Proceedings of International Conference on Applied Innovation in IT
2021/04/28, Volume 9, Issue 1, pp.41-53

Multilevel Ontologies for Big Data Analysis and Processing


Maryna Popova, Larysa Globa, Rina Novogrudska


Abstract: The problem of ever-increasing amounts of unstructured information in various fields of human activity is known as the problem of Big Data. Providing support for analytical activities requires determining the main factors that affect certain states of objects and processes in domains, as well as the degree of their influence, this significantly complicates the decision-making process, especially if data are represented heterogeneous information, there is a need to simultaneously take into account the impact of data from several areas dealing with several levels of classification. Given the significant volumes of text documents, it is impossible to solve the problem of structuring linguistic information by computer-aided extraction of the basic concepts that determine the text content (meaning), as well as the problem of constructing a formalized structure for formation the classes of individual objects and relations between them. The paper considers the ontological approach to the analysis and processing of Big Data represented both heterogeneous and linguistic data in the form of a multilevel ontology, implemented by computer-aided extracting of the basic concepts that define the text content (meaning) and determining semantic relations between the distributed information resources. The proposed approach uses the possibility of non-canonical conceptual ontologies to define equivalent concepts and thus to integrate the multiple ontologies that affect the same subject domain. This approach was implemented to create a multilevel ontology in the systemic biomedicine, the application of which in the process of postgraduate doctors and pharmacist’s education has significantly reduced the search time of relevant information and errors number due to the lack of unified terminology.

Keywords: Big Data, Multilevel Ontology, Taxonomization, Relations, Knowledge, Concepts

DOI: 10.25673/36583

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References:

