Using Linked Data to Annotate and Search Educational Video Resources for Supporting Distance Learning

Journal article

Yu, H., Pedrinaci, C., Dietze, S. and Domingue, J. 2012. Using Linked Data to Annotate and Search Educational Video Resources for Supporting Distance Learning. IEEE Transactions on Learning Technologies. 5 (2), pp. 130-142. https://doi.org/10.1109/tlt.2012.1
AuthorsYu, H., Pedrinaci, C., Dietze, S. and Domingue, J.
Abstract

Multimedia educational resources play an important role in education, particularly for distance learning environments. With the rapid growth of the multimedia web, large numbers of educational video resources are increasingly being created by several different organizations. It is crucial to explore, share, reuse, and link these educational resources for better e-learning experiences. Most of the video resources are currently annotated in an isolated way, which means that they lack semantic connections. Thus, providing the facilities for annotating these video resources is highly demanded. These facilities create the semantic connections among video resources and allow their metadata to be understood globally. Adopting Linked Data technology, this paper introduces a video annotation and browser platform with two online tools: Annomation and SugarTube. Annomation enables users to semantically annotate video resources using vocabularies defined in the Linked Data cloud. SugarTube allows users to browse semantically linked educational video resources with enhanced web information from different online resources. In the prototype development, the platform uses existing video resources for the history courses from the Open University (United Kingdom). The result of the initial development demonstrates the benefits of applying Linked Data technology in the aspects of reusability, scalability, and extensibility.

Keywordsdistance learning; educational video resources; semantic web; linked data
Year2012
JournalIEEE Transactions on Learning Technologies
Journal citation5 (2), pp. 130-142
PublisherIEEE Xplore
ISSN1939-1382
Digital Object Identifier (DOI)https://doi.org/10.1109/tlt.2012.1
Web address (URL)https://ieeexplore.ieee.org/document/6165257
Output statusPublished
Publication datesApr 2012
Publication process dates
Deposited24 Jun 2022
Permalink -

https://repository.derby.ac.uk/item/97574/using-linked-data-to-annotate-and-search-educational-video-resources-for-supporting-distance-learning

Log in to edit
  • 35
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics
AlZoubi, A., Eskandari, A., Yu, H. and Du, H. 2024. Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics . Bioengineering. 11 (5), pp. 1-23. https://doi.org/10.3390/bioengineering11050453
An Experimental Study of Integrating Fine-tuned LLMs and Prompts for Enhancing Mental Health Support Chatbot System
Yu, H. and McGuinness, S. 2024. An Experimental Study of Integrating Fine-tuned LLMs and Prompts for Enhancing Mental Health Support Chatbot System. Journal of Medical Artificial Intelligence. pp. 1-16. https://doi.org/10.21037/jmai-23-1
Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network
Tao Li, Jiawei Yang, Chenxi Li, Lulu Lv, Kang Liu, Zhipeng Yuan, Youyong Li, Hongqing Yu and Yu, H. 2024. Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network. International Workshop on Internet of Things of Big Data for Healthcare. Springer. https://doi.org/10.1007/978-3-031-52216-1_4
Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare
Yu, H., Adebola Alaba and Ebere Eziefuna 2024. Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare. International Workshop on Internet of Things of Big Data for Healthcare. Springer. https://doi.org/10.1007/978-3-031-52216-1_2
Attention Enhanced Siamese Neural Network for Face Validation
Yu, H. 2023. Attention Enhanced Siamese Neural Network for Face Validation. Artificial Intelligence and Applications. 2 (1), pp. 21-27. https://doi.org/10.47852/bonviewAIA32021018
IoTBDH-2023: The 5th International Workshop on Internet of Things of Big Data for Healthcare
Qi, J., Yu, H., Yang, P., Yang, Y. and Pang, Z. 2023. IoTBDH-2023: The 5th International Workshop on Internet of Things of Big Data for Healthcare. 32nd ACM International Conference on Information and Knowledge Management (CIKM’23), Birmingham, UK. ACM. https://doi.org/10.1145/3583780.3615299
AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research
Yu, H., O'Neill, S. and Kermanizadeh, A. 2023. AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research. Bioengineering. 10 (10), pp. 1-18. https://doi.org/10.3390/bioengineering10101134
Learning Disease Causality Knowledge from Web of Health Data
Yu, H. and Reiff-Marganiec, S. 2022. Learning Disease Causality Knowledge from Web of Health Data. International journal on semantic web and information systems. 18 (1), pp. 1-19. https://doi.org/10.4018/IJSWIS.297145
Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning
Yu, H. 2020. Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning. IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE Xplore. https://doi.org/10.1109/iccsnt47585.2019.8962507
Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System
Yu, H. 2020. Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System. in: Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3 New York Springer.
Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System
Handscombe, J. and Yu, H. 2019. Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System. Sensors. 19 (2), p. 347. https://doi.org/10.3390/s19020347
Semantic Lifting and Reasoning on the Personalised Activity Big Data Repository for Healthcare Research
Yu, H. and Dong, F. 2019. Semantic Lifting and Reasoning on the Personalised Activity Big Data Repository for Healthcare Research. International Journal of Web Engineering and Technology. 14 (2), pp. 103 - 121.
Mining Symptom and Disease Web Data with NLP and Open Linked Data
Yu, H. 2019. Mining Symptom and Disease Web Data with NLP and Open Linked Data. 5th World Congress on Electrical Engineering and Computer Systems and Sciences (EECSS’19) Lisbon, Portugal – August, 2019. https://doi.org/10.11159/mvml19.108
A linear logic approach to the composition of RESTful web services
Zhao, X., Liu, E., Yu, H. and Clapworthy, G.J. 2015. A linear logic approach to the composition of RESTful web services. International Journal of Web Engineering and Technology. 10 (3), pp. 245-271. https://doi.org/10.1504/ijwet.2015.072348
Socio-semantic Integration of Educational Resources - the Case of the mEducator Project
Dietze, Stefan, Kaldoudi, Eleni, Dovrolis, Nikolas, Giordano, Daniela, Spampinato, Concetto, Hendrix, Maurice, Protopsaltis, Aristidis, Taibi, v and Yu, H. 2013. Socio-semantic Integration of Educational Resources - the Case of the mEducator Project. Journal of Universal Computer Science. 19 (11), pp. 1-27. https://doi.org/10.3217/jucs-019-11-1543
Interlinking educational resources and the web of data
Dietze, S., Sanchez‐Alonso, S., Ebner, H., Yu, H., Giordano, D., Marenzi, I. and Pereira Nunes, B. 2013. Interlinking educational resources and the web of data. Program. 47 (1). https://doi.org/10.1108/00330331211296312
An automated approach to Semantic Web Services Mediation
Dietze, S., Gugliotta, A., Domingue, J., Yu, H. and Mrissa, M. 2010. An automated approach to Semantic Web Services Mediation. Service Oriented Computing and Applications. 4, p. 261–275. https://doi.org/10.1007/s11761-010-0070-7