Abstract: The task of forming a digital space of scientific knowledge (DSSK) is analyzed in the paper. The difference of this concept from the general concept of the information space is considered. DSSK is presented as a set containing objects verified by the world scientific community. The form of a structured representation of the digital knowledge space is a semantic network, the basic organization principle of which is based on the classification system of objects and the subsequent construction of their hierarchy, in particular, according to the principle of inheritance. The classification of the objects that make up the content of the DSSK is introduced. A model of the central data collection system is proposed as a collection of disjoint sets containing digital images of real objects and their characteristics, which ensure the selection and visualization of objects in accordance with multi-aspect user requests. The concept of a user collection is defined, and a hierarchical classification of types of user collections is proposed. The use of the concepts of set theory in the construction of DSSK allows you to break down information into levels of detail and formalize the algorithms for processing user queries, which is illustrated by specific examples.

Abstract: An approach is proposed for organizing expert evaluation of a scientific document submitted to a mathematical journal. Domain restriction is associated with the use of the Mathematical Sciences Classification System – MSC. A recommendation system is presented that allows you to create a list of possible experts for conducting scientific peer-reviewing on a mathematical article. The recommender system uses the MSC codes presented by the author of the article on the MSC2020 classifiers. If the codes MSC2000 or MSC2010 are indicated in the article, they are automatically converted to codes MSC2020. For each expert, the system supports a personal profile that contains a set of codes MSC2020, supplemented by numerical characteristics – weights calculated for each code in accordance with the system of accounting for competencies, preferences or refusals to participate in the review procedure. This set is automatically edited if the expert is included in the list of possible reviewers – the weights of several codes increase or decrease, as well as new codes are added. The recommendation system is implemented as an integrated tool (plug-in) of the Open Journal Systems (OJS) platform. The developed method has been tested in the information system of the Lobachevskii Journal of Mathematics (https://ljm.kpfu.ru).

Abstract:

Subject ontology in the context of this article is understood as a set of key concepts related to a certain field of science, with their semantic connections, supplemented by indexes of various classification systems describing this scientific field. Subject ontologies are a necessary component of each subspace that is part of the Unified digital space of scientific knowledge (DSSK). This article presents the results of research related to the construction of subject ontologies based on the created automated system for supporting terminological dictionaries and suggests a methodology for identifying new key terms in a particular field of science. The proposed methodology is based on the use of existing classification systems in conjunction with citation databases, such as Web of Science and Scopus for English–language publications and the Russian citation index for Russian-language publications. The methodology involves dividing the scientific field into a number of sections in accordance with the selected classification system, extracting from the CSB the core of articles related to each section, and from the articles - new author's keywords, which should constitute, in combination with the corresponding sections of classification systems, the basis of the subject ontologies of this scientific field.

Abstract:

The study examines the principle of creating virtual exhibitions as a means of integration into the Common Digital Space of Scientific Knowledge (CDSSK), information systems in the field of science and culture, with the aim of promoting science, ensuring access to information in various scientific fields, and drawing attention to current issues and achievements in the scientific sphere. The main methods of creating virtual exhibitions are formulated, including content selection and segmentation into main sections. In addition, a classification of virtual exhibitions into autonomous, remote, and combined is proposed. Special attention is paid to the methodology of creating virtual exhibition at the Moscow Center of the Russian Academy of Sciences. Using the example of an interdepartmental combined virtual exhibition, a detailed description of the "Mrs. Penicillin" exhibition dedicated to the creator of penicillin, Z.V. Ermolyeva, is provided.

