
This section will provide course outline and expected outcomes. We will also discuss the use case scenario and associated design details you will submit as your class assignment and prototyping effort.
In the section, we will introduce use cases which will be used to explain Knowledge Graph schema design, data mapping, loading, exploration and query development using TigerGraph
This section provides step by step instructions on getting started with TigerGraph sandbox. Once the sandbox is set-up, it will provide you an environment for creating graph solutions.
This section will introduce TigerGraph GraphStudio. Using this graphical user interface, you can define the graph schema, ingest data stored in spreadsheets and explore it using its graphical query capabilities
As we have now setup the sandbox and have updated the sandbox with additional data, we are ready to execute queries associated with the structured graph.
Learn to convert unstructured data into structured keywords and represent them in a TigerGraph knowledge graph to enable elastic queries, including natural language queries and keyword analysis for movies.
Learn how to access and integrate a knowledge graph with applications by reviewing solution components, data stores, and runtime and training systems, and implement Python-based access to Tachograph.
Develop knowledge graph solutions for movie exploration using TigerGraph, covering cloud setup, structure and lastic graph queries, integration components, and a comprehensive homework assignment with datasets.
"Rapid Prototyping of Knowledge Graph Solutions using TigerGraph" course will help you strategize knowledge graph use cases and help you build or prototype a use case for your knowledge graph engagement. This course includes
- How to define Graph Use Case
- How to set up Sandbox using TigerGraph for your Graph use case
- How to develop and execute structured graph queries
- How to define elastic or higher level graph representation
- Finally how to connect your graph solution with other solution components using Python.