Model of Taxonomy for Accessibility Ontology

Authors

  • Galina Bogdanova Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences, 8, G. Bonchev Str., Sofia, 1113, Bulgaria
  • Todor Todorov Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences, 8, G. Bonchev Str., Sofia, 1113, Bulgaria; St. St. Cyril and Methodius University of Veliko Tarnovo, 2, T. Turnovski Str., Veliko Tarnovo, 5003, Bulgaria
  • Nikolay Noev Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences, 8, G. Bonchev Str., Sofia, 1113, Bulgaria

DOI:

https://doi.org/10.55630/dipp.2023.13.6

Keywords:

Accessibility, Semantic Knowledge, Taxonomy

Abstract

Digital accessibility to educational and informational resources for people with disabilities is an important part of contemporary life. Semantic organization of knowledge in the domain of accessibility provides a convenient way of extracting and organizing data. The paper focuses on semantic design techniques and related taxonomy models that could be used in the digitization process of resources intended of people with disabilities usage.

References

Basel, A., Bataineh, E., & Kamoun, F. (2013). E-Government Web Accessibility: WCAG 1.0 versus WCAG 2.0 Compliance. International Journal of Digital Information and Wireless Communications (IJDIWC) 3 (4), 56-65.

Bogdanova, G., Todorov, T., & Noev, N. (2017). Creating and representing semantic knowledge of bell objects. International Journal of Applied Engineering Research, (19), 8986 – 8994.

Data Taxonomy. (n.d.). Retrieved from Data. NSW: https://data.nsw.gov.au/IDMF/data-structure-and-coordination/data-taxonomy

Engelhardt, M., Gluszak, B., Kosiedowski, M., Kramer, T., & Urbanski, J. (2019). Global Atlas of People with Profound Intellectual and Multiple Disabilities. Journal on Technology and Persons with Disabilities, , 106-119. http://hdl.handle.net/10211.3/210394.

Jęśko, W. (2021). Vocalization Recognition of People with Profound Intellect ual and Multiple Disabilities (PIMD) Using Machine Learning Algorithms. In Proc. Interspeech 2021 (pp. 2921-2925). https://doi.org/10.21437/Interspeech.2021-1239.

Masuwa-Morgan, K. R. (2008). Introducing AccessOnto: Ontology for Accessibility Requirements Specification. In First International Workshop on Ontologies in Interactive Systems, (pp. pp. 33-38). https://doi.org/10.1109/ONTORACT.2008.18.

Nalepa, G. J., Bobek, S., Kutt, K., & Atzmueller, M. (2021). Semantic Data Mining in Ubiquitous Sensing: A Survey. Sensors. 21(13): 4322 . DOI:10.3390/s21134322.

Paciello, M. (2000). Web accessibility for people with disabilities. Crc Press.

Paneva-Marinova, D., Stoikov, J., Goynov, M., Luchev, D., Pavlov, R., & Pavlova, L. (2019). Intelligent Data Curation in Virtual Museum for Ancient History and Civilization. Digital Presentation and Preservation of Cultural and Scientific Heritage, 9 , 131–144.

Sirichanya, C., & Kesorn, K. (2021). Semantic data mining in the information age: A systematic review. International Journal of Intelligent Systems, 36 , 3880 - 3916.

Todericiu, I.- A., Şerban, C., & Dioşan, L. (2021). Towards Accessibility in Education through Smart Speakers. An ontology based approach. Procedia Computer Science, , 883-892.

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Published

2023-09-01

How to Cite

Bogdanova, G., Todorov, T., & Noev, N. (2023). Model of Taxonomy for Accessibility Ontology. Digital Presentation and Preservation of Cultural and Scientific Heritage, 13, 69–74. https://doi.org/10.55630/dipp.2023.13.6

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