The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, not withstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
| Key: | BLRS11 |
| Author: | Doreen Böhnstedt, Lasse Lehmann, Christoph Rensing, Ralf Steinmetz |
| Date: | September 2011 |
| Kind: | In proceedings - use for conference & workshop papers |
| Publisher: | Springer |
| Address: | Heidelberg |
| Book title: | Towards Ubiquitious Learning, Proceedings of the 6th European Conference on Technology Enhanced Learning, EC-TEL 2011 |
| Editor: | Carlos Delgado Kloos, Denis Gillet, Raquel M. Crespo Garcia, Fridolin Wild, Martin Wolpers |
| Number: | LNCS 6964 |
| Pages: | 57-70 |
| ISBN: | 9783642239847 |
| Language: | English |
| Keywords: | Tagging, Tag Type Identification, Semantic Tagging |
| Number of characters: | 37560 |
| Research Area(s): | Knowledge Media |
| Abstract: | When users use tags they often have a rich semantic structure in mind, which can not be fully explicated using existing tagging systems. However, a tagging system needs to be simple in order to be successful, otherwise it will not be accepted by users. In our ELWMS.KOM system for the support of self-regulated Resource-Based Learning users can assign specific semantic types to the tags they use in order to manage their web-based learning resources. However studies have shown that most users would appreciate an automatic identification of tag types. In this paper we present a knowledgebased approach for the automatic identification of the tag types used in the ELWMS.KOM system. Evaluations conducted on different corpora show that the algorithm works with an overall accuracy of up to 84%. |
| URL: | http://www.springerlink.com/content/l644613v5722u807/fulltext.pdf |
If the paper is not available from this page, you might contact the author(s) directly via the "People" section on our KOM Homepage.
| |
Technische Universität Darmstadt | |