Most of the knowledge we possess is encoded in many ways. There are books, publications, dictionaries, standards, government regulations, company three-letter-acronyms (TLA) and so on. Also as we develop insights, we know how to connect the dots by relating seemingly disparate insights by connecting the dots.
As computers get smarter, they also need to know how to connect the dots and relate to published knowledge. Today, you can use Google to connect the dots across a large number of web pages. Graph analytics drives the most successful recommendation engines. It powers media content analysis and discovery for news, movies and speeches. How do you bring this ability to your private and public information and how do you use it for a business application?
A Graph can be designed at various levels of sophistication. We start from simple graphs connecting nodes using edges, and then gradually introduce elastic graphs, classifications, ontology and temporal reasoning to bring advanced graph concepts. We show how graphs can be used for a variety of reasoning techniques including search, discovery, and recommendation.
The course will cover many topics associated with design of a Graph Analytics Solution:
Design and analytics using structured graph
Evolution of Knowledge Graphs
Knowledge graph concepts and maturity levels
Elastic Graph design and applications
Classification Graph design and applications
Higher levels of Knowledge graph insight solutions
Components of a Graph solution and how they interact with users
Using a dataset on movies, we have created a course material to show you how to design a Graph solution which uses many Knowledge Graph techniques to mimic how we process information and develop insights by connecting the dots. The examples have been created using TigerGraph, a leading Graph database provider. We also show the system components for a graph based solution and how it integrates with data / knowledge sources, users and knowledge curators.