Abstract:

The results on the creation of methods for managing mathematical knowledge in the context of digital mathematical libraries are presented. The software tools developed on the basis of these methods are part of the OntoMath digital ecosystem, within which they interact. A brief description of the architecture of the OntoMath ecosystem is given, the levels of subject ontologies and external ontologies are highlighted, as well as the level of software tools and services. Semantic services are separated into a separate category. This term denotes software tools, in the functionality of which queries to subject ontologies are used to ensure the management of knowledge objects. General descriptions of developed subject ontologies are given: educational mathematical ontology OntoMath^Edu and ontology of professional mathematics OntoMath^PRO. The development of educational ontology is reflected in the direction of including educational prerequisite links between classes. Among the software tools of the digital ecosystem, search services for mathematical electronic collections, a service for semantic annotation of mathematical documents, tools for semantic marking of educational mathematical documents, as well as a system for automatically generating testing tests in mathematical educational disciplines are highlighted. As part of the OntoMath digital ecosystem, special-purpose recommender systems are being developed. The current version of the ecosystem includes a recommender system for generating a list of related articles based on the OntoMath^PRO ontology, a recommender system for appointing experts to support the scientific review process, and recommender systems for selecting subject classifiers UDC and Mathematics Subject Classification codes for mathematical documents. The results are also presented in the direction of creating a digital library metadata factory, which includes services and tools for extracting, refining, replenishing and normalizing the metadata of electronic mathematical collections. Note that the OntoMath ecosystem is being developed as the technological basis for the Lobachevskii Digital Mathematical Library.

Abstract: Some scientific digital libraries provide scientometric services which collect different statistics including numbers of viewing and downloads over its content, etc. Sometimes statistics about publications are aggregated for its authors and at the next step for organizations of authors' affiliation. In this paper we present a scientometric service that makes an aggregation of publications' statistics for codes of scientific classification systems (rubricator) included in the publications metadata. This application works as a part of the Socionet Statistic system.

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.

Abstract:

Classification of documents with the assignment of classifier codes is a traditional way of systematizing and searching for documents on a specific topic. The Universal Decimal Classification (UDC) underlies the systematization of knowledge presented in libraries, databases and other information repositories. In Russia, UDC is an obligatory attribute of all book production and information on natural and technical sciences. The choice of classification codes is associated with the analysis of the structure of the classifier tree and is traditionally decided by the author of a scientific article. This article proposes a solution for automating the assigning the UDC classification code for a mathematical article based on a special resource – the OntoMath^PRO ontology for professional mathematics, developed at Kazan Federal University. An approach to solving the problem is to create "code maps" for each classifying code in the UDC tree in the field of mathematics. Under the "code map" is meant a weighted set of all extracted, with the help of OntoMath^PRO ontology, mathematical named entities from the collection of articles with a given UDC code. The creation of "code maps" is based on the hypothesis that the choice of the UDC code is determined by a certain set of classifying features that can be represented by classes from the OntoMath^PRO ontology. The proposed hypothesis was tested and confirmed in the paper. The hypothesis was tested on a collection of mathematical articles An approach to solving the problem is to create "code maps" for each classifying code in the UDC tree in the field of mathematics. Under the "code map" is meant a weighted set of all extracted, with the help of OntoMath^PRO ontology, mathematical named entities from the collection of articles with a given UDC code. The creation of "code maps" is based on the hypothesis that the choice of the UDC code is determined by a certain set of classifying features that can be represented by classes from the OntoMath^PRO ontology. The proposed hypothesis was tested and confirmed in the paper.  The hypothesis was tested on a collection of mathematical articles published during 1999-2009 in the "Izvestiya VUZov. Mathematics" journal. 

Abstract:

The use of modern methods of thematic analysis for the analytical processing of information is currently used in almost all areas of human activity, including scientometrics. Many scientometric and citation systems, including the world famous WoS, Scopus, Google Shcolar, develop thematic categories for searching and processing information. Most important tasks that can be solved using thematic classification methods are: assessment of the dynamics of the development of thematic areas in the organization, country and in world science; search for articles on a given topic; search and assessment of the authority of experts; search for journal for publication and other relevant tasks. The Lomonosov Moscow State University is currently developing and using the system ISTINA. In this project, algorithms have been created that solve some of the problems listed. Scientific research is underway to create new effective mathematical models and algorithms in this area.