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АНАЛИЗ НЕПОЛНЫХ ДАННЫХ В ЗАДАЧАХ ПОСТРОЕНИЯ ФОРМАЛЬНЫХ ОНТОЛОГИЙ

АНАЛИЗ НЕПОЛНЫХ ДАННЫХ В ЗАДАЧАХ ПОСТРОЕНИЯ ФОРМАЛЬНЫХ ОНТОЛОГИЙ

Abstract

The article considers the problem of automating the formation of ontological specifications subject domains on the basis of measurements. This problem is the pivotal issue of ontological analysis. The article presents models and methods, aimed at identifying conceptual structure and, ultimately, detecting the formal ontology of the considered subject domain. Fundamental realities of accumulation of empirical data (multiple independent measurements for each training sample properties of the object; congruence of the measurement procedures; differentiation of trust to different data sources) are reflected in the "objects-properties" summary table model. Imperfection (inaccuracy, inconsistency, uncertainty) of this information implies the need to use many-valued logic models for its primary processing. The result of this treatment fuzzy formal context should be approximated as a unique context, from which the possible conclusion of formal concepts in the framework of applied branch of the lattice theory, known as the "formal concept analysis". The genesis of the "properties’ limits of existence" that affect the correctness of the approximation of fuzzy formal context is studied. The models and the method of accounting for this additional information are proposed. Guidelines for conversion of lattice formal concepts into a formal ontology are formulated. A model example of the developed models and ontological data analysis methods is presented.

В статье рассматривается проблема автоматизации формирования онтологических спецификаций предметных областей на основе измерений стержневая проблема онтологического анализа данных. Представлены модели и методы, направленные на выявление понятийной структуры и, в конечном счете, формальной онтологии исследуемой предметной области. Фундаментальные реалии накопления эмпирической информации: многократные независимые измерения каждого свойства объекта обучающей выборки; конгруэнтность части процедур измерения; дифференциация доверия к различным источникам данных отражены в модели обобщенной таблицы «объекты-свойства». Неполнота (неточность, противоречивость, неопределенность) этой информации влечет необходимость использования для её первичной обработки моделей многозначной логики. Результат такой обработки нестрогий формальный контекст должен быть аппроксимирован однозначным контекстом, из которого возможен вывод формальных понятий в рамках прикладной ветви теории решёток, известной как «анализ формальных понятий». Исследуется генезис «ограничений существования свойств», которые влияют на корректность аппроксимации нестрогого формального контекста. Предлагаются модели и метод учета этой дополнительной информации. Формулируются прагматически обоснованные принципы преобразования решетки формальных понятий в формальную онтологию. Приводится модельный пример использования разработанных моделей и методов онтологического анализа данных.

Keywords

ОНТОЛОГИЧЕСКИЙ АНАЛИЗ ДАННЫХ,ФОРМАЛЬНАЯ ОНТОЛОГИЯ,АНАЛИЗ ФОРМАЛЬНЫХ ПОНЯТИЙ,МНОГОЗНАЧНАЯ ВЕКТОРНАЯ ЛОГИКА,ОГРАНИЧЕНИЯ СУЩЕСТВОВАНИЯ СВОЙСТВ,ONTOLOGICAL DATA ANALYSIS,FORMAL ONTOLOGY,FORMAL CONCEPT ANALYSIS,MULTI-VALUED VECTOR LOGIC,PROPERTIES EXISTENCE CONSTRAINTS

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold