Abstract:

We investigated the current models of the publication and dissemination of scientific knowledge. We describe the modern information management system of scientific publications and services that determine their functionality.

We discuss the concept of the digital infrastructure of the electronic scientific journal. Under this infrastructure, we understand the complex that combines management software platform of electronic journal and a number of specialized information systems. The software platform realizes the basic operating log management processes. Information systems provide the operation of additional services, taking into account the specifics of the journal subject area.

We present an approach to the organization of the digital infrastructure of the scientific journal based on an open platform Open Journal Systems (OJS). We provide software services that extend the functionality of this platform and considering specificity of the subject area of scientific journals. We have created software modules for automating of electronic scientific journal workflow. These modules are an extension of OJS.

We present a system of services for the automated processing of collections of scientific documents. These services provide verification of document compliance to the accepted rules of formation of collections and their conversion to the established formats; structural analysis of documents and extraction of metadata, as well as their integration into the scientific information space. The system allows to automatically performing a set of operations that cannot be realized for acceptable time with the traditional manual processing of electronic content. It is designed for the large collections of scientific documents.

Algorithms style validation of texts at the article registration stage in the information system of electronic scientific journal, the selection of reviewers, alert and control the timing of reviewing were automated. Information gathering algorithm with dedicated news lines of scientific journals, further analysis and distribution of news by categories and degrees of importance were developed. The algorithm automatically extract bibliographic data from a homogeneous array of publications (in particular, the issues of the scientific journal) and the formation of metadata blocks for export to international information and analytical system were created. Methods integration of OJS platform and international databases of science citation were developed.

We present methods of processing documents containing mathematical formulas: in the collections of documents that contain mathematical formulas, algorithm for the search formulas is developed; basic ideas, approaches and results already obtained by the mathematical knowledge management based on ontology are presented; a method of constructing recommender systems based on mathematical knowledge ontologies described. The method of primary processing automated of scientific article using TеX-notation developed.

The new direction of researches of scientific communications in the environment of Web 2.0 – altmetrics – is considered. We have analyzed the content of the notion «altmetrics», we conducted a comparison of traditional (bibliometric and scientometric) and alternative indicators. We describe the use of world experience informetric services on scientific journals sites. We discussed options for implementing these approaches to create an electronic scientific journal management platform. 

Abstract: We investigated the current models of the publication and dissemination of scientific knowledge. We also presented different approaches to the organization of the personal information space of scientist. We describe the modern information management system of scientific publications and services that determine their functionality. We have created software modules for automating of electronic scientific journal workflow. These modules are an extension of Open Journal Systems. We describe the architecture of a universal information management system of electronic scientific journals and management platform of “Russian Digital Libraries Journal”.

Abstract: A number of problems related to the construction of the metadata factory of the digital mathematical library Lobachevskii-DML have been solved. By metadata factory we mean a system of interconnected software tools aimed at creating, processing, storing and managing metadata of digital library objects and allowing integrating created electronic collections into aggregating digital scientific libraries. In order to select the optimal such software tools from existing ones and their modernization:we discussed the features of the presentation of the metadata of documents of various electronic collections related both to the formats used and to changes in the composition and completeness of the set of metadata throughout the entire publication of the corresponding scientific journal;we presented and characterized software tools for managing scientific content and methods for organizing automated integration of repositories of mathematical documents with other information systems;we discussed such an important function of the digital library metadata factory as the normalization of metadata in accordance with the formats of other aggregating libraries.As a result of the development of the metadata factory of the digital mathematical library Lobachevskii-DML, we proposed a system of services for the automated generation of metadata for electronic mathematical collections; we have developed an xml metadata presentation language based on the Journal Archiving and Interchange Tag Suite (NISO JATS); we have created software tools for normalizing metadata of electronic collections of scientific documents in formats developed by international organizations – aggregators of resources in mathematics and Computer Science; we have developed an algorithm for converting metadata to oai_dc format and generating the archive structure for import into DSpace digital storage; we have proposed and implemented methods for integrating electronic mathematical collections of Kazan University into domestic and foreign digital mathematical libraries.

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:

Methods for creating digital mathematical collections that include unstructured sets of documents are presented. These sets contain materials from scientific conferences, as well as articles from the archives of mathematical journals of the "pre-digital" period.

Using the software tools of the metadata factory of the digital mathematical library Lobachevskii DML, a mandatory set of metadata for digital collection documents was formed. To refine and replenish the metadata sets, knowledge extraction methods from Wikidata were used.

To search Wikidata for information about digital collection documents and their authors, a system of SPARQL queries has been developed. A set of Wikidata entities is defined, which determine the features of the search, as well as the subsequent filtering of the results.

