Visualizing RDF Schema inferencing through Neo4J, Tinkerpop, Sail and Gephi by Dave Suvee.
From the post:
Last week, the Neo4J plugin for Gephi was released. Gephi is an open-source visualization and manipulation tool that allows users to interactively browse and explore graphs. The graphs themselves can be loaded through a variety of file formats. Thanks to Martin Škurla, it is now possible to load and lazily explore graphs that are stored in a Neo4J data store.
In one of my previous articles, I explained how Neo4J and the Tinkerpop framework can be used to load and query RDF triples. The newly released Neo4J plugin now allows to visually browse these RDF triples and perform some more fancy operations such as finding patterns and executing social network analysis algorithms from within Gephi itself. Tinkerpop’s Sail Ouplementation also supports the notion of RDF Schema inferencing. Inferencing is the process where new (RDF) data is automatically deducted from existing (RDF) data through reasoning. Unfortunately, the Sail reasoner cannot easily be integrated within Gephi, as the Gephi plugin grabs a lock on the Neo4J store and no RDF data can be added, except through the plugin itself.
Being able to visualize the RDF Schema reasoning process and graphically indicate which RDF triples were added manually and which RDF data was automatically inferred would be a nice to have. To implement this feature, we should be able to push graph changes from Tinkerpop and Neo4J to Gephi. Luckily, the Gephi graph streaming plugin allows us to do just that. In the rest of this article, I will detail how to setup the required Gephi environment and how we can stream (inferred) RDF data from Neo4J to Gephi.
Visual is good!
Visual display and exploration of graphs is better!
Visual display and exploration of Neo4j data stores from within Gephi is the best!
Dave concludes:
With just a few lines of code we are able to stream (inferred) RDF triples to Gephi and make use of its powerful visualization and analysis tools to explore and inspect our datasets. As always, the complete source code can be found on the Datablend public GitHub repository. Make sure to surf the internet to find some other nice Gephi streaming examples, the coolest one probably being the visualization of the Egyptian revolution on Twitter.
Other suggestions for Gephi streaming examples?