Natural Language Processing Tools for Predictive Modeling of Advanced Trends in Formal Ontologies in Biomedical Sciences
https://doi.org/10.21603/sibscript-2024-26-4-567-575
Abstract
Natural language processing methods can be used to predict advanced application trends in formal ontologies. Formal ontologies help to formalize the characteristics of objects in various domains. As a result, machine learning programs identify patterns and relationships between these characteristics. The article describes an experiment based on machine learning methods in combination with text search methods. It involves the CatBoost algorithm for predictive modeling and clustering of lexical items. The vector models of the corresponding items reflect a trend in a particular domain of knowledge; proximity between them was calculated based on the idea of semantic distance. The experiment revealed four advanced areas for formal ontologies, i.e., genotype – phenotype; personalization; clustering algorithms, and collaborative task management. Each area that represented the predictable trends of development in this particular domain was provided with keywords. The article also contains a review of most popular scientific articles on these trends.
About the Authors
M. M. Charnine
Computer Science and Control Federal Research Center, Russian Academy of Sciences
Russian Federation
Competing Interests:
Russian Federation
Mikhail M. Charnine
Scopus Author ID: 6504713775
Moscow
Competing Interests:
Conflict of interests: The authors declared no potential conflict of interests regarding the research, authorship, and / or publication of this article.
S. S. Kalinin
International Slavic Institute
Russian Federation
Competing Interests:
Russian Federation
Stepan S. Kalinin
Scopus Author ID: 57206675409
Moscow
Competing Interests:
Conflict of interests: The authors declared no potential conflict of interests regarding the research, authorship, and / or publication of this article.
References
1. Bova V. V., Leshchanov D. B., Kravchenko D. Yu., Novikov A. A. Computational ontology: Objectives and methodology of construction. Informatika, vychislitelnaia tekhnika i inzhenernoe obrazovanie, 2014, (4): 44–55. (In Russ.) https://www.elibrary.ru/ubnfff
2. Korsun I. A., Palchunov D. E. Methods of data retrieving from texts written in natural language and formal representation of ontological knowledges. Knowledges – Ontologies – Theories (ZONT-2017): Proc. All-Russian Sci. Conf. with Intern. Participation, Novosibirsk, 2–6 Oct 2017. Novosibirsk: Digit Pro, 2017, vol. 2, 21–25. (In Russ.) https://www.elibrary.ru/eidazb
3. Mitrofanova O. A., Azarova I. V. Computational representation of meanings. Applied and computational linguistics, eds. Nikolaev I. S., Mitrenina O. V., Lando T. M. 2nd ed. Moscow: Lenand, 2017, 59–93. (In Russ.) https://www.elibrary.ru/zcwtrj
4. Charnine M. M. A bibliometric analysis of trending topics in the English language linguistics research literature. Russian Linguistic Bulletin, 2023, (12). (In Russ.) https://doi.org/10.18454/RULB.2023.48.17
5. Bukhari A. C., Kim Y.-G. Integration of a secure type-2 fuzzy ontology with a multi-agent platform: A proposal to automate the personalized flight ticket booking domain. Information Sciences, 2012, 198: 24–47. https://doi.org/10.1016/j.ins.2012.02.036
6. Charnine M., Klokov A., Kochiev L., Tishchenko A. Research trending topic prediction as cognitive enhancement. 2021 International Conference on Cyberworlds (CW). Caen, 2021a, 217–220. https://doi.org/10.1109/CW52790.2021.00044
7. Charnine M., Tishchenko A., Kochiev L. Visualization of research trending topic prediction: Intelligent method for data analysis. CEUR Workshop Proceedings: Proc. 31st Intern. Conf. on Computer Graphics and Vision, Nizhny Novgorod, 27–30 Sep 2021. 2021b, vol. 3027, 1028–1037. https://www.elibrary.ru/ptcljf
8. Coulet A., Smaïl-Tabbone M., Benlian P., Napoli A., Devignes M. D. Ontology-guided data preparation for discovering genotype-phenotype relationships. BMC Bioinformatics, 2008, 9(S4). https://doi.org/10.1186/1471-2105-9-s4-s3
9. Einig M., Tagg R., Peters G. Managing the knowledge needed to support an electronic personal assistant – an end-user friendly graphical ontology editing tool. Proceedings of the Eighth International Conference on Enterprise Information Systems – ISAS, 2006, vol. 5, 304–309. https://doi.org/10.5220/0002446003040309
10. Hoehndorf R., Harris M. A., Herre H., Rustici G., Gkoutos G. V. Semantic integration of physiology phenotypes with an application to the Cellular Phenotype Ontology. Bioinformatics, 2012, 28(13): 1783–1789. https://doi.org/10.1093/bioinformatics/bts250
