Mob4LOD is a framework that allows for building tailored browsers for Linked Open Data. It can be extended with custom filters and renderers to build individual browser solutions. The combination of filters and renderers can be used, e.g., for
- creating faceted views on Linked Open Data which concentrate on certain aspects of the data
- augmenting the data with external information
- providing custom visualizations, such as map and timeline views
The current version of the framework is available for download here:
There is also a tutorial which explains how to build applications with MoB4LOD:
We currently have two running prototypes based MoB4LOD:
- LODATC enables heuristic inference on Linked Open Data. It uses lazy association rule mining to infer additional missing types and mappings to other data sets on the fly, while browsing the dataset.
- Semantic Browser is a view of Linked Open Data which attempts to make sense of the inherent semantics of the data shown. For large data views, it clusters related facts in semantically coherent groups. The Semantic Browser was developed by Alexander Seeliger in the course of his Bachelor's thesis.
MoB4LOD was developed by a group of students in the course of software engineering hands-on training.
Developers: Chinara Mammadova, Dominik Wienand, Jan Stengler, and Peter Klöckner
Project Management: Melanie Weiland
Supervision: Heiko Paulheim