Methods and Algorithms for Increasing Linked Data Expressiveness (Overview)

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Published: 28.05.2020
UDC 004.822 004.021
DOI: https://doi.org/10.26907/1562-5419-2020-23-4-808-834
Issue
Vol. 23 No. 4 (2020): Special issue «Scientific Services & Internet». Part 2

Main Article Content

Olga Avenirovna Nevzorova
Kazan Federal University
https://orcid.org/0000-0001-8116-9446

Abstract

This review discusses methods and algorithms for increasing linked data expressiveness which are prepared for Web publication. The main approaches to the enrichment of ontologies are considered, the methods on which they are based and the tools for implementing the corresponding methods are described.The main stage in the general scheme of the related data life cycle in a cloud of Linked Open Data is the stage of building a set of related RDF- triples. To improve the classification of data and the analysis of their quality, various methods are used to increase the expressiveness of related data. The main ideas of these methods are concerned with the enrichment of existing ontologies (an expansion of the basic scheme of knowledge) by adding or improving terminological axioms. Enrichment methods are based on methods used in various fields, such as knowledge representation, machine learning, statistics, natural language processing, analysis of formal concepts, and game theory.

Article Details

Author Biography

Olga Avenirovna Nevzorova

Kazan Federal University, Institute of Computer Mathematics and Information Technologies, Associated Professor the Department of Information Systems, PhD. Major Fields of Scientific Research: Natural language processing, Artificial intelligence.

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