ellibsЭлектронные библиотекиRussian Digital Libraries Journal1562-5419Казанский (Приволжский) федеральный университет10.26907/1562-5419-2020-23-4-808-834ellibs-230Research ArticleСтатьиМетоды и алгоритмы повышения выразительности связанных данных (обзор)Methods and Algorithms for Increasing Linked Data Expressiveness (Overview)НевзороваО. А.NevzorovaO. A.onevzoro@gmail.comКазанский федеральный университетKazan Federal University202028082020234808834Copyright © Невзорова О.А., 20202020Невзорова О.А.Nevzorova O.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ellibs.elpub.ru/jour/article/view/230

В обзорной статье рассмотрены методы и алгоритмы повышения выразительности связанных данных, подготовленных для публикации в Вебе. Представлены основные подходы к обогащению онтологий, описаны методы, на которых они базируются, а также приведен инструментарий, реализующий эти подходы и инструменты применения соответствующих методов.Основным этапом в общей схеме жизненного цикла данных в облаке открытых связанных данных является этап построения набора связанных RDF-триплетов. Для улучшения классификации данных и анализа их качества применяются различные методы повышения выразительности связанных данных. Основные идеи рассматриваемых методов связаны с обогащением существующих онтологий (расширением базовой схемы знаний) путем добавления или совершенствования терминологических аксиом. Методы обогащения опираются на методы, применяемые в различных областях, таких как представление знаний, машинное обучение, статистика, обработка текстов на естественном языке, анализ формальных понятий и теория игр.

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.

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The authors declare that there are no conflicts of interest present.