-level semantic

interaction between agents in the course of executing their common task.

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