  1. B. Neumayr, K. Grn, and M. Schrefl, “Multilevel domain modeling with m-objects and m- relationships,” Proc. of 6th Asia-Pacific Conf. on Conc. Model., New Zealand, 2009.
  2. C. Atkinson and T. Khne, “The essence of multilevel modeling,” Proc. of 4th Int. Conf. on the Unified Model. Lang., pp. 19-33, Toronto, Canada, 2001.
  3. V. Mayer-Schonberger and K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Canada: Eamon Dolan/Houghton Mifflin Harcourt, 2013, p. 242.
  4. A. Luntovskyy and L. Globa, “Big Data: Sources and Best Practices for Analytics,” Proc. of International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo’19), pp. 1-6, 2019.
  5. O. Stryzhak, V. Prychodniuk, and V. Podlipaiev, “Model of Transdisciplinary Representation of GEOspatial Information,” in Advances in Information and Communication Technologies. Lecture Notes in Electrical Engineering, vol. 560, Cham: Springer, 2019, pp. 34-75
  6. M. Popova, O. Stryzhak, O. Mintser, and R. Novogrudska, “Medical Transdisciplinary Cluster Development for Multivariable COVID-19 Epidemiological Situation Modeling,” Proc. Of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020), 2020, pp. 1662-1667, doi: 10.1109/BIBM49941.2020.9313204.
  7. G. Barzdins, N. Gruzitis, G. Nešpore-Bērzkalne, B. Saulīte, I. Auziņa, and K. Levāne-Petrova, “Multidimensional Ontologies: Integration of Frame Semantics and Ontological Semantics,” Proc. of 13th EURALEX International Congress, pp. 23-28, Barcelona, Spain, 2008
  8. I. L. Artemyeva, “Complexly structured subject areas. Construction of multilevel ontologies,” Information Technology, vol. 1, pp. 16-21, 2009.
  9. V. Nebot, R. Berlanga-Llavori, J. Pérez-Martínez, M. Aramburu, and T. Pedersen, “Multidimensional Integrated Ontologies: A Framework for Designing Semantic Data Warehouses,” Data Semantics, vol. 13, pp. 1-36, 2009.
  10. N. Prat, J. Akoka, and I. Comyn-Wattiau, “Transforming multidimensional models into OWL- DL ontologies,” Proc. of Multidimensional Models Meet the Semantic Web: Defining and Reasoning on OWL-DL Ontologies for OLAP, Hawaii, USA, 2012.
  11. T. Niemi and M. Niinimäki, “Ontologies and summarizability in OLAP,” Proc. of the Proceedings of the 2010 ACM Symposium on Applied Computing(SAC’10), pp. 1349-1353, March 2010.
  12. O. P. Mintser and V. M. Zaliskiy, Systemic biomedicine. Kyiv: Interservice, 2019, p. 552 .
  13. L. El Saraj, B. Espinasse, T. Libourel, and S. Rodier, “Towards Ontology-Driven Approach for Data Warehouse Analysis,” Proc of 8th Int. Conf. on Software Eng. Advances (ICSEA 2013), pp. 1-6, Venice, Italy, 2013.
  14. N. F. Noy and D. L. McGuinness, “Ontology development 101: A guide to creating your first ontology,” Technical report ksl-01-05 and stanford medical informatics technical report smi-2001-0880, Stanford Knowledge Systems Laboratory, 2001
  15. S. Jean, G. Pierra, and Y. Ait-Ameur, “Domain Ontologies : A Database-Oriented Analysis,” Proc. of Web Inf. Sys. and Techn. (WEBIST’2006), pp. 238- 254, Set`ubal, Portugal, April 2006.
  16. V. Prychodniuk, “Technological means of transdisciplinary representation of geospatial information,” ITGIS, Kyiv, 2017.
  17. V. Prychodniuk, “Taxonomy of natural-language texts,” Information Models and Analyses, vol. 5(3), pp. 270-284, 2016.
  18. O. Ye. Stryzhak, L. S. Globa, V. Y. Velichko, M. A. Popova, and others, “Computer program Cognitive IT platform “POLYHEDRON”,” Certificate of copyright to the work No96078 dated 17.02.2020, Official bulletin No 57 (31.03.2020), pp. 402-403, 2020.
  19. V. Velychko, M. Popova, V. Prykhodniuk, and O. Stryzhak, “TODOS – IT-platform for the formation of transdisciplinary informational environments,” Syst. Arms and Milit. Equip., vol. 1(49), pp. 10-19, 2017.
  20. O. Ye. Stryzhak, V. V. Prykhodniuk, S. I. Haiko, and V. B. Shapovalov, “Display of network information in the form of interactive documents. Transdisciplinary approach,” Math. Model. in econ., vol. 5(3), pp. 87- 100, 2018.
  21. M. Popova, “Ontology of interaction in the environment of the geographic information system,” ITGIS, Kyiv, 2014.
  22. M. Popova, “A model of the ontological interface of aggregation of information resources and means of GIS,” Inf. Tech. and Knowl., vol. 7(4), pp. 362-370, 2013.
  23. C. Rung-Ching, L. Bo-Ying, and B. Cho-Tscan, “Using Domain Ontology Mapping for Drugs Recommendation,” Department Of Information Management, Chaoyang University Of Technology, Taiwan, 2009
  24. I. Olaronke, A. Soriyan, and I. Gambo, “Ontology Matching: An Ultimate Solution for Semantic Interoperability in Healthcare,” Int. J. Comp. App., vol. 51, pp. 7-14, 2012, doi:10.5120/8325-1707.
  25. M. Ehrig and S. Staab, “QOM – Quick Ontology Mapping,” Proc of the Int Sem. Web Conf., vol. 3298, pp. 683–697, 2004.
  26. B. Veli, B. L. Gokce, D. Asuman, and K. Yildiray, “Artemis Message Exchange Framework: Semantic Interoperability of Exchanged Messages in the Healthcare Domain,” Software Research and Development Center, Middle East Technical University (METU), Ankara, Turkiye, 2006.
  27. S.Z.Katrin,“Instance-BasedOntologyMatchingand the Evaluation of Matching Systems,” Inaugural- Dissertation. Department of Computer Science, Heinrich Heine University of Dusseldorf, Germany, 2010
  28. E. Rahm and P. Bernstein, “A Survey of Approaches to Automatic Schema Matching,” VLDB Journal, pp. 334-350, 2001.
  29. M.V.Nadutenko,“Virtualizedlexicographicsystems and their application in applied linguistics,” ULIF, Kiev, 2016.
  30. V. A. Shirokov, Information theory of lexicographic systems. Kyiv: Dovira, 1998, p. 331.
  31. The ontology description language Ontolingua [Online]. Available: http://www.ksl.stanford.edu/ software/ontolingua, July 2005.
  32. A. E. Vovchenko, V. N. Zakharov, L. A. Kalini- chenko, D. Yu. Kovalev, O. V. Ryabukhin, and oth., “Multilevel specifications in conceptual and ontological modeling,” Proc. of 13th All-Russian Scientific Conf. (RCDL'2011), pp. 35-43, Voronezh, 2011.


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DOI: http://dx.doi.org/10.25673/112984


        

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