The Data Fabric for Machine Learning – Part 2: Building a Knowledge-Graph

I’ve been talking about the data fabric in general, and giving some concepts of Machine Learning and Deep Learning in the data fabric. And also gave my definition of the data fabric:

The Data Fabric is the platform that supports all the data in the company. How it’s managed, described, combined and universally accessed. This platform is formed from an Enterprise Knowledge Graph to create an uniform and unified data environment.

If you take a look at the definition, it says that the data fabric is formed from an Enterprise Knowledge Graph. So we better know how to create and manage it.



Set up the basis of knowledge-graphs theory and construction.


Explain the concepts of knowledge-graphs related to enterprises.
Give some recommendation about building a successful enterprise knowledge-graph.
Show examples of knowledge-graphs. 

Main theory

The fabric in the data fabric is built from a knowledge-graph, to create a knowledge-graph you need semantics and ontologies to find an useful way of linking your data that uniquely identifies and connects data with common business terms.

Section 1. What is a Knowledge-Graph?

The knowledge graph consists in integrated collections of data and information that also contains huge numbers of links between different data.

The key here is that instead of looking for possible answers, under this new model we’re seeking an answer. We want the facts — where those facts come from is less important. The data here can represent concepts, objects, things, people and actually whatever you have in mind. The graph fills in the relationships, the connections between the concepts.

In this context we can ask this question to our data lake:

What exists here?

We are in a different here. A one where it’s possible to set up a framework to study data and its relation to other data. In a knowledge-graph information represented in a particular formal ontology can be more easily accessible to automated information processing, and how best to do this is an active area of research in computer science like data science.

All data modeling statements (along with everything else) in ontological languages and the world of knowledge-graphs for data are incremental, by their very nature. Enhancing or modifying a data model after the fact can be easily accomplished by modifying the concept.

With a knowledge-graph what we are building is a human-readable representation of data that uniquely identifies and connects data with common business terms. This “layer” helps end users access data autonomously, securely and confidently.


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