Formation of Structured Representations of Scientific Journals for Integration into a Knowledge Graph and Semantic Search
Main Article Content
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.
Article Details
References
2. Frankston C. et al. Using HTML Papers on arXiv: Why It’s Important, and How We Made It Happen // arXiv preprint 2024. https://doi.org/10.48550/arXiv.2402.08954 (In Russ.)
3. Serebryakov V.A., Galochkin M.P., Gonchar D.R., Furugyan M.G. Theory and Implementation of Programming Languages. 2nd ed. Moscow: MZ-Press, 2006. 352 p. (In Russ.)
4. Hopcroft J., Motwani R., Ullman J. Introduction to Automata Theory, Languages, and Computation. Moscow: Williams, 2002. 528 p. (In Russ.)
5. Aho A.V., Lam M.S., Sethi R., Ullman J.D. Compilers: Principles, Techniques, and Tools. 2nd ed. Moscow: Williams, 2008. 1184 p. (In Russ.)
6. Mikolov T., Sutskever I., Chen K., Corrado G., Dean J. Distributed Representations of Words and Phrases and their Compositionality // Advances in Neural Information Processing Systems (NIPS 26). 2013. P. 3111–3119. URL: https://dl.acm.org/doi/10.5555/2999792.2999959 (date accessed: 08.11.2025)
7. Pennington J., Socher R., Manning C. GloVe: Global Vectors for Word Representation // Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014. P. 1532–1543. https://doi.org/10.3115/v1/D14-1162
8. Joulin A., Grave E., Bojanowski P., Mikolov T. Bag of Tricks for Efficient Text Classification // Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Valencia, Spain, April 2017. P. 427–431. https://doi.org/10.18653/v1/E17-2068
9. Feng F., Yang Y., Cer D., Arivazhagan N., Wang W. Language-agnostic BERT Sentence Embedding // Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL). Dublin, Ireland, May 2022. Р. 878–891. https://doi.org/10.18653/v1/2022.acl-long.62
10. Zmitrovich D. et al. A Family of Pretrained Transformer Language Models for Russian // arXiv preprint 2023. https://doi.org/10.48550/arXiv.2309.10931
11. Kuratov Y., Arkhipov M. Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language // arXiv preprint 2019. https://doi.org/10.48550/arXiv.1905.07213
12. Nikolich A., Puchkova A. Fine-tuning GPT-3 for Russian Text Summarization // arXiv preprint 2021. https://doi.org/10.48550/arXiv.2108.03502
13. Kutuzov A., Kuzmenko E. WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models // In: Ignatov D. et al. (Eds.) Analysis of Images, Social Networks and Texts (AIST 2016). Communications in Computer and Information Science. Vol. 661. Springer, Cham, 2017. https://doi.org/10.1007/978-3-319-52920-2_15
14. Kasenchak R.T. What is Semantic Search? and Why Is It Important? // Information Services and Use. 2019. Vol. 39. No. 3. Р. 205–213. https://doi.org/10.3233/ISU-190045
15. Shelke P. et al. A Systematic and Comparative Analysis of Semantic Search Algorithms // International Journal on Recent and Innovation Trends in Computing and Communication. 2023. Vol. 11, No. 11s. P. 222–229. https://doi.org/10.17762/ijritcc.v11i11s.8094
16. Weckmüller D., Dunkel A., Burghardt D. Embedding-Based Multilingual Semantic Search for Geo-Textual Data in Urban Studies // Journal of Geovisualization and Spatial Analysis. 2025. Vol. 9. No. 31. P. 1–18. https://doi.org/10.1007/s41651-025-00232-5
17. Siddharth Pratap Singh. Vector Search in the Era of Semantic Understanding: A Comprehensive Review of Applications and Implementations // International Journal of Computer Engineering and Technology. 2024. Vol. 15. No. 6. P. 1794–1805. https://doi.org/10.34218/IJCET_15_06_153
18. Zhou Y. et al. Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words // 2022. https://doi.org/10.48550/arXiv.2205.05092
19. Healy J., McInnes L. Uniform manifold approximation and projection // Nature Reviews Methods Primers. 2024, Vol. 4. No. 82. P. 1–15. https://doi.org/10.1038/s43586-024-00363-x
This work is licensed under a Creative Commons Attribution 4.0 International License.
Presenting an article for publication in the Russian Digital Libraries Journal (RDLJ), the authors automatically give consent to grant a limited license to use the materials of the Kazan (Volga) Federal University (KFU) (of course, only if the article is accepted for publication). This means that KFU has the right to publish an article in the next issue of the journal (on the website or in printed form), as well as to reprint this article in the archives of RDLJ CDs or to include in a particular information system or database, produced by KFU.
All copyrighted materials are placed in RDLJ with the consent of the authors. In the event that any of the authors have objected to its publication of materials on this site, the material can be removed, subject to notification to the Editor in writing.
Documents published in RDLJ are protected by copyright and all rights are reserved by the authors. Authors independently monitor compliance with their rights to reproduce or translate their papers published in the journal. If the material is published in RDLJ, reprinted with permission by another publisher or translated into another language, a reference to the original publication.
By submitting an article for publication in RDLJ, authors should take into account that the publication on the Internet, on the one hand, provide unique opportunities for access to their content, but on the other hand, are a new form of information exchange in the global information society where authors and publishers is not always provided with protection against unauthorized copying or other use of materials protected by copyright.
RDLJ is copyrighted. When using materials from the log must indicate the URL: index.phtml page = elbib / rus / journal?. Any change, addition or editing of the author's text are not allowed. Copying individual fragments of articles from the journal is allowed for distribute, remix, adapt, and build upon article, even commercially, as long as they credit that article for the original creation.
Request for the right to reproduce or use any of the materials published in RDLJ should be addressed to the Editor-in-Chief A.M. Elizarov at the following address: amelizarov@gmail.com.
The publishers of RDLJ is not responsible for the view, set out in the published opinion articles.
We suggest the authors of articles downloaded from this page, sign it and send it to the journal publisher's address by e-mail scan copyright agreements on the transfer of non-exclusive rights to use the work.