11. Hoehndorf R., Loebe F., Kelso J., Herre H. Representing default knowledge in biomedical ontologies: Application to the integration of anatomy and phenotype ontologies. BMC Bioinformatics, 2007, 8. https://doi.org/10.1186/1471-2105-8-377
12. Holloway E. From genotype to phenotype: Linking bioinformatics and medical informatics ontologies. Comparative and functional genomics, 2002, 3(5): 447–450. https://doi.org/10.1002/cfg.181
13. Ivanova T., Andreev R., Terzieva V. Integration of ontology with development of personalized e-learning facilities for dyslexics. Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2010: Proc. 14th Intern. Conf., Varna, Bulgaria, 8–10 Sep 2010. Berlin Heidelberg: Springer, 2010, 265–266. https://doi.org/10.1007/978-3-642-15431-7_29
14. Kim J., Kang S. An ontology-based personalized target advertisement system on interactive TV. Multimedia Tools and Applications, 2013, 64(3): 517–534. https://doi.org/10.1007/s11042-011-0965-0
15. Liotine M., Ramaprasad A., Syn T. Managing a smart city's resilience to ebola: An ontological framework. 49th Hawaii International Conference on System Sciences (HICSS 2016). Kauai, Hawaii, USA, 2016, 2935–2943. http://dx.doi.org/10.1109/HICSS.2016.368
16. Maleszka B. A method for profile clustering using ontology alignment in personalized document retrieval systems. Computational Collective Intelligence. ICCCI 2015: Proc. 7th Intern. Conf., Madrid, Spain, 21–23 Sep 2015. Springer, 2015, 410–420. https://doi.org/10.1007/978-3-319-24069-5_39
17. Moshirpour M., Mireslami S., Alhajj R., Far B. H. Automated ontology construction from scenario based software requirements using clustering techniques. 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI). 2012, 541–547.
18. Munir K., Anjum S. M. The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics, 2018, 14(2): 116–126. http://dx.doi.org/10.1016/j.aci.2017.07.003
19. Prokhorenkova L., Gusev G., Vorobev A., Dorogush A. V., Gulin A. CatBoost: Unbiased boosting with categorical features. Advances in neural information processing systems: Proc. Conf., Montreal, QC, 2–8 Dec 2018, 6638–6648. https://www.elibrary.ru/nryevs
20. Rojano E., Córdoba-Caballero J., Jabato F. M., Gallego D., Serrano M., Pérez B., Parés-Aguilar A., Perkins J. R., Ranea J. A. G., Seoane-Zonjic P. Evaluating, filtering and clustering genetic disease cohorts based on human phenotype ontology data with cohort analyzer. Journal of Personalized Medicine, 2021, 11(8). https://doi.org/10.3390/jpm11080730
21. Sandhiya R., Sundarambal M. Clustering of biomedical documents using ontology-based TF-IGM enriched semantic smoothing model for telemedicine applications. Cluster Computing, 2019, 22(S2): 3213–3230. https://doi.org/10.1007/s10586-018-2023-4
22. Schmidt D., Pannison A., Freitas A., Bordini R. H., Meneguzzi F., Vieira R. An ontology-based mobile application for task managing in collaborative groups. Proceedings of the 29th Florida Artificial Intelligence Research Society Conference (FLAIRS). Association for the Advancement of Artificial Intelligence (AAAI), 2016, 522–525.
23. Weiler G., Schwarz U., Rauch J., Rohm K., Lehr T., Theobald S., Kiefer S., Götz K., Och K., Pfeifer N., Handl L., Smola S., Ihle M., Turki A. T., Beelen D. W., Rissland J., Bittenbring J., Graf N. XplOit: An ontology-based data integration platform supporting the development of predictive models for personalized medicine. Studies in health technology and informatics, 2018, 247: 21–25. https://doi.org/10.3233/978-1-61499-852-5-21
24. Yu G., Yang Y., Qingsong X. An ontology-based approach for knowledge integration in product collaborative development. Journal of Intelligent Systems, 2017, 26(1): 35–46. https://doi.org/10.1515/jisys-2015-0046
25. Zhang G., Bie R., Sun Y. Bring biomedical ontologies to personalized healthcare: A smart inquiry framework. 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). Washington, DC, USA, 2016, 84–88. https://doi.org/10.1109/CHASE.2016.32
26. Zhao M., Havrilla J., Peng J., Drye M., Fecher M., Guthrie W., Tunc B., Schultz R., Wang K., Zhou Y. Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records. Journal of Neurodevelopmental Disorders, 2022, 14. https://doi.org/10.1186/s11689-022-09442-0
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For citations:
Charnine M.M., Kalinin S.S. Natural Language Processing Tools for Predictive Modeling of Advanced Trends in Formal Ontologies in Biomedical Sciences. SibScript. 2024;26(4):567-575. (In Russ.) https://doi.org/10.21603/sibscript-2024-26-4-567-575
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