A.I. Kanev – Post-graduate Student,

Bauman Moscow State Technical University (Moscow, Russia) E-mail: kanevai@student.bmstu.ru

Using traditional information retrieval, the user is faced with the need of repeated queries and analysis of intermediate data if he cannot accurately formulate the initial query. Therefore, in recent years, ways to increase the capabilities of information retrieval have been actively developing: query cards, visual presentation of document topics, neural networks.Another direction is semantic search. The author of this article proposes to use semantic search and text mining to more accurately interpret user queries and improve search quality. One of the main tasks for text mining is representation of knowledge gained during natural language processing. Two main approaches are used for natural language processing: rule-based and statistical with machine learning. Each of them has their advantages and disadvantages. The metagraph approach allows to combine soft computing and knowledge processing methods andit is chosen to implement the representation of knowledge obtained in the process of text mining. Attributes of metavertexes and metaedgeswith real number values are used for machine learning.

The purpose of this paper is a description of requirements for a semantic search system using text mining and metagraph knowledge base.

The paper describes formulas for ranking query results using the knowledge gained in the analysis of the query and text documents. They are required to combine index results for various concepts and relations from the knowledge base.

Two variants for ranking query results differs one form each other in way of knowledge processing. The first one obtains concepts and relations only from queries and uses classic search index for words with the same meanings from knowledge base. The second variant gains concepts and relations from queries and documents and usesspecial semantic index to search documents.

The proposed information retrieval technique can be used to improve the accuracy and completeness of search engines primarily on small amounts of data that are not enough for existing methods.The emergence property of the metagraph allows to store and process data from small amount of information, gradually detailing it. Using a single knowledge base for all languages it allows to index documents and process queries simultaneously in different languages.

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