METHOD OF BUILDING THE SEMANTIC NETWORK OF DISTRIBUTED SEARCH IN E-LEARNING
AbstractThe subject matter of the article is semantic networks of distributed search in e-learning. The goal is to synthesize a decision tree and a stratified semantic network that allows network intelligent agents in the e-learning to construct inference mechanisms according to the required attributes and specified relationships. The following results are obtained. The model of the base decision tree in e-learning is suggested. To simulate the decision tree in e-learning, the logic of predicates of the first order was used, which enabled making calculations both at the nodes of the tree and at its edges, and making decisions based on the results of calculations; applying partitioning operations to select individual fragments; specifying the solutions with further expanding the inference upper vertices; expanding the multi-level model vertically and horizontally. At the first stage of the model formalization, the graph of the base decision tree was constructed, whose nodes represent a substructure capable of performing an autonomous search subtask. The second stage is filling the base tree with semantic information and organizing its interaction with network intelligent agents. To provide the tree branches of decisions in e-learning with information, the process of stratified expansion of the base decision tree was suggested where the components of the decision node were detailed and the links among the received sub-units were established both on the horizontal and on the vertical levels. It is shown that in order to establish a set of goals and search problems on the studied structure, it suffices to determine: the graphs of goals and search problems for each node type; a set of edges that determine the dependence of the execution of search targets for the nodes that are not of the same type; a set of pointers that establish probable relationships for redistributing resources in accordance with the requirements of intelligent agents; communication mapping. The developed mathematical model of the base decision tree enabled a stratified expansion. Determining intensions and extensions allowed stratified semantic networks to be used for searching. Conclusions. The method of synthesizing a decision tree and a stratified semantic network is suggested; this method enables considering them as closely interrelated ones in the context of distributed search in e-learning. As a result, the process of searching and designing inference mechanisms can be formalized by the network intelligent agents according to the required attributes and given relationships.
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