To the Point: A Shortcut to Essential Learning

The volume of information on the Web is constantly growing. Consequently, finding specific pieces of information becomes a harder task. Wikipedia, the largest online reference Website is beginning to witness this phenomenon. Learners often turn to Wikipedia in order to learn facts regarding different subjects. However, as time passes, Wikipedia articles get larger and specific information gets more difficult to be located. In this work, we propose an automatic annotation method that is able to precisely assign categories to any textual resource. Our approach relies on semantic enhanced annotations and Wikipedia’s categorization schema. The results of a user study shows that our proposed method provides solid results for classifying text and provides a useful support for locating information. As implication, our research will help future learners to easily identify desired learning topics of interest in large textual resources.

Authors: Ricardo Kawase, Patrick Siehndel and Bernardo Pereira Nunes

PDF: kawase-icalt2014

Generating resource profiles by exploiting the context of social annotations

Fabian Abel presenting @ISWC2011

Typical tagging systems merely capture that part of the tagging interactions that enrich the semantics of tag assignments according to the system’s purposes. The common practice is to build tag-based resource or user profiles on the basis of statistics about tags, disregarding the additional evidence that pertain to the resource, the user or the tag assignment itself. Thus, the main bulk of this valuable information is ignored when generating user or resource profiles.

In this work, we formalize the notion of tag-based and context-based resource profiles and introduce a generic strategy for building such profiles by incorporating available context information from all parts involved in a tag assignment. Our method takes into account not only the contextual information attached to the tag, the user and the resource, but also the metadata attached to the tag assignment itself. We demonstrate and evaluate our approach on two different social tagging systems and analyze the impact of several context-based resource modeling strategies within the scope of tag recommendations. The outcomes of our study suggest a significant improvement over other methods typically employed for this task.

Venue: ISWC2011

Presentation: video and slides (by Fabian Abel)

Authors: Ricardo Kawase, George Papadakis, Fabian Abel

PDF: kawase.iswc2011

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