Processing of Scientific and Technical Information in Interdisciplinary Research by Methods of Mathematical and Linguistic Directed Search by the Example of the Study of Biomaterials for Tissue Engineering

E. Antonov, A. Artamonov, A. Orlov, V. Nikolaev, V. Zakharov, M. Khokhlova, Yu. Kontsevaya, A. Bonartsev, V. Voinova


The development of new effective methods for processing scientific technical information in interdisciplinary research using mathematical-linguistic targeted search is an urgent problem in such scientific field as the biomaterials for tissue engineering, because of the need to process large information volume, the diversity of information sources and terminological confusion. The authors have conducted a study to analyze the incoming flow of research papers for their relevance to the user's subject area. A personal database of 3,650 articles collected over 20 years of professional activity by the “Medical Biopolymers” scientific group was taken as an initial dataset. It was compared with the data collected by automated method from 3 highly ranked journals - Acta Biomaterialia, Biomaterials, Materials Today Bio. The software library difflib, based on the Ratcliffe-Mezner algorithm, was used to determine the similarity of the texts. According to the results of the study it was found that the developed approach was able to adequately identify the publications corresponding to the interests of the leader of the scientific group "Medical Biopolymers", but it also revealed a number of challenges, which are planned to solve at the next stages of the study.

Full Text:

PDF (Russian)


DOI: 10.25559/INJOIT.2307-8162.10.202211.134-140

I.V. Lysak, “Interdisciplinarity: advantages and problems of application”, Modern problems of science and education, no. 5, p. 264, 2016.

A.N. Knigin, “Interdisciplinarity: the main problem”, Vestnik Tomsk State University. Philosophy. Sociology. Political science, no. 3(4), pp. 14-21, 2008.

N.K. Paschos, W.E. Brown, R. Eswaramoorthy, J.C. Hu, K.A. Athanasiou, “Advances in tissue engineering through stem cell-based co-culture”, J Tissue Eng Regen Med, no. 9(5), pp. 488-503, 2015, doi: 10.1002/term.1870.

Koons, G.L., Diba, M. & Mikos, A.G. Materials design for bone-tissue engineering. Nat Rev Mater 5, 584–603 (2020).

H. Qu, H. Fu, Z. Han, Y. Sun, “Biomaterials for bone tissue engineering scaffolds: A review”, RSC advances, no. 9(45), pp. 26252-26262, 2019.

A. Eltom, G. Zhong, A. Muhammad, “Scaffold techniques and designs in tissue engineering functions and purposes: a review”, Advances in materials science and engineering, 2019, 3429527, doi: 10.1155/2019/3429527.

Y. Feng, S. Zhu, D. Mei, J. Li, J. Zhang, S. Yang, S. Guan, “Application of 3D printing technology in bone tissue engineering: a review”, Current Drug Delivery, no. 18(7), pp. 847-861, 2021.

S. Chahal, A. Kumar, F.S.J. Hussian, “Development of biomimetic electrospun polymeric biomaterials for bone tissue engineering. A review”, Journal of biomaterials science, polymer edition, no. 30(14), pp. 1308-1355, 2019.

J. Wang, Y. Dong, “Measurement of text similarity: a survey”, Information, vol. 11, no. 9. 2020, doi: 10.3390/info11090421.

A.W. Qurashi, V. Holmes, A.P. Johnson, “Document processing: Methods for semantic text similarity analysis”, International Conference on Innovations in Intelligent SysTems and Applications (INISTA), IEEE, 2020, pp. 1-6.

M.B. Magara, S.O. Ojo, T. Zuva, “A comparative analysis of text similarity measures and algorithms in research paper recommender systems”, ICTAS2018, IEEE, 2018, pp. 1-5.

M.M. Deza, E. Deza, Encyclopedia of distances, Berlin, Heidelberg: Springer, 2009.

R.F. Cancho, “Euclidean distance between syntactically linked words”, Physical Review E, vol. 70, no. 5, 2004, doi: 10.1103/PhysRevE.70.056135.

D. Gunawan, C.A. Sembiring, M.A. Budiman, “The implementation of cosine similarity to calculate text relevance between two documents”, Journal of physics: conference series, vol. 978, no. 1, 2018, doi :10.1088/1742-6596/978/1/012120.

A.R. Lahitani, A.E. Permanasari, N.A. Setiawan, “Cosine similarity to determine similarity measure: Study case in online essay assessment”, 4th International Conference on Cyber and IT Service Management, IEEE, 2016, doi: 10.1109/CITSM.2016.7577578.

J. Jotheeswaran, Y.S. Kumaraswamy, “Opinion mining using decision tree based feature selection through manhattan hierarchical cluster measure”, Journal of Theoretical & Applied Information Technology, vol. 58, no. 1, 2013.

M. Eminagaoglu, “A new similarity measure for vector space models in text classification and information retrieval”, Journal of Information Science, vol. 48, no. 4, pp. 463-476, 2022.

S.V. Znamenskij, “A model and algorithm for sequence alignment”, Program systems: theory and applications, vol. 5, no. 4(22), pp. 257-267, 2014.

S.J.G. Bermudez, S.U. Kerimova, “About method of determination of textual proximity, based on the semantics classes”, Engineering journal of Don, vol. 43, no. 4(43), 2016.

R. Campos et al. “YAKE! Keyword extraction from single documents using multiple local features”, Information Sciences, vol. 509, pp. 257-289, 2020.

K. Wołk, K. Marasek, “A sentence meaning based alignment method for parallel text corpora preparation”, New Perspectives in Information Systems and Technologies, vol. 1, pp. 229-237, 2014.


  • There are currently no refbacks.

Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162