Skip to main content
SpringerLink
Log in

Knowledge and Datasets as a Resource for Improving Artificial Intelligence

  • Conference paper
  • First Online:
Book cover Data Science and Intelligent Systems (CoMeSySo 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 231))

Included in the following conference series:

Abstract

This work discusses and analyzes options that could be helpful for the research and development of artificial intelligence. The work is based on analyzes and results of other authors as well as on the own experience. Preprocessing and selecting appropriate data is the key to success in using tools and algorithms for artificial intelligence. This means that the input data is largely a factor that affects the outcome and resulting success of the algorithms used in artificial intelligence. Data is essential for AI improvement, and this article gives our suggestion on how to improve that data, how to turn it into intelligent data that will provide more knowledge for AI algorithms. We describe our suggestions and experiences with the creation of datasets using ontology model and findings of what they should meet, and what are the problems associated with it.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.ime.usp.br/~fr/opencyc/.

  2. 2.

    https://wordnet.princeton.edu/.

  3. 3.

    https://github.com/Ebiquity/uco2.

  4. 4.

    https://github.com/rychtovy24/diplomova_praca/tree/master/Ontology.

  5. 5.

    https://attack.mitre.org/.

  6. 6.

    https://cuckoosandbox.org/.

  7. 7.

    https://www.hybrid-analysis.com.

References

  1. Ucci, D., Aniello, L., Baldoni, R.: Survey of machine learning techniques for malware analysis. Comput. Secur. 81, 123–147 (2019)

    Article  Google Scholar 

  2. Wissner-Gross, A.: Datasets Over Algorithms. Edge. http://www.edge.org/response-detail/26587 (2016). Accessed 01 Aug 2021

  3. Datasets Over Algorithms: https://www.kdnuggets.com/2016/05/datasets-over-algorithms.html (2016). Accessed 01 Aug 2021

  4. Knight, M.: Taxonomy vs Ontology: Machine Learning Breakthroughs. https://www.dataversity.net/taxonomy-vs-ontology-machine-learning-breakthroughs/# (2017). Accessed 01 Aug 2021

  5. LClaire, B., Patrick, J., Sébastien, M., Nicolas, P.: Data sharing is key to innovation in health care. https://www.technologyreview.com/2019/09/27/132847/data-sharing-is-key-to-innovation-in-health-care/ (2019). Accessed 01 Aug 2021

  6. Baker, L.: Sharing Information is the Key. https://www.cyberwyoming.org/sharing-information-is-the-key/ (2019). Accessed 01 Aug 2021

  7. Letouzé, E., Oliver, N.: Sharing is Caring Four Key Requirements for Sustainable Private Data Sharing and Use for Public Good. Data-pop alliance and Vodafone Institute for Society and Communications, London (2019)

    Google Scholar 

  8. Mavroeidis, V., Bromander, S.: Cyber threat intelligence model: an evaluation of taxonomies, sharing standards, and ontologies within cyber threat intelligence. In: 2017 European Intelligence and Security Informatics Conference (EISIC), pp. 91–98. IEEE (2017)

    Google Scholar 

  9. The MITRE Corporation: Science of Cyber-Security. https://fas.org/irp/agency/dod/jason/cyber.pdf (2010). Accessed 01 Aug 2021

  10. Ulicny, B.E., Moskal, J.J., Kokar, M.M., Abe, K., Smith, J.K.: Inference and ontologies. In: Kott, A., Wang, C., Erbacher, R.F. (eds.) Cyber Defense and Situational Awareness. AIS, vol. 62, pp. 167–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11391-3_9

    Chapter  Google Scholar 

  11. Syed, Z., Padia, A., Finin, T., Mathews, L., Joshi, A.: UCO: a unified cybersecurity ontology. In: Workshops at the Thirtieth AAAI Conference on Artificial Intelligence (2016)

    Google Scholar 

  12. Svec, P., Balogh, S., Homola, M.: Experimental Evaluation of Description Logic Concept Learning Algorithms for Static Malware Detection. In ICISSP, pp. 792–799 (2021)

    Google Scholar 

  13. Takahashi, T., Panta, B., Kadobayashi, Y., Nakao, K.: Web of cybersecurity: linking, locating, and discovering structured cybersecurity information. Int. J. Commun. Syst. 31(3), e3470 (2018)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by research grants APVV-19-0220.

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Slovak Technical University, Bratislava, Slovakia

    Štefan Balogh

Authors
  1. Štefan Balogh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Štefan Balogh .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Balogh, Š. (2021). Knowledge and Datasets as a Resource for Improving Artificial Intelligence. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Intelligent Systems. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-030-90321-3_68

Download citation

Publish with us

Policies and ethics

Search

Navigation