Use of ontological knowledge in machine learning methods for intelligent analysis of Big Data

J.V. Rogushina


The paper discusses problems related to the processing of Big Data in order to acquire implicit knowledge from them. Machine  learning (ML) methods oriented on these tasks can be combined with elements of the Semantic Web technologies and Artificial Intelligence (AI), which deals with intelligent behavior, learning and adaptation in computational systems.

 We analyse challenges and opportunities background knowledge using to improve ML results, the role of ontologies and other   resources of domain knowledge. Domain knowledge could improve the quality of ML results by using reasoning techniques to select learning models and prepare the training and test data.

 We propose some examples demonstrated the  use of ontologies and semantic Wiki markup for improving the efficiency of machine learning are considered deal with functional posibilities of the portal version of the Great Ukrainian Encyclopedia. Ontological model of this informational resource is considered as a domain knowledge base. Groupping  of examples is based on high-level ontological classes, and semantic properties and their relations are used for construction of space of attributes.

Problems in programming 2018; 4: 69-81



machine learning; Big Data; ontology


Laney D. 3-D data management: Controlling data volume, velocity and variety. Application Delivery Strategies by META Group Inc. 2001, P. 949.

Gandom A., Haide, M. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 2015. P. 137–144. CrossRef

Demchenko Y., De Laat C., Membrey P. Defining architecture components of the Big Data Ecosystem // Collaboration Technologies and Systems (CTS). 2014. P. 104–112.

Gladun A.Y., Rogushina J.V. Semantic technologies: principles and practics. K.: ADEF-Ukraine, 2016. 308 p. [in Ukrainian]

Mitchell T.M. Machine learning. 1997. Burr Ridge, IL: McGraw Hill. 45(37). 1997. P. 870–877.

Nikolenko S.I., Kadurin A.A., Arhangelskaya E.O. Deep Learning. Piter. 2017. [in Russian]

Bayes T. An Essay Towards Solving a Problem in the Doctrine of Chances. Philosophical Transactions of the Royal Society of London. 1763. Vol. 53. P. 370–418.

Jeffreys H. Theory of Probability, Oxford: Oxford University Press, 1939.

Savage L. The Foundations of Statistics, New York: Wiley, 1954.

Goodfellow I., Bengio Y., Courville A., Bengio Y. Deep learning (Vol. 1). Cambridge: MIT press, 2016.

Erli S. Artificial Intelligence for scalable personification. Open Systems. 2018. N 1. P. 20–24. [in Russian]

Rogushina J.V., Gladun A.Y., Osadchy V.V., Pryima S.M. Ontological Analysis for Web. Melitopol: Bogdan Hmelnitsky MDUPU. 2015. 407 p. [in Ukrainian]

Quinlan J.R. Discovery rules from large collections of examples: a case study. Expert Systems in the Microelectronic Age. Edinburg, 1979. P. 87–102.

Rogushina J.V., Grishanova I.Y. Use of inductive inference method for improvement of ontology of search domain. System research and information technologies. 2007. N 1. P. 62–70. [in Russian]

Gladun A.Y., Rogushina J.V. Data Mining: retrieval of knowlegde into data. K.: ADEF-Ukraine. 2016. 452 p. [in Ukrainian]

Breiman L. Bagging predictors. Machine Learning. 1994. 24(2). P. 123–140. CrossRef

Baclawski K., Bennett M., Berg-Cross G., Fritzsche D., Schneider T., Sharma R., Westerninen A. Ontology Summit 2017 communiqué–AI, learning, reasoning and ontologies. Applied Ontology. (Preprint). 2018. P. 1–16.

Rogushina J.V. Use of semantic properties of the Wiki resources for expansion of functional posibilities of "Great Ukrainian Encyclopedia". Encyclopaedias in the modern information space: collective monograph / Ed. Kirillon A.M. Kyiv. 2017. P. 104–115. [in Ukrainian]

Rogushina J. Semantic Wiki resources and their use for the construction of personalized ontologies. CEUR Workshop Proceedings 1631. 2016. P. 188–195.



  • There are currently no refbacks.