After carefully listening to your and the community’s feedback, we’ve decided to make some drastic improvements to our documentation platform. I’m very excited to share this with you!

A technology as essential as Grakn needs to be thoroughly documented. That means that you, or any new Grakn developer, must be taken on a journey where you can learn about all the capabilities and use cases involved in building a Grakn knowledge graph. …


Ever since we’ve been able to sequence proteins, three-dimensional structures have received a tremendous experimental attention. Thanks to the development of new methods and technological advancements, determining these structures has become a more accurate and progressive process over time.

The problem, however, lays in the fact that the progress of discovering new protein structures has not kept pace with the rate at which new sequences are being produced. As a result, we see a continuously growing gap between the number of new sequences being produced and the three-dimensional structures being identified.

Given sufficient accuracy, a possible solution is the computational…


This tutorial may be out of date against the latest version of Grakn. For the most up-to-date version of this tutorial, please refer to the Grakn Documentation.

This tutorial illustrates how a dataset in CSV, JSON or XML format can be migrated into a Grakn knowledge graph, using Grakn’s Java Client.

The knowledge graph that we’ll work on in this post, is called phone_calls. The schema for this knowledge graph was defined in a previous post, here.

If you’re already familiar with Grakn, and all you need is a migration example to follow, you’ll find this Github repository useful. …


The sequencing of proteins and DNA has arguably become one of the biggest biological evolutions in the last 50 years. It has enabled researchers to produce sequence alignments that can be precisely analysed for discovering meaningful and evolutionary relationships.

The Basic Local Alignment Tool (BLAST) has become the go-to place for many bioinformaticians who routinely search for sequence alignments, as part of their research workflow. The flexibility of its search algorithms and the reliable output produced by them are known to be the main reasons for BLAST’s ever-growing popularity.

There are many articles that delve deep into explaining the…


This tutorial may be out of date against the latest version of Grakn. For the most up-to-date version of this tutorial, please refer to the Grakn Documentation.

This tutorial illustrates, using the Grakn Python Client:

  • First: how to migrate a dataset in CSV, JSON or XML format into a Grakn knowledge graph.
  • Next: how to query our newly created knowledge graph to gain interesting insights over an example dataset.

The knowledge graph that we will work on in this post, is called phone_calls. The schema for this knowledge graph was defined in a previous post, here.

For the experienced engineer

If all you need is a good example that shows how migrating data into Grakn works, you’ll find what you’re looking for here.

The Step by Step Guide

If you’d like to follow this tutorial step…


This tutorial may be out of date against the latest version of Grakn. For the most up-to-date version of this tutorial, please refer to the Grakn Documentation.

This tutorial illustrates, using the Grakn Node.js Client:

  • First: how to migrate a dataset in CSV, JSON or XML format into a Grakn knowledge graph.
  • Next: how to query our newly created knowledge graph to gain interesting insights over an example dataset.

The knowledge graph that we will work on in this post, is called phone_calls, The schema for this knowledge graph was defined in a previous post, here.

For the experienced engineer

If all you need is a good example that shows how migrating data into Grakn works, you’ll find what you’re looking for here.

The Step by Step Guide

If you’d like to follow this tutorial step…


This tutorial may be out of date against the latest version of Grakn. For the most up-to-date version of this tutorial, please refer to the Grakn Documentation.

In this tutorial, our aim is to write a schema and load it into our knowledge graph; phone_calls. One that describes the reality of our dataset.

The Dataset

First off, let’s look at the dataset we are going to be working with. Simply put, we’re going to have:

people who call each other. Those who make calls, have a contract with company “Telecom”.

People, calls, contracts and companies. That’s what we are dealing with. But what do we want to get out of this data?

The insights

The below insights will give us a better perspective of what else needs to be included in…

Soroush Saffari

Solution Architect at GRAKN.AI

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