Article

Article title KNOWLEDGE SIFTER MODEL FOR SEMANTIC IDENTIFICATION PROBLEMS
Authors Yu. A. Kravchenko
Section SECTION IV. DATA ANALYSIS AND KNOWLEDGE MANAGEMENT
Month, Year 04, 2018 @en
Index UDC 004.89
DOI
Abstract This article is devoted to solving the problem of knowledge intellectual accumulation and processing of abstract universal models constructing by the search and decision-making semantics formalization in the knowledge processing. A special role in the solution of this problem plays the reduction of information uncertainty in the information flow accompanying the process of knowledge accumulation and the use of appropriate means for information analyzing to perform the necessary optimization procedures, forecasting and study the feasibility of the results. Concrete scientific result is agent model of knowledge filter which can solve problems of semantic identification of key information and processing of heterogeneous knowledge resources on the basis of ontology-based structures. The application of semantic identification procedure as a tool for the classification analysis of knowledge requires, first of all, the clarification of generic concepts that will serve as identifiers in the selection of the appropriate lexical-semantic groups of terms. Elements of knowledge in the network resources are depersonalized, which leads to desemantization, i.e. attributing the meaning to the term, which the term did not have before, and then using the term in a new meaning, at the same time flouting the old meaning. This raises the problem of semantic identification, which requires a complex and lengthy learning of the linguistic features of the source being processed to reflect the meaning and construct a system of knowledge elements relations on the set. The solution of semantic ambiguity problem required the development of ontological methods for finding a one-to-one correspondence of the identical and similar.

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Keywords Semantic models; knowledge search and processing; agent models; knowledge sifter; semantic identification; ontological structure.
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