HYBRID BIOINSPIRED ALGORITHM FOR ONTOLOGIES MAPPING IN THE TASKS OF EXTRACTION AND KNOWLEDGE MANAGEMENT
Abstract
The article is devoted to solving the problem of mapping ontological models in the processes
of extracting and knowledge management. The relevance and significance of this task are due to
the need to maintain reliability and eliminate redundancy of knowledge during the integration
(unification) of various origins structured information sources. The proximity and consistency of
the conceptual semantics of the combined resource during the mapping is the main criterion for
the effectiveness of the proposed solutions. The article considers the problems of choosing appropriate
solution approaches that preserve semantics when displaying concepts. The strategy of
choosing bio-inspired modeling is substantiated. The aspects of the effectiveness of various decentralized
bio-inspired methods are analyzed. The reasons for the need for hybridization are identified.
The paper proposes to solve the problem of mapping ontological models using a bio-inspired
algorithm based on hybridization of bacterial and cuckoo search algorithms optimization mechanisms.
The hybridization of these algorithms allowed us to combine their main advantages: a consistent
bacterial search that provides a detailed study of local areas, and a significant number of
the cuckoo agent during the implementation global movements of Levy flights. To evaluate the
effectiveness of the proposed hybrid bio-inspired algorithm, a software product was developed and
experiments were performed on the mapping of different sizes ontologies. Each concept of any
ontology has a certain set of attributes, which is a semantic vector of attributes. The degree of the
semantic vectors similarity for the compared concepts of displayed ontologies is a criterion for
their integration. To improve the quality of the display process, a new encoding of solutions has
been introduced. The quantitative estimates obtained demonstrate time savings in solving problems
of relatively large dimension (from 500,000 ontograph vertices) of at least 13 %. The time
complexity of the developed hybrid algorithm is O (n 2). The described studies have a high level of
theoretical and practical significance and are directly related to the solution of classical problems
of artificial intelligence aimed at finding hidden dependencies and patterns on a multitude of
knowledge elements.
References
Systems Association World Congress, 2016, Vol. 1, pp. 496-502.
2. Castano S., Ferrara A., Montanelli S., Racca G. Semantic information interoperability in open
networked systems, Proc. оf the Int. Conf. SNW. Paris, 2004, pp. 215-230.
3. Kravchenko Yu.A., Kursitys I.O., Markov V.V. Bioinspired Algorithm for Acquiring New
Knowledge based on Information Resource Classification, 2019 International Russian Automation
Conference (RusAutoCon).
4. Haase P., Siebes R., Harmelen F. Peer selection in peerto-peer networks with semantic topologies,
Proc. оf Int. Conf. on Semantics in a Networked World. Paris, 2004, pp. 108125.
5. Maedche A., Zacharias V. Clustering ontology-based metadata in the Semantic Web, Proc. 6th
European PKDD Conf. LNCS. Berlin: Springer, 2002, Vol. 2431, pp. 348-360.
6. Abraham A., Grosan G., Ramos V. Swarm Intelligence in Data Mining. Berlin. Heidelberg:
SpringerVerlag, 2006, 267 p.
7. Sousa T., Silva A., Neves A. Particle Swarm based Data Mining Algorithms for classification
tasks, Parallel Computing, 2004, Vol. 30, Issue 5–6, pp. 767-783.
8. Parsopoulos K.E., Vrahatis M.N. Recent Approaches to Global Optimization Problems
Through Particle Swarm Optimization, Natural Computing, 2002, No. 1 (2–3), pp. 235-306.
9. Kravchenko Yu.A., Kravchenko D.Y., Kursitys I.O. Architecture and method of integrating
information and knowledge on the basis of the ontological structure, Advances in Intelligent
Systems and Computing. 1st International Conference of Artificial Intelligence, Medical Engineering,
and Education, AIMEE 2017. Moscow, 2018, Vol. 658, pp. 93-103.
10. Kravchenko Yu.A., Kureichik V.V. Knowledge management based on multi-agent simulation in
informational systems, Conference proceedings. 8th IEEE International Conference “Application
of Information and Communication Technologies – AICT 2014”. – 15-17 October 2014,
Astana, Kazakhstan, pp. 264-267.
11. Arijit S.D., Sambarta B., Abraham D.A. Bacterial Foraging Optimization Algorithm: Theoretical
Foundations, Analysis, and Applications, Foundations of Computational Intelligence.
Springer, 2009, Vol. 3, pp. 23-55.
12. Payne R.B., Sorenson M.D., and Klitz K. The Cuckoos. Oxford University Press, 2005.
13. Kravchenko Yu.A., Kuliev E.V., Kulieva N.V., Kureichik V.V. Problem-oriented knowledge
processing on the basis of hybrid approach, Information technologies in science. Management,
Social Sphere and Medicine (ITSMSSM 2016), pp. 510-513.
14. Martens D., De Backer M., Haesen R., Vanthienen J., Snoeck M. and Baesens B. Classification
With Ant Colony Optimization, IEEE Transactions on Evolutionary Computation, 2007,
Vol. 11, No. 5, pp. 651-665.
15. Falco I.D., Cioppa A.D., and Tarantino E. Evaluation of particle swarm optimization effectiveness
in classification, LNAI3849, 2006, pp. 164-171.
16. Soliman O.S. and Adly A. Bio-inspired algorithm for classification association rules, 8th International
Conference on Informatics and Systems (INFOS), Cairo, 2012, pp. 154-160.
17. Bova V., Zaporozhets D., and Kureichik V. Integration and processing of problem-oriented
knowledge based on evolutionary procedures, Advances in Intelligent Systems and Computing,
2016, Vol. 450, pp. 239-249.
18. Semenova A.V. and Kureichik V.M. Ensemble of classifiers for ontology enrichment, Journal
of Physics: Conference Series, 2018, Vol. 1015, Issue 3, article id. 032123.
19. Kureychik V.M. Overview and problem state of ontology models development, 9th International
Conference on Application of Information and Communication Technologies, AICT
2015 - Proceedings 9, 2015, pp. 558-564.
20. Semenova A.V. and Kureychik V.M. Application of swarm intelligence for domain ontology
alignment, Proceedings of the First International Scientific Conference “Intelligent Information
Technologies for Industry” (IITI’16), 2016, Vol. 1, pp. 261-270.
21. Bova V., Kureichik V. and Zaruba D. Heuristic approach to model of corporate knowledge
construction in information and analytical systems, 2016 IEEE 10th International Conference
on Application of Information and Communication Technologies (AICT), Baku, 2016, pp. 1-5.
22. Kureichik V., Zaporozhets D., and Zaruba D. Generation of bioinspired search procedures for
optimization problems, Application of Information and Communication Technologies, AICT
2016 - Conference Proceedings, 2016, Vol. 10.
23. Kar A.K. Bio inspired computing – A review of algorithms and scope of applications, Expert
Systems with Applications, 2016, Vol. 59, pp. 20-32.
24. Zaporozhets D., Zaruba D., and Kulieva N. Parallel approach for bioinspired algorithms, Journal
of Physics: Conference Series Ser. “International Conference Information Technologies in
Business and Industry 2018 - Enterprise Information Systems”, 2018.
25. Bova V.V., Nuzhnov E.V., Kureichik V.V. The combined method of semantic similarity estimation
of problem oriented knowledge on the basis of evolutionary procedures, Advances in Intelligent
Systems and Computing, 2017, Vol. 573, pp. 74-83.
