**Acknowledgements**

It is possible to annotate network resources using classes (concepts) of the corresponding ontology (TBox – terminological components), using objects (individuals) of knowledge graphs (ABox – assertion components), or using both. The depth of the carried out semantic analysis can be limited by considering only textual metadata inherent in network resources and entities in knowledge graphs. Full-text semantic analysis can be very expensive and, in many ways, redundant. Improving the accuracy of annotation in full-text analysis often does not

*performed; 4 – knowledge graphs and entities corresponding to the annotated network resource.*

*Displaying semantic annotation results: 1 – addresses of the annotated network resources (URLs); 2 – setting options and starting the semantic annotation process; 3 – network resource for which semantic annotation is*

The number of displaying entities from knowledge graphs can be limited by the user. At the top of the output of the "Semantic annotation" software agent, the entities that are most adequate to the annotated resource appear. All the results of

As an example of using the "Semantic annotation" software agent, **Figure 11** below shows the results of the semantic annotation of one network resource. It can be seen that semantic annotations from five different knowledge graphs were discovered. With one click, the user can open the RDF browser and visualize the found annotations in any of the knowledge graphs, as well as anyone can see the surroundings of the entities found, for example, their classes and neighboring objects. This information is essential for a knowledge engineer who is engaged in

Groups of scientists from the University of Manchester, Stanford University, University of Bari and a number of other universities are focused on the issues of

justify the increased consumption of computing resources and time.

the work can be saved in files on the user's computer for later study.

knowledge graph refinement and enrichment.

*Cloud Computing Security - Concepts and Practice*

**8. Related work and conclusion**

**112**

**Figure 11.**

The reported study was funded by the Russian Foundation for Basic Research and Government of the Kaluga Region according to the research projects 19-47- 400002 and was funded by the Vladimir Potanin Foundation according to the project GC190001383.

*Cloud Computing Security - Concepts and Practice*

**References**

p. 505

[1] W3C OWL 2 Web Ontology

[2] Baader F, Calvanese D, McGuinness D, Nardi D, Patel-Schneider P. The Description Logic Handbook: Theory, Implementation and

Applications. 2nd ed. New York: Cambridge University Press; 2010.

[3] Telnov V, Korovin Y. Semantic web and knowledge graphs as an educational technology of personnel training for nuclear power engineering. Nuclear Energy and Technology. 2019;**5**(3): 273-280. DOI: 10.3897/nucet.5.39226

[4] Telnov V. Semantic educational portal. Nuclear knowledge graphs. Intellectual search agents [Internet]. 2020. Available from: http://vt.obninsk. ru/x/ [Accessed: 29 March 2020]

Superstructure. 2012. Available from: http://drive.google.com/file/d/

0B0jk0QU2E5q9NVIwMFNieGxOZVU

[6] Ontology example "Nuclear Physics at MSU and MEPhI". 2020. Available from: http://drive.google.com/file/d/ 1AIXMsm3cfAxR6NX220R4ZeFe oSFp0mj5 [Accessed: 29 March 2020]

[7] Fanizzi N, d'Amato C, Esposito F. Induction of concepts in web ontologies through terminological decision trees. In: ECML/PKDD. Barcelona. Spain; 2010. pp. 442-457. DOI: 10.1007/978-3-

[8] Bobillo F, Carvalho R, Costa P, d'Amato C, Fanizzi N, Laskey K, et al. Uncertainty reasoning for the semantic

web III. In: SWC International Workshops URSW. Revised Selected Papers; 21–25 October. Sydney,

[5] ISO 19505 UML Part 2

[Accessed: 29 March 2020]

642-15880-3\_34

**115**

Language. 2012. Available from: http:// www.w3.org/TR/owl2-overview/ [Accessed: 29 March 2020]

*DOI: http://dx.doi.org/10.5772/intechopen.92433*

*Semantic Web and Interactive Knowledge Graphs as an Educational Technology*

Australia; 2013. pp. 1-328. DOI: 10.1007/

[9] Levenshtein V. Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics – Doklady.

[10] Chen Y, Wang Z, Yang E, Li Y.

(SKIMA); 15–17 December 2016.

10.1109/SKIMA.2016.7916207

200388

March 2020]

pp. 197-200

150200

[14] Camarda D, Mazzini S,

Antonuccio A. Lodlive, exploring the web of data. In: Proceedings of the 8th International Conference on Semantic Systems, I-SEMANTICS, ACM; September 2012. Graz, Austria; 2012.

[15] Schlobach S, Janowicz K. Visualizing ontologies with VOWL. Semantic Web. 2016;**7**:399-419. DOI: 10.3233/SW-

recommendation using a multi–objective artificial wolf–pack algorithm. In: Proceedings of 10th International Conference on Software, Knowledge, Information Management & Applications

Chengdu, China; 2016. pp. 116-121. DOI:

[11] d'Amato C. Machine learning for the semantic web: Lessons learnt and next research directions. Semantic Web. 2020;**11**:195-203. DOI: 10.3233/sw-

[12] d'Amato C, Fanizzi N, Fazzinga B, Gottlob G, Lukasiewicz T. Combining Semantic Web Search with the Power of Inductive Reasoning. 2013. Available from: http://ceur-ws.org/Vol-527/pape r2.pdf [Accessed: 29 March 2020]

[13] Bikakis N, Sellis T. Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art. 2016. Available from: http://arxiv.org/ pdf/1601.08059.pdf [Accessed: 29

978-3-319-13413-0

1965;**10**(8):707-710

Pareto-optimality solution