Methods for clarifying and supplementing the bibliographic references given in the articles are proposed. When forming the metadata of documents of retrocollections, a search was made in Wikidata for information about the years of life of the authors of articles, as well as URLs of web pages with information about articles and their authors. The results of the formation of several new digital collections of the Lobachevskii-DML digital library are presented.

Abstract: The technology of automated construction of the subject domain ontology, based on information extracted from the comments of the TATNEFT oil company relational databases, is considered. The technology is based on building a converter (compiler) translating the logical data model of Epicenter Petrotechnical Open Software Corporation (POSC), presented in the form of ER diagrams and a set of the EXPRESS object-oriented language descriptions, into the OWL ontology description language, recommended by the W3C consortium. The basic syntactic and semantic aspects of the transformation are described.

Abstract:

This paper examines the application of an ontological approach to constructing a dataset for evaluating and comparing context enrichment systems for large language models using knowledge graphs in the domain of mechanical product lifecycle management systems. In this domain, obtaining the required amount of textual data with a formal logical structure to form an evaluation set without using generated synthetic data is challenging. To avoid introducing distortions and hallucinations when generating evaluation data, a novel solution to the data deficiency is proposed. This solution involves extracting ontology directly from product and assembly files compliant with the STandard for Exchange of Product Model Data. This potentially enables the use of all product data as a source for scaling evaluation data. The goal of this paper is to create a dataset of structured textual data in the domain of mechanical product lifecycle management systems, develop an evaluation methodology, and implement context enrichment pipelines for large language models with and without knowledge graphs to analyze the contribution of data-structure-extracting systems to the quality of generated responses. In this paper: a new source of evaluation data is proposed, a new methodology for generating text evaluation data while preserving the logical structure is developed, a pipeline for using the generated evaluation data is implemented, and evaluation results are obtained that confirm the positive contribution of systems with the extraction of structured data to the quality of generated responses in the domain of mechanical product lifecycle management systems.

Abstract: Methods of the semantic documents parsing in digital control system of scientific collections, including electronic journals, offered. The methods of processing documents containing mathematical formulas and methods for the conversion of documents from the OpenXML-format in ТеХ-format considered. The search algorithm for the mathematical formulas in the collections of documents stored in OpenXML-format designed. The algorithm is implemented as online-service on platform science.tatarstan.

Abstract:

The problem of effective navigation and search for relevant information within growing volumes of scientific publications necessitates a shift from classical full-text search methods to semantic models. This work proposes an approach to structuring a heterogeneous corpus of scientific texts by constructing a Knowledge Graph (KG). A data processing pipeline is developed that encompasses the extraction of metadata, keywords, and structural elements of articles, followed by their integration into a unified graph. Based on the constructed Knowledge Graph, methods for analyzing explicit connections and extracting implicit connections between publications are implemented. The research results demonstrate the effectiveness of the graph-based representation of scientific information for uncovering hidden patterns within subject domains and supporting intelligent navigation.

Abstract:

The article examines current challenges in scientometrics arising from the surge in publication activity and the widespread adoption of generative artificial intelligence. The existing scientometric toolkit for analyzing research activity is reviewed, categorized into quantitative metrics and science mapping methods (citation network analysis, academic genealogy, semantic analysis, etc.). An attempt is made to overcome the limitations of traditional citation analysis, such as “semantic blindness” and vulnerability to manipulation. As a potential solution, a conceptual model is proposed where the unit of analysis shifts from the publication as a whole to an individual “key statement”. This approach involves recording not only the statement’s content but also its type, area of relevance, and its logical relationship with other claims (confirmation, refutation, clarification, generalization, etc.). Within this framework, principles for calculating modified scientometric metrics are introduced.

The proposed model was tested on a corpus of 728 articles from the Russian  journal Informatics and Education (2016–2025). An analysis conducted using large language models revealed that retrospective extraction of statements faces significant hurdles due to established cultures of scientific communication. Consequently, the study highlights the advantages of having authors formulate key statements themselves as a distinct type of metadata. In conclusion, the paper outlines development paths for the concept of an “embedding space of knowledge,” which could eventually complement existing approaches to analyzing the evolution of scientific ideas and theories.

Abstract: This article considers approach to intelligent search of complex objects in different types of texts with structural markup which can be used for Big Data processing. We research two types of data entry: relational databases, which use their schemes as structural markup, and full-text scientific documents containing mathematical expressions (formulae). For such full-text documents we suggest additory automated markup to allow formula search. In both cases we use natural language texts, which are semistructured data, as data source for building ontology and conducting search at a later stage. For relational databases those are comments to table and table attribute names; for scientific documents (articles, monographs, etc.) it is a text content of marked up documents.

Abstract: This paper describes the morphological disambiguation methods and their application for the Tatar language. The state-of-the art technology is discussed. We analyze the contextual and statistical methods and their evaluations for different languages.