Helping Courseware Authors to Build Ontologies: The Case of TM4L

Authors:
Darina Dicheva;Christo Dichev
Affiliations:
Winston-Salem State University, 601 M.L.K. Jr. Dr., Winston Salem, N.C. 27110, USA;Winston-Salem State University, 601 M.L.K. Jr. Dr., Winston Salem, N.C. 27110, USA
Venue:
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Year:
2007

Citing 5
Cited 3

Do HTML Tags Flag Semantic Content?

IEEE Internet Computing
Mining topic-specific concepts and definitions on the web

WWW '03 Proceedings of the 12th international conference on World Wide Web
Automating metadata generation: the simple indexing interface

WWW '05 Proceedings of the 14th international conference on World Wide Web
Authoring Educational Topic Maps: Can We Make It Easier?

ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Overcoming the brittleness bottleneck using wikipedia: enhancing text categorization with encyclopedic knowledge

AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2

Automated Educational Course Metadata Generation Based on Semantics Discovery

EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Ontological technologies for user modelling

International Journal of Metadata, Semantics and Ontologies
Enhancing automatic term recognition algorithms with HTML tags processing

Proceedings of the 12th International Conference on Computer Systems and Technologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

The authors of topic map-based learning resources face major difficulties in constructing the underlying ontologies. In this paper we propose two approaches to address this problem. The first one is aimed at automatic construction of a “draft” topic map for the authors to start with. It is based on a set of heuristics for extracting semantic information from HTML documents and transforming it into a topic map format. The second one is aimed at providing help to authors during the topic map creating process by mining the Wikipedia knowledge base. It suggests “standard” names for the new topics (paired with URIs), along with lists of related topics in the considered domain. The proposed approaches are implemented in the educational topic maps editor TM4L.