The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Cognitive support for ontology modeling
International Journal of Human-Computer Studies - Protégé: community is everything
A Toolkit for Addressing HCI Issues in Visual Language Environments
VLHCC '05 Proceedings of the 2005 IEEE Symposium on Visual Languages and Human-Centric Computing
Ontology visualization methods—a survey
ACM Computing Surveys (CSUR)
NodeTrix: a Hybrid Visualization of Social Networks
IEEE Transactions on Visualization and Computer Graphics
A user study on visualizing directed edges in graphs
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Topology-aware navigation in large networks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using a degree of interest model to facilitate ontology navigation
VLHCC '09 Proceedings of the 2009 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
Representation-Independent In-Place Magnification with Sigma Lenses
IEEE Transactions on Visualization and Computer Graphics
Energy-based clustering of graphs with nonuniform degrees
GD'05 Proceedings of the 13th international conference on Graph Drawing
Proceedings of the 2012 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization
A study on development of cognitive support features in recent ontology visualization tools
Artificial Intelligence Review
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Most Semantic Web data visualization tools structure the representation according to the concept definitions and interrelations that constitute the ontology's vocabulary. Instances are often treated as somewhat peripheral information, when considered at all. These instances, that populate ontologies, represent an essential part of any knowledge base, and are often orders of magnitude more numerous than the concept definitions that give them machine-processable meaning. We present a visualization technique designed to enable users to visualize large instance sets and the relations that connect them. This hybrid visualization uses both node-link and adjacency matrix representations of graphs to visualize different parts of the data depending on their semantic and local structural properties, exploiting ontological knowledge to drive the graph layout. The representation is embedded in an environment that features advanced interaction techniques for easy navigation, including support for smooth continuous zooming and coordinated views.