Leveraging Search and Content Exploration by Exploiting Context in Folksonomy Systems

With the advent of Web 2.0 tagging became a popular feature in social media systems. People tag diverse kinds of content, e.g. products at Amazon, music at Last.fm, images at Flickr, etc. In the last years several researchers analyzed the impact of tags on information retrieval. Most works focused on tags only and ignored context information. In this article we present context-aware approaches for learning semantics and improve personalized information retrieval in tagging systems. We investigate how explorative search, initialized by clicking on tags, can be enhanced with automatically produced context information so that search results better fit to the actual information needs of the users. We introduce the SocialHITS algorithm and present an experiment where we compare different algorithms for ranking users, tags, and resources in a contextualized way. We showcase our approaches in the domain of images and present the TagMe! system that enables users to explore and tag Flickr pictures. In TagMe! we further demonstrate how advanced context information can easily be generated: TagMe! allows users to attach tag assignments to a specific area within an image and to categorize tag assignments. In our corresponding evaluation we show that those additional facets of tag assignments gain valuable semantics, which can be applied to improve existing search and ranking algorithms significantly.

Venue: The New Review of Hypermedia and Multimedia 2010

Authors: Fabian Abel, Matteo Baldoni, Cristina Baroglio, Nicola Henze, Ricardo Kawase, Daniel Krause and Viviana Patti

PDF: abel-nrhm2010

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