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Abstract

Traditional search mechanisms are based on the keyword search, which does not consider the semantic links between different concepts. This leads to the loss of relevant documents due to inaccurate query formulation or using contextually close words and concepts in the query. To solve the problems of formulating user queries and interdisciplinarity of concepts, it is suggested to use semantic search. The proposed method for implementing semantic search is applicable to large scopes of text data and is based on using a genetic algorithm. Unlike standard methods for information search, the suggested method allows us to consider the semantics of interrelationships between concepts and to handle interdisciplinary concepts correctly. By the aid of semantic tagging, documents contain concepts that are not present in the user's initial query but are semantically close to the requested concepts. Semantic tagging is performed for each document separately, which provides parallel tagging in several subject areas. By the time of the document ontological profile formation is completed, all semantic distances between pairs of distinguished concepts are calculated. Concepts are considered contextually close if their semantic proximity value is above a certain threshold value that is specified in the search parameters. Building a document ontological profile is a multicriteria task, since it depends on a lot of characteristics, so genetic algorithms can be used to solve it effectively. The developed genetic algorithm is intended for more accurate distribution of weight coefficients and estimation of semantic proximity of concepts.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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