The Open UniversitySkip to content
 

Semantic annotation of multilingual learning objects based on a domain ontology

Knoth, Petr (2009). Semantic annotation of multilingual learning objects based on a domain ontology. In: Doctoral consortium Workshop at The Fourth European Conference on Technology Enhanced Learning (EC-TEL 2009), 29 Sep - 02 Oct 2009, Nice, France.

Full text available as:
[img]
Preview
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (293Kb)
URL: http://ectel09.org/adc.html
Google Scholar: Look up in Google Scholar

Abstract

One of the important tasks in the use of learning resources in e-learning is the necessity to annotate learning objects with appropriate metadata. However, annotating resources by hand is time consuming and difficult. Here we explore the problem of automatic extraction of metadata for description of learning resources. First, theoretical constraints for gathering certain types of metadata important for e-learning systems are discussed. Our approach to annotation is then outlined. This is based on a domain ontology, which allows us to annotate learning resources in a language independent way.We are motivated by the fact that the leading providers of learning content in various domains are often spread across countries speaking different languages. As a result, cross-language annotation can facilitate accessibility, sharing and reuse of learning resources.

Item Type: Conference Item
Copyright Holders: 2009 Springer-Verlag
Academic Unit/Department: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 24744
Depositing User: Kay Dave
Date Deposited: 23 Nov 2010 11:03
Last Modified: 04 Aug 2016 16:33
URI: http://oro.open.ac.uk/id/eprint/24744
Share this page:

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

▼ Automated document suggestions from open access sources

Actions (login may be required)

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340   general-enquiries@open.ac.uk