Methodology for Building a Prototype System for Complex Data Analysis of Thematic Sites
https://doi.org/10.35596/1729-7648-2023-21-4-101-109
Abstract
One of the modern directions of obtaining information for making informed decisions is the analysis of data from open Internet sources, the analysis of media containing hundreds of thousands of publications. It is critically important not only to obtain reliable information, but also the time needed to obtain and analyze it. The purpose of the research in this work is the development and testing of a complex methodology for quickly building a prototype of a system for complex analysis of thematic sites. A technology of interconnected methods, methodologies, and tools for building a graph database, a knowledge graph, data analysis using methods and mo dels of machine learning with the provision of analytical results to users has been created. The main task of this work is to use these technologies to analyze data from well-known world sites in order to build a prototype of a systems for complex analysis of data from Internet sources.
Keywords
complex analysis system,
RDF schema,
RDF dictionaries and ontologies,
machine learning,
graph databases,
graph algorithms,
PageRank
Supporting agencies: The authors are grateful to the students of the Belarusian State University of Informatics and Ra dioelectronics P. A. Zorko and O. A. Kulevich for approbation of technological solutions
About the Authors
I. I. Piletski
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Piletski Ivan Ivanavich - Cand. of Sci., Associate Professor at the Department of Informatics.
220013, Minsk, P. Brovki St., 6. Tel.: +375 17 293-23-01
M. P. Batura
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Mikhail P. Batura - Dr. of Sci. (Tech.), Professor, Head of the Research Laboratory 8.1 “New Learning Technologies”.
220013, Minsk, P. Brovki St., 6
N. A. Volarava
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Natalia A. Volarava - Cand. of Sci., Associate Professor, Head of the Department of Informatics.
220013, Minsk, P. Brovki St., 6
References
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3. Hamilton W. L., Rex Ying, Leskovec J. (2017) Representation Learning on Graphs: Methods and Applications. Stanford, Stanford University. (9), 1–25.
4. Portisch Jan, Heist Nicolas, Paulheim Heiko (2022) Knowledge Graph Embedding for Data Mining VS. Knowledge Graph Embedding for Link Prediction – Two Sides of the Same Coin? Semantic Web. (1), 1–24. DOI: 10.3233/SW-212892
Review
For citations:
Piletski I.I., Batura M.P., Volarava N.A. Methodology for Building a Prototype System for Complex Data Analysis of Thematic Sites. Doklady BGUIR. 2023;21(4):101-109. (In Russ.) https://doi.org/10.35596/1729-7648-2023-21-4-101-109
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