Jupyter+IntelligentGraph=Graph Data Analyst Workbench

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

This document is available for download here: GettingStarted

SPARQLing

Using the Jupyter ISparql, we can easily perform SPARQL queries over the same IntelligentGraph created above. The notebook is available here:[google-drive-embed url="https://drive.google.com/uc?id=1XUEb-7qp0iwpRcxrjaFmgIpQBFIHkQUp&export=download" title="GettingStartedSPARQL.ipynb" icon="https://drive-thirdparty.googleusercontent.com/16/type/application/octet-stream" style="download"]

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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