No abstract

The problems of increasing the value of scientific classifiers for the systematization of scientific information in the digital age are discussed, for example, the classification of documents (assignment of indices-classifiers) is a traditional way of systematization of knowledge and information search. In this paper we propose a recommendation system for automated selection of Universal decimal classification (UDC) indices for physical and mathematical documents. This system implements one of the services of the digital mathematical library Lobachevskii-DML. The proposed algorithm is based on the use of terms extracted from the title, the list of keywords and annotations given in the analyzed documents. Extraction of terms from the documents of the collection is carried out with the help of software tools developed by us, taking into account the stylistic features of the documents and the positions in the text of the required terms. The data obtained were included in a dictionary that has an inverted index structure. The generated dictionary contains both classification features and sets of key terms, which are used to systematize and classify the material. Most of these terms were obtained by automated processing of the collection of archives of physical and mathematical publications of the All-Russian mathematical portal Math-Net.Ru. The proposed variant of the semantic markup of a table of indices of the Universal decimal classification. The model of classification of scientific documents is described.

This paper contains the review of modern scientometric databases. The specificity of the representation of scientific materials in them is highlighted. The integration methods based on automation of the process of creating metadata for documents, included in digital scientific collections, are presented. Features of the formation of metadata for international scientometric databases on mathematical and computer sciences are noted. An algorithm for the automated formation of metadata in the format of the Russian scientific citation index (RSCI) is given. To automatically parse the text of articles, several regular expression patterns have been created, with the help of which the main metadata groups were selected. The algorithm is implemented as a service, consisting of modules for analyzing the structure of documents, automatically selecting documents according to the established order (for example, lexicographic), extracting the annotation block, the alphabetical index generating module, creating a bibliographic description of the article for writing headers of this article, converting documents to the portable document format (pdf), according to the determined parameters. The final module is the formation of metadata for exports to the RSCI. Approbation of the algorithm for the collection of articles of the journal "Russian Digital Libraries" was noted. The service for the formation of metadata for the documents of the digital collection Lobachevskii DML, made in accordance with the diagrams of the fundamental metadata of the European Digital Mathematical Library (EuDML) and the bibliographic database DBLP, is presented. Templates for showing metadata of articles of digital collections Lobachevskii DML in accordance with the scheme NISO JATS V1.0 are prepared. Plugins for the Open Journal System, allowing generation of metadata for science-based databases for downloadable articles are developed.

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).