There are plenty of projects aimed at incorporating semantic information into present day document processing. The main problem is their real-world usability. E-learning is one of the areas which can take advantage of the semantically described documents. In this paper we would like to introduce a framework of cooperating tools which can help extract, store, visualize semantics in this area.
Authors: Maria Grineva, Maxim Grinev, Denis Turdakov, Pavel Velikhov, Alexander Boldakov
Abstract: The quality of the current tagging services can be greatly improved if the service is
able to cluster tags by their meaning. Tag clouds clustered by higher level topics enable the
users to explore their tag space, which is especially needed when tag clouds become large. We
demonstrate TagCluster – a tool for automated tag clustering that harnesses knowledge from
Wikipedia about semantic relatedness between tags and names of categories to achieve smart
clustering. Our approach shows much better quality of clusters compared to the existing
techniques that rely on tag co-occurrence analysis in the tagging service.
Keywords: Wikipedia, User-generated content analysis, network analysis, tagging
Categories: H.3.3, H.3.5
Authors: Steffen Lohmann, Stefan Thalmann, Andreas Harrer, Ronald Maier
Abstract: The importance of comprehensive annotations for learning resources is widely
recognized. However, it is often unclear how these annotations should be created. A promising
solution might be that learners annotate learning resources during execution of learning
processes. This paper examines tagging as a learner-driven approach that can be used to get
annotations on a collaborative level. The characteristics of learner-generated annotations in
learning scenarios and the applicability of these annotations to describe learning resources are
investigated. As empirical grounding, the results of three independent experiments are
presented in order to inform decisions concerning the establishment of institutionalized settings
deploying tagging approaches.
Keywords: annotation, learning resources, tagging, metadata, collaboration
Categories: L6.2, L6.1, L3.6, L3.5, L1.2
Authors: Martin Memmel, Michael Kockler and Rafael Schirru
Abstract: In today’s information environments, tagging is widely used to provide information
about arbitrary types of digital resources. This information is created by end
users with different motivations and for different kinds of purposes. When aiming to
support users in the tagging process, these differences play an important role. This paper
discusses several approaches to generate tag recommendations, and a prototypical
recommender system for the social resource sharing platform ALOE will be presented.
This interactive system allows users to control the generation of the recommendations
by selecting the sources to be used as well as their impact. The component was introduced
at DFKI, and a first evaluation showed that the recommender component was
considered as helpful by a majority of users.
Key Words: classification, collaborative tagging, digital resources, knowledge management,
knowledge sharing, metadata, recommender, tagging, web 2.0
Category: H.3.0, H.3.2, H.3.3, H.3.4, H.3.5, H.5.0, H.5.1, H.5.2
Authors: Ralf Klamma, Yiwei Cao, Anna Glukhova, Andreas Hahne, and Dominik Renzel
Abstract: Story-telling has been a means of bequeathing knowledge since aeons.
Whereas the idea of the story-telling remains unaltered, the world around undertakes
continuous changes. New media, new technologies and devices, new ways of communication
define a new format of the story-telling. This paper proposes a new non-linear
version of mobile story-telling in the emerging ubiquity of knowledge sharing. To support
our concept we provide a description of multimedia services based on the MPEG-7
metadata standard used for our non-linear mobile story-telling environment.
Key Words: Multimedia, MPEG-7, Tagging, Story-telling, Mobile Services
Category: H.3.3, H.3.4, H.3.5, H.5.1, H.5.2, H.5.4
Authors: Benjamin Adrian, Leo Sauermann, Thomas Roth-Berghofer
Abstract: ConTag is an approach to generate semantic tag recommendations for documents
based on Semantic Web ontologies and Web 2.0 services. We designed and implemented
a process to normalize documents to RDF format, extract document topics
using Web 2.0 services and finally match extracted topics to a Semantic Web ontology.
Due to ConTag we are able to show that the information provided by Web 2.0 services
in combination with a Semantic Web ontology enables the generation of relevant semantic
tag recommendations for documents. The main contribution of this work is a
semantic tag recommendation process based on a choreography of Web 2.0 services.
Key Words: Ontology, Web 2.0, Semantic Web, Social Software, Tagging
Category: H.1.1, H.3.3
