|Article title||SEMANTIC SEARCH WITH THE USE OF GENETIC OPERATORS|
|Authors||A. A. Novikov, Yu. S. Starkova, V. V. Markov, D. Yu. Kravchenko|
|Section||SECTION II. ARTIFICIAL INTELLIGENCEAND FUZZY SYSTEMS|
|Month, Year||07, 2017 @en|
|Abstract||Traditional search mechanisms are based on keyword search, which does not take into ac-count the semantic links between different concepts. This leads to the loss of relevant documents due to inaccurate wording of the query or the use in the query of related words and concepts. To solve the problems of formulating user requests and interdisciplinarity of concepts, it is sug-gested to use the semantic search. The approach proposed in the article for the implementation of semantic search is applicable to large volumes of text data and is based on the use of a genetic algorithm. Unlike standard approaches to information retrieval, the described approach allows taking into account the semantics of interrelations between concepts, as well as correctly handle interdisciplinary concepts. Thanks to semantic indexing, documents define concepts not presented in the user"s initial query, but semantically close to the concepts from the query. Semantic indexing is performed for each document separately, which allows parallel indexation on several subject areas. By the time of completion of the formation of the ontological profile of the document in question, all semantic distances between pairs of distinguished concepts are calculated. Concepts are considered close in meaning if their semantic proximity value is above a certain threshold value that is specified in the search parameters. Building an ontological document profile is a multicriteria task, since it depends on a lot of characteristics, so genetic algorithms can be used to solve it. The developed genetic algorithm is intended for more accurate distribution of weight coefficients and estimation of semantic proximity of concepts.|
|Keywords||Semantic search; information retrieval; ontology; genetic algorithm.|
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