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Formation of Structured Representations of Scientific Journals for Integration into a Knowledge Graph and Semantic Search

https://doi.org/10.26907/1562-5419-2025-28-6-1306-1323

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.

About the Authors

Olga Muratovna Ataeva
Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences
Russian Federation


Mikhail Gennadievich Kobuk
S. Witte University of Moscow
Russian Federation


References

1. Hoftich M. TEX4ht: LATEX to Web Publishing // TUGboat. 2019. Vol. 40, No. 1. P. 76–81.

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


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For citations:


Ataeva O.M., Kobuk M.G. Formation of Structured Representations of Scientific Journals for Integration into a Knowledge Graph and Semantic Search. Russian Digital Libraries Journal. 2025;28(6):1306-1323. (In Russ.) https://doi.org/10.26907/1562-5419-2025-28-6-1306-1323

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