In 1999, the Semantic Web was proposed as an extension to the World Wide Web in which information should be represented as semantic data with a clearly defined meaning. Accordingly, in the last years more and more information has been made available as semantic data with the DBpedia project being one of the most popular examples. DBpedia tries to extract semantic data from Wikipedia articles and to make them publicly available. The purely automated use of this semantic data, however, reaches its limit, since the communication between computer and user in the Semantic Web is difficult. On the one hand, queries that are formulated in natural languages can be ambiguous and therefore can be hard to interpret by computers, but on the other hand, being able to formulate queries in artificial languages like SPARQL cannot be expected from average users. Therefore, appropriate interactive tools are needed that allow a simple formulation of uniquely defined queries as well as the exploration and visualization of semantic data. "Interaction in the Semantic Web" is therefore a highly topical subject and also particular relevant to the success of the Semantic Web research area. In the following, we present some research on this subject.
Discovering relationships with the RelFinder
Are you interested in how things are related with each other? The RelFinder helps to get an overview: It extracts and visualizes relationships between given objects in datasets and makes these relationships interactively explorable..
More information about the RelFinder, as e.g. an interactive demo and scientific publications, can be found at: http://relfinder.semanticweb.org/
Complex semantic querying made easy with gFacet
gFacet represents facets as nodes in a graph visualization that can be interactively added and removed by the users in order to produce individual search interfaces.
More information about gFacet, as e.g. an interactive demo and scientific publications, can be found at: http://gFacet.semanticweb.org/
Analyzing trends with SemLens
Do you want to analyze trends and correlations in RDF data?
SemLens provides a visual interface that combines a scatter plot and semantic lenses in order to understand both global trends and local contexts.
More information about SemLens, as e.g. an interactive demo and scientific publications, can be found at: http://SemLens.visualdataweb.org/
Hierarchical faceted exploration with tFacet
tFacet applies known interaction concepts to allow hierarchical faceted exploration of RDF data. The aim is to facilitate ordinary users to formulate semantically unambiguous queries so as to support the fast and precise access to information.
More information about tFacet, as e.g. an interactive demo and scientific publications, can be found at: http://tFacet.visualdataweb.org/
Visualizing shared properties with the ChainGraph
Common graph visualizations tend to produce edge crossings and overlaps when used to display resource collections that are highly interrelated via shared properties. With ChainGraph we present a new approach that visualizes resources and their shared properties in chains to prevent dense graphs and to better support the exploration of relationships.
A detailed description of the ChainGraph can be found in the following publications:
ChainGraph: A New Approach to Visualize Shared Properties in Resource Collections.
In: K. Tochtermann and H. Maurer, editors, Proceedings of the 9th International Conference on Knowledge Management and Knowledge Technologies (I-KNOW 08), pages 106-114. J.UCS, Graz, 2009.
Philipp Heim and Steffen Lohmann.
Exploring Relationships between Annotated Images with the ChainGraph Visualization.
In: Proceedings of the 4th International Conference on Semantic and Digital Media Technologies (SAMT 2009), pages 16-27. Springer, Berlin, Heidelberg, 2009.
Steffen Lohmann, Philipp Heim, Lena Tetzlaff, Thomas Ertl and Jürgen Ziegler.