17 May 2021
less than a minute read
At Relational AI, we have built the first Relational Knowledge Graph Management System (RKGMS).
We believe relational knowledge graphs are the foundation for data-centric applications - these are systems that learn, reason, and predict over richly interconnected data.
Why Relational ? ? ?
In some of our other videos we discuss where we are today and where we will be in the future, in this video we will provide historical trends that we have witnessed that support our belief in the Relational paradigm and that a relational knowledge graph management systems is history repeating itself . . . again
Now a look back at that history.
It’s been said often that data is growing in velocity, variety, and veracity; starting with structured, adding semi structured and most recently complex (unstructured for SQL) but this data growth is part of the back story.
Everytime data has hit a growth spurt (volume) or new types have arisen (variety) the data model and approach to handle it has left relational faltering and a new temporary paradigm has come along however relational catches up replaces these temporary workarounds and creates new multi-Billion dollar industries
We believe that Relational Knowledge Graphs are the foundation of modern Data Apps and Machine Learning. The relational paradigm always wins.
It wins because it separates the WHAT from the HOW and the hard parts that the patch solutions workaround become part of the relational paradigm.
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This demo is called the “Knowledge Graph to Learn Knowledge Graphs”, or KGLKG. It parallels the authors own journey from programming in imperative languages like Java, C++, and Python to RAI's declarative language, Rel. For many years, I used those legacy languages plus SQL in an application-centric database-oriented way of solving business problems. But today I am using Rel and a data-centric business modeling approach to creating business solutions.Read More