You can also use automated reasoning inside Grakn (machine learning)

In Grakn, distributed analytics are built-in

Start by instantiating a client and opening a session. Afterwards, you can begin to run queries

After setting up Grakn keyspace, you can interact with it using Python client API

It's good to have a single Grakn keyspace per application (outermost container for data in a Grakn knowledge graph, corresponding closely to a relational database)

After defining schema, it needs to be loaded into Grakn:

1) Place schema.gql in the container volume, such as db/schema.