Getting Started with Jupyter+IntelligentGraph

Since IntelligentGraph combines Knowledge Graphs with embedded data analytics, Jupyter is an obvious choice as a data analysts’ IntelligentGraph workbench. 

The following are screen-captures of a Jupyter-Notebook session showing how Jupyter can be used as an IDE for IntelligentGraph to perform all of the following:

  • Create a new IntelligentGraph repository
  • Add nodes to that repository
  • Add calculation nodes to the same repository
  • Navigate through the calculated results
  • Query the results using SPARQL

GettingStarted is available as a JupyterNotebook here: GettingStarted JupyterNotebook

Images of the GettingStarted JupyterNotebook follow:



Using the Jupyter ISparql, we can easily perform SPARQL queries over the same IntelligentGraph created above. The notebook is available here:


GettingStarted Using SPARQL

We do not have to use Java to script our interaction with the repository. We can always use SPARQL directly as described by the following Jupyter Notebook


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