Annotation Tool for Enhancing E-Learning Courses

One of the most popular forms of learning is through reading and for years we have used hard copy documents as the main material to learn. With
the advent of the Internet and the fast development of new technologies, new tools have been developed to assist the learning process. However,
reading is still the main learning method that is an individual activity. In this paper we propose a highlighting tool that enables the reading
and learning process to become a collaborative and shared activity. In other words, the highlighting tool supports the so-called active-reading,
a well-known and efficient means of learning. The highlighting tool brings to the digital environment the same metaphor of the traditional
highlight marker and puts it in a social context. It enables users to emphasize certain portions of digital learning objects. Furthermore, it
provides students, tutors, course coordinators and educational institutions new possibilities in the teaching and learning process. In this work
we expose the first quantitative and qualitative results regarding the use of the highlight tool by over 750 students through 8 weeks of
courses.

Venue: ICWL2012

Authors:  Bernardo Pereira Nunes, Ricardo Kawase, Stefan Dietze, Gilda Helena Bernardino de Campos and Wolfgang Nejdl

TagMe!: Enhancing Social Tagging with Spatial Context

TagMe! is a tagging and exploration front-end for Flickr images, which enables users to annotate specific areas of an image, i.e. users can attach tag assignments to a specific area within an image and further categorize the tag assignments. Additionally, TagMe! automatically maps tags and categories to DBpedia URIs to clearly define the meaning. In this work we discuss the differences between tags and categories and show how both facets can be applied to learn semantic relations between concepts referenced by tags and categories. We also expose the benefits of the visual (spatial) context of the tag assignments, with respect to ranking algorithms for search and retrieval of relevant items. We do so by analyzing metrics of size and position of the annotated areas. Finally, in our experiments we compare different strategies to realize semantic mappings and show that already lightweight approaches map tags and categories with high precisions (86.85% and 93.77% respectively). The TagMe! system is currently available at http://tagme.groupme.org .

Venue: WEBIST2010 (Selected Papers)

Authors:  Fabian Abel, Nicola Henze, Ricardo Kawase, Daniel Krause and Patrick Siehndel

PDF: abel-webist-selected2010

The Impact of Multifaceted Tagging on Learning Tag Relations and Search

In this paper, we present a model for multifaceted tagging, i.e. tagging enriched with contextual information. We present TagMe!, a social tagging front-end for Flickr images, that provides multifaceted tagging functionality: It enables users to attach tag assignments to a specific area within an image and to categorize tag assignments. Moreover, TagMe! maps tags and categories to DBpedia URIs to clearly define the meaning of freely-chosen words. Our experiments reveal the benefits of these additional tagging facets. For example, the exploitation of the facets significantly improves the performance of FolkRank-based search. Further, we demonstrate the benefits of TagMe! tagging facets for learning semantics within folksonomies.

Venue: ESWC2010

Authors: Fabian Abel, Nicola Henze, Ricardo Kawase and Daniel Krause

PDF: abel-eswc2010

Leveraging multi-faceted tagging to improve search in folksonomy systems

In this paper we present ranking algorithms for folksonomy systems that exploit additional contextual information attached to tag assignments available. We evaluate the algorithms in the TagMe! system, a tagging front-end for Flickr, and show that our algorithms, which exploit categories, spatial information, and URIs describing the semantics of tag assignments, perform significantly better than the FolkRank that does not consider such contextual information.

Venue: HT2010

Authors: Fabian Abel, Ricardo Kawase and Daniel Krause

PDF: abel-ht2010

The Art of Multi-faceted Tagging – Interweaving Spatial Annotations, Categories, Meaningful URIs and Tags

In this paper we present TagMe!, a tagging and exploration front-end for Flickr images, which enables users to attach tag assignments to a specific area within an image and to categorize tag assignments. We analyze the differences between tags and categories and show how both facets can be applied to learn semantic relations between concepts referenced by tags and categories. TagMe! automatically maps tags and categories to DBpedia URIs to clearly define the meaning. In our experiments we compare different strategies to realize such semantic mappings and show that already lightweight approaches map tags and categories with high precisions (86.85% and 93.77% respectively). We further discuss how multi-faceted tagging helps to improve the retrieval of folksonomy entities. The TagMe! system is currently available at http://tagme.groupme.org

 

Venue: WEBIST2010

Authors:  Fabian Abel, Ricardo Kawase, Daniel Krause, Patrick Siehndel and Nicole Ullmann