Abstract:

An ontological design approach was used to describe the semantics of some boundary value problems in the LibMeta digital library. To describe problems in the LibMeta library, connections of terms and concepts with classical definitions of the mathematical encyclopedia and other primary sources have been established. Establishing links allows you to form a dictionary and thesaurus of the applied subject area of new boundary value problems and place the results in the semantic environment of the digital library. Examples of this approach are demonstrated using the capabilities of the LibMeta semantic library, which contains a digitized version of the mathematical encyclopedia, encyclopedia of mathematical physics, classifiers, and applied mathematical thesauri and dictionaries. New terms from publications, after being added to the content of the library, were reflected with links in the mathematical encyclopedia. The thesaurus for problems in the elasticity theory domain was created for the first time by integrating subject dictionaries, classifiers, metadata of specialized journal publications and encyclopedic content of the LibMeta library. The purpose of such research is to provide the user with additional services in the search for publications in the applied scientific field.

Abstract: Digital library management system LibMeta is presented. It provides a means of library and museum metadata integration. Metadata schema of Digital library management system LibMeta which is based on ENIP schema is exposed. It contains main, library profiles and also profiles to work with museum and media objects. Integration of information from different sources could lead to duplication of resource descriptions. Detailed integration algorithm is given; it helps to avoid duplication of resource descriptions.

Abstract: The main projects that have been implemented in the Computing Center named A.A. Dorodnitsyna of the Russian Academy of Sciences (CC RAS) for the last 20 years, that is, since 1998, are analyzed. One of the first was the implementation of the pilot project “Integrated Information Resource System (ISIR) RAS”. The successful completion of this project allowed the development of work on the integration of heterogeneous scientific information resources into the general academic scientific information system. An important stage was the project of creating the Unified Scientific Information Space (ENIP) of the RAS. This project was based on the subsystem “Scientific Institute of the Russian Academy of Sciences”, created at the CC of the Russian Academy of Sciences and the Center for Scientific Telecommunications (CNTK) of the Russian Academy of Sciences. Considering the importance of building digital libraries, in 2006 the Russian Academy of Sciences adopted the target scientific program “Creating the Central Bank “Scientific Heritage of Russia”, in accordance with which the digital library was implemented. The created GeoMeta Portal is a standardized and decentralized spatial information management environment designed to access geodatabases, map products and associated metadata from various sources, facilitating the exchange of spatial information between organizations and its sharing via the Internet. Currently, the main line of work is the LibMeta digital personal semantic library. The main task of this system is to provide the user with a unified view for the possibility of automated extraction of information of interest to him on a particular subject area.

Abstract:

This paper examines the development of the SciLibRu library of scientific subject areas, as a continuation of the semantic description of scientific works from the library LibMeta project. This library is based on a conceptual data model, the structure and semantics of which are formed based on the principles of ontological modeling. This approach ensures a strict description of the subject area, formalization of the relationships between entities, and the possibility of further automated data analysis. The goal of the study is to develop and experimentally apply methods for structuring scientific journal data in LaTeX format for their integration into the library ontology and to support semantic search.

An algorithm for translating data represented by multiple files into XML format is proposed for integration into the library ontology. A vector search module based on embedding calculation using language models is implemented. Patterns in the distribution of embeddings and factors influencing the accuracy of search results ranking are identified. Testing of the two components is conducted.

The developed method forms the basis for automatically incorporating scientific journal data into the SciLibRu knowledge graph and creating training corpora for language models limited to scientific subject areas. The obtained results contribute to the development of journal knowledge graph navigation systems, recommendation engines, and intelligent search tools for Russian-language scientific texts.

Abstract:

The work is devoted to the problem of data integration for representing scientific subject areas based on their semantic description in the SciLibRu digital library. The LibMeta library's ontology and knowledge graph are used as the data model. SciLibRu is populated by adding data from scientific journals. The paper demonstrates how the stages of processing semi-structured scientific publications for their integration into the library's ontology are implemented. Completing all data preprocessing stages yields a dataset that can be used to train language models for queries in Russian-language scientific subject areas.

Abstract:

The problem of presenting scientific results of academic institute in a digital environment is considered. A new look at the knowledge space of a scientific institute constitutes a natural stage in the development of WEB technologies. The data structure inherent in previous studies allows you to organize search and navigation through them using a knowledge graph, like a version of the semantic library LibMeta. The knowledge graph gives a more complete and high-quality idea of the knowledge space, often removing the cognitive load in the perception of complex structures and data connections.

Abstract:

The problem of finding the most relevant documents as a result of an extended and refined query is considered. For this, a search model and a text preprocessing mechanism are proposed, as well as the joint use of a search engine and a neural network model built on the basis of an index using word2vec algorithms to generate an extended query with synonyms and refine search results based on a selection of similar documents in a digital semantic library. The paper investigates the construction of a vector representation of documents based on paragraphs in relation to the data array of the digital semantic library LibMeta. Each piece of text is labeled. Both the whole document and its separate parts can be marked. The problem of enriching user queries with synonyms was solved, then when building a search model together with word2vec algorithms, an approach of "indexing first, then training" was used to cover more information and give more accurate search results. The model was trained on the basis of the library's mathematical content. Examples of training, extended query and search quality assessment using training and synonyms are given.