352
M. C. Ridley
3. Al-Arfaj, A., Al-Salman, A.: Ontology construction from text: challenges and trends. Int. J.
Artif. Intell. Expert Syst. 6(2), 15–26 (2015)
4. Shamsfard, M., Barforoush, A.: The state of the art in ontology learning: a framework for
comparison. Knowl. Eng. Rev. 18(4), 293–316 (2003)
5. Petrucci, G., Rospocher, M., Ghidini, C.: Expressive ontology learning as neural machine
translation. J. Web Semantics 52, 66–82 (2018)
6. Brown, S., Bonial, C., Obrst, L., Palmer, M.: The rich event ontology. In: Proceedings of the
Events and Stories in the News Workshop, pp. 87–97 (2017)
7. Ridley, M.: Software tool for extraction of temporal context from text documents. In: Proceedings of the XI International Conference on Nonequilibrium Processes in Nozzles and Jets,
pp. 577–578 (2016) (in Russian)
8. DOMO Data Never Sleeps 7.0 Infographics for 2019. https://www.domo.com/learn/data-neversleeps-7. Last accessed 2020/07/04
9. Trabelsi, S., Plate, H., Abida, A., Aoun, M., Zouaoui, A., Missaoui, C., Ayari, A.: Mining
social networks for software vulnerabilities monitoring. In: 2015 7th International Conference
on New Technologies, Mobility and Security, pp. 1–7. IEEE (2015)
10. Imran, M., Castillo, C., Diaz, F., Vieweg, S.: Processing social media messages in mass
emergency: a survey. ACM Comput. Surv. 47(4), 1–38 (2015)
11. Kuzmina, N, Ridley, M.: Architecture of ontology construction and semantic search system for
enhancing civil aviation processes. Sci. Bull. State Sci. Res. Inst. Civ. Aviation 28, 103–113
(2019) (in Russian)
12. Kuzmina, N, Ridley, M.: Automatic domain ontology construction from text corpus for civil
aviation information systems. Sci. Bull. State Sci. Res. Inst. Civ. Aviation 21, 122–131 (2018)
(in Russian)
Précédent

- 349/374

Suivant