The sentiment-analysis algorithm of social networks text resources based on ontology

In this paper the features of semantic and sentiment analysis of textual data of social networks are presented, and an original model and algorithm for sentiment analysis of textual fragments of social networks using fuzzy linguistic ontology are proposed. This approach involves the use of various subgraphs of fuzzy ontology when considering texts of various subject areas with regard to contexts. In addition, the algorithm involves the assessment of the sentiment scores of individual syntagmatic structures into which the analyzed text fragments are divided. It also presents the results of experiments comparing the efficiency of the developed algorithm with a group of existing approaches in analyzing text fragments on the example of data from the social network VKontakte

Development of a fuzzy knowledge base based on context analysis of problem area

The article describes the process of developing a fuzzy knowledge base (KB). The content of fuzzy KB is formed as a result of the analysis of the contexts of the problem area (PrA). In this case, the context is a certain "point of view" on the PrA and its features. A graph database (DB) is used as the basis for storing the contents of the KB in the form of an applied ontology. An attempt is made to implement the mechanism of inference by the contents of a graph database. The mechanism is used to dynamically generate the screen forms of the user interface to simplify the work with the KB.This work was financially supported by the Russian Foundation for Basic Research (Grant No. 16-47- 732054)

The applying of syntagmatic patterns for the development of question-answer systems

Основная статьяQuestion-answer (QA) systems are systems that can take questions and respond to them in a natural language. In most cases, the principles of building question-answer systems are used in the development of decision support systems. The mechanism of syntagmatic patterns is used when processing open-ended questions and when extracting answers to it from semi-structured resources. This article describes the application of the mechanisms of syntagmatic patterns in the construction of various types of QA-systems and expert systems.This paper has been approved within the framework of the federal target project “R&D for Priority Areas of the Russian Science-and-Technology Complex Development for 2014-2020”, government contract No 14.607.21.0164 on the subject “The development of architecture, methods and models to build software and hardware complex semantic analysis of semi-structured information resources on the Russian element base” (Application Code «2016-14-579-0009-0687»)

Construction of ontology of problem area based on the syntagmatic analysis of text documents

The activities of any large organization requires the work of specialists with a large volume of unstructured information to obtain and extract the necessary knowledge to interact with partners, decision-making and so on. An array of unstructured textual information is not adapted to the structuring and semantic search. Thus, development intelligent algorithms and text analysis methods to dynamically generate the contents of a knowledge base is needed. Extract of syntagmatic structure of the text and further representation of extracted knowledge in the form of a single unified ontology allows you to access the knowledge base for solving complex problems.This paper has been approved within the framework of the federal target project “R&D for Priority Areas of the Russian Science-and-Technology Complex Development for 2014-2020”, government contract No 14.607.21.0164 on the subject “The development of architecture, methods and models to build software and hardware complex semantic analysis of semi-structured information resources on the Russian element base” (Application Code « 2016-14-579-0009-0687 »)

The phonetic composition of the recognized speech recovery using lexical ontology

Улучшение качества распознавания речи в условиях шума можно реализовать, адаптировав алгоритм восстановления распознанного слова к особенностям использования и особенностям предметной области. В статье описан подход к восстановлению фонетической композиции распознанного слова с использованием лексических онтологий. Лексическая онтология содержит лексические отношения между членами предметной области и фонетическим составом каждого из этих слов в терминах таблицы SAMPA + для русского языка. Improving the quality of speech recognition in conditions of noisiness can be realized by adapting the algorithm for reconstructing the recognized word to the features of usage and the features of the subject area.The article describes an approach to reconstructing the phonetic composition of a recognized word by using lexical ontologies. The lexical ontology contains lexical relations between terms of the subject domain and the phonetic composition of each of these words in terms of the SAMPA + table for the Russian language.Работа выполнена при финансовой поддержке РФФИ. Проекты No 16-48-732046 и No16-48-730305

Hybridization of fuzzy time series and fuzzy ontologies in the diagnosis of complex technical systems

В рамках данной работы была исследована методика построения нечетких онтологий и была разработана онтологическая модель состояния вертолетных агрегатов. В процессе интеграции нечетких временных рядов и нечеткой онтологии был реализован метод интегрирующий ВР и онтологию и разработан программный продукт, обеспечивающий интеграцию. Помимо этого, были проведены эксперименты по поиску аномальных ситуаций и поиску возможных неисправных агрегатов с использованием разработанного подхода к интеграции нечетких временных рядов и нечеткой онтологии. A method for integrating fuzzy time series and fuzzy ontology was implemented and a software product was developed that provides integration. An ontological model of the state of helicopter units was also developed. In addition, experiments were conducted to search for anomalous situations and to search for possible faulty units using the developed approach.Работа выполнена при финансовой поддержке РФФИ. Проекты No 18-37-00450, No 19-07-00999 и 18-47-732007