Scientific support systems based on the constructing user profile

This article provides an overview of modern scientific support systems (SSS), which increases the effectiveness of scientific activity of specialists due to timely receipt of relevant publications. Special attention is paid to the application of SSS in rapidly changing subject areas, in particular Computer Science. The main approaches to the creation of SSS are outlined, it is noted that the key direction of the development of SSS is the personalization of information search and analysis. One of the main ways to implement a personal approach is to build a user profile (PP). Various technologies are used for this purpose. This paper shows the advantages of constructing PP based on keywords (KW) — unigrams, bigrams, trigrams and n-grams. This allows to create a reliable «information portrait» of the text, to obtain a compressed semantic core for it. KW describe short documents most effectively and are well suited for analyzing bibliographic descriptions of scientific articles that include titles, annotations (and other supporting sections). The paper makes a formal statement of the task of extracting KW from scientific documents, systematizes methods for detecting KW, and justifies the choice of the neural network model KeyBERT for practical use in SSS. The high versatility of the KeyBERT and its effectiveness for detecting KW in short scientific texts are noted. An algorithm implementing KeyBERT is given (embedding vectors are based on the RoBERTa model). To demonstrate the capabilities of the developed SSS (in particular, the construction and updating), research was conducted on a sample of «UIT-2024», formed at the Department of Control and Intelligent Technologies of the Moscow Power Engineering Institute. It includes 10,000 documents on ten topics in the field of Computer Science. After additional learning, the KeyBERT algorithm extracts informative KW from this sample and automatically generates an expanded list of one-word and multi-word descriptors that allow the specialists to form and update their profiles, increasing the relevance of recommendations in the SSS. The possibilities of the proposed approach are considered in detail using the example of a hypothetical specialist in Computer Science.

1. Kamshilova O. N. Small forms of scientific text: keywords and abstract (informational aspect) / Izv. Ross. Gos. Ped. Univ. im. A. I. Gertsena. 2013. No. 156. P. 106 – 117 [in Russian].

2. Sheremetyeva S. O. Methods and models of automatic keyword extraction / Vestn. Yuzh.-Ural. Gos. Univ. 2015. Vol. 12. No. 1. P. 76 – 81 [in Russian].

3. Scientometry and expertise in science management / Edited by D. A. Novikov, A. I. Orlov, P. Yu. Chebotarev: Coll. of works. Spec. issue 44. — Moscow: IPU RAN, 2013. — 568 p. [in Russian].

4. Orlov A. I. Modern problems of science and scientometry / Biocosmology — neo-Aristotelism. 2017. Vol. 7. Nos. 3 – 4. P. 389 – 410 [in Russian].

5. Artificial intelligence: The handbook is in three volumes. Vol. 2. Edited by V. N. Zakharov, E. V. Popov, D. A. Pospelov, V. F. Khoroshevsky. — Moscow: Radio i sviaz’. 1990. — 304 p. [in Russian].

6. Salton J. Dynamic library and information systems. — Moscow: Mir, 1979. — 560 p. [Russian translation].

7. Bollacker K. D., Lawrence S., Giles C. L. CiteSeer: an autonomous web agent for automatic retrieval and identification of interesting publications / Proceedings of the 2nd international conference on Autonomous agents. 1998. P. 116 – 123.

8. Cross J. MEDLINE, PubMed, PubMed Central, and the NLM / Editors’ Bulletin. 2006. No. 2. DOI: 10.1080/17521740701702115

9. Gündogan E., Kaya M. A novel hybrid paper recommendation system using deep learning / Scientometrics. 2022. Vol. 127. No. 7. P. 3837 – 3855. DOI: 10.1007/s11192-022-04420-8

10. Bai X., Wang M., Lee I., et al. Scientific paper recommendation: a survey / IEEE Access. 2019. Vol. 7. P. 9324 – 9339.

11. Li Z., Zou X. A review on personalized academic paper recommendation / Computer and Information Science. 2019. Vol. 12. No. 1. P. 33 – 43.

12. Kreutz C., Schenkel R. Scientific Paper Recommendation Systems: a Literature Review of recent Publications. 2022. ArXiv: 2201.00682

13. Beel J., Gipp B., Langer S., et al. Introducing Mr. DLib, a Machine-readable Digital Library / Proceedings of the ACM/IEEE Joint Conference on Digital Libraries. 2011. DOI: 10.1145/1998076.1998187

14. Bogers T., Van den Bosch A. Recommending scientific articles using CiteUlike / Proc. ACM Conf. Recommender Syst. 2008. P. 287 – 290.

15. Gingstad K., Jekteberg Y., Balog K. ArXivDigest: A Living Lab for Personalized Scientific Literature Recommendation. 2020. ArXiv: 2009.11576.

16. Beel J., Gipp B., Langer S., Breitinger C. Research-paper recommender systems: a literature survey / International Journal on Digital Libraries. 2015. P. 1 – 34.

17. Fee H., Barth J., Gremm J., et al. Coverage of academic citation databases compared with coverage of scientific social media: Personal publication lists as calibration parameters / Online Information Review. 2015. Vol. 39. P. 255 – 264. DOI: 10.1108/oir-07-2014-0159

18. Gollapalli S. D., Caragea C. Extracting keyphrases from research papers using citation networks / AAAI’14: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. 2014. Vol. 28. No. 1. P. 1629 – 1635.

19. Zhang Ch., Afanasyev G. I. Basic technologies and prospects for the evolution of personalized recommendation systems / E-Scio. 2022. No. 4(67). P. 309 – 320 [in Russian].

20. Zharova M. A., Tsurkov V. I. Neural network approaches for recommendation systems / Izv. RAN. Teor. Sist. Upravl. 2023. No. 6. P. 150 – 165 [in Russian]. DOI: 10.31857/s0002338823060124

21. Liu Y., Ott M., Goyal N., et al. RoBERTa: A robustly optimized BERT pretraining approach. 2019. ArXiv: 1907.11692.

22. Papagiannopoulou E., Tsoumakas G. A review of keyphrase extraction / Wiley Interdisciplinary Reviews — Data Mining and Knowledge Discovery. 2020. Vol. 10. No. 2. DOI: 10.1002/widm.1339

23. Kulkarni M., Mahata D., Arora R., Bhowmik R. Learning Rich Representation of Keyphrases from Text / Findings of the Association for Computational Linguistics. 2022. P. 891 – 906. DOI: 10.18653/v1/2022.findings-naacl.67

24. Liang X., Wu S., Li M., Li Z. Unsupervised keyphrase extraction by jointly modeling local and global context / Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2021. P. 155 – 164. DOI: 10.18653/v1/2021.emnlp-main.14

25. Grootendorst M. KeyBERT: Minimal Keyword Extraction with BERT. 2020. DOI: 10.5281/zenodo.4461265

26. Kozlov P. A., Nazarov N. A., Tolcheev V. O. Personalized support system for scientific activities in small research teams / Collection of materials of the XXVII International scientific and technical conference «Information systems and technologies», R. E. Alekseev Nizhny Novgorod State Technical University. 2021. P. 630 – 635 [in Russian].

27. Kozlov P. A., Mokhov A. S., Nazarov N. A., et al. Comparative analysis of binary classifiers on an array of scientific publications / Industr. Lab. Mater. Diagn. 2022. Vol. 88. No. 7. P. 79 – 87 [in Russian]. DOI: 10.26896/1028-6861-2022-88-7-79-87