How can the semantic web and ontologies help history and archeology

Book chapter

Souag, A. 2019. How can the semantic web and ontologies help history and archeology. in: Dans les dédales du web. Historiens en territoires numériques Paris Éditions de la Sorbonne.
AuthorsSouag, A.
KeywordsSemantic web; History ; Archeology
Year2019
Book titleDans les dédales du web. Historiens en territoires numériques
Publisher Éditions de la Sorbonne
Output statusPublished
Place of publicationParis
Series« Homme et société »
ISBN9791035103002
Publication process dates
Deposited23 Oct 2023
Related URLhttps://journals.openedition.org/lectures/36194
Permalink -

https://repository.canterbury.ac.uk/item/961z2/how-can-the-semantic-web-and-ontologies-help-history-and-archeology

  • 110
    total views
  • 0
    total downloads
  • 7
    views this month
  • 0
    downloads this month

Export as

Related outputs

Real-time mushroom detection and maturity classification using YOLO-Tiny on Raspberry Pi platform
Duman, S., Elewi, A. and Souag, A. 2025. Real-time mushroom detection and maturity classification using YOLO-Tiny on Raspberry Pi platform. in: Proceedings of the 9th International Symposium on Innovative Approaches in Smart Technologies (ISAS) Curran.
A hybrid machine learning approach for enhanced skin cancer diagnosis using convolutional neural networks, support vector machines, and gradient boosting
Patel, F., Souag, A. and Olowolayemo, A. 2025. A hybrid machine learning approach for enhanced skin cancer diagnosis using convolutional neural networks, support vector machines, and gradient boosting. in: Sztandera, L. (ed.) AIHealth 2025: The Second International Conference on AI-Health ThinkMind. pp. 35-51
Evaluating the impact of machine learning platforms on cancer classification model performance: A cross-platform comparative study
Olowolayemo, A., Souag, A. and Sirlantzis, K. 2024. Evaluating the impact of machine learning platforms on cancer classification model performance: A cross-platform comparative study. International Journal on Advanced in Life Sciences. 16 (no 3 & 4), pp. 96 - 111.
Machine learning in ASL fingerspelling recognition: A literature review
Pinnington, J., Souag, A. and Azhar, H. 2024. Machine learning in ASL fingerspelling recognition: A literature review. in: 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI) IEEE.
Utilising transformers for American Sign Language fingerspelling recognition
Pinnington, Jamie, Souag, A. and Azhar, H. 2024. Utilising transformers for American Sign Language fingerspelling recognition. in: 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI) IEEE. pp. 000129-000134
A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications
Duman, S., Elewi, A., Hajhamed, A., Khankan, R., Souag, A. and Ahmed, A. 2024. A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications. Data in Brief. 57. https://doi.org/10.1016/j.dib.2024.111074
Design and implementation of a cost-aware and smart oyster mushroom cultivation system
Souag, A., Elewi, A., Hajhamed, A., Khankan, R., Duman, S. and Ahmed, A. 2024. Design and implementation of a cost-aware and smart oyster mushroom cultivation system. Smart Agricultural Technology. Volume 8. https://doi.org/10.1016/j.atech.2024.100439
Cancer: Investigating the impact of the implementation platform on machine learning models
Olowolayemo, A. S., Souag, A. and Sirlantzis, K. 2024. Cancer: Investigating the impact of the implementation platform on machine learning models. in: Mengoni, M. and Souag, A. (ed.) AIHealth 2024, The First International Conference on AI-Health ThinkMind.
Why should everybody learn Artificial Intelligence?
Turner, S. and Souag, A. 2022. Why should everybody learn Artificial Intelligence? ETD blog, Canterbury Christ church University
Using the AMAN-DA method to generate security requirements: a case study in the maritime domain
Souag, A., Mazo, R., Salinesi, C. and Comyn-Wattiau, I. 2018. Using the AMAN-DA method to generate security requirements: a case study in the maritime domain. Requirements Engineering Journal. 23 (557–580). https://doi.org/10.1007/s00766-017-0279-5
Reusable knowledge in security requirements engineering: a systematic mapping study
Souag, A., Mazo, R., Salinesi, C. and Comyn-Wattiau, I. 2016. Reusable knowledge in security requirements engineering: a systematic mapping study. Requirements Engineering Journal. 21 (251–283). https://doi.org/10.1007/s00766-015-0220-8
A security ontology for security requirements elicitation
Souag, A. and Salinesi C., Mazo R., Comyn-Wattiau I. 2015. A security ontology for security requirements elicitation. https://doi.org/10.1007/978-3-319-15618-7_13
AMAN-DA: A knowledge reuse based approach for domain specific security requirements engineering
Souag, A. 2015. AMAN-DA: A knowledge reuse based approach for domain specific security requirements engineering. PhD Thesis Université Paris 1 Panthéon-Sorbonne CRI - Centre de Recherche en Informatique de Paris 1
A methodology for defining security requirements using security and domain ontologies
Souag, A., Salinesi C. and Comyn-Wattiau I. 2013. A methodology for defining security requirements using security and domain ontologies. Insight. Volume 16 (4), pp. 14-16. https://doi.org/10.1002/inst.201316414
Ontologies for security requirements: a literature survey and classification’
Souag, A. and Salinesi C., Comyn-Wattiau I. 2012. Ontologies for security requirements: a literature survey and classification’. https://doi.org/10.1007/978-3-642-31069-0_5