Abstract
The paper presents a novel method of information retrieval using semiotic models based on multilayer semantic graphs. The layers correspond to the level of detail of the extracted semantic information. The key developments underlying the method are described. Firstly, it is a graph model, which can be automatically built on the basis of general linguistic dictionaries or specialized encyclopedias. Using formalized text sources allows us to build a graph in a reasonable amount of time. The graph vertices are presented by canonical forms of words. Three types of connections are used as graph edges: association, definition, and synonymy. A method of automatic selection of a connection type based on the analysis of the dictionary entry structure is proposed. Secondly, an approach to using the semantic graph as a flexible basis for complex information retrieval systems is described. Information about existing connections between lexical units can be interpreted in different ways. Experiments show that, depending on the task, information about synonymous or associative relationships may come to the fore. In general, data extracted from dictionaries are reliable and complete enough to use the presented graph as a framework for information retrieval systems.
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Korney, A., Kryuchkova, E., Savchenko, V. (2020). Information Retrieval Approach Using Semiotic Models Based on Multi-layered Semantic Graphs. In: Jordan, V., Filimonov, N., Tarasov, I., Faerman, V. (eds) High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production. HPCST 2020. Communications in Computer and Information Science, vol 1304. Springer, Cham. https://doi.org/10.1007/978-3-030-66895-2_11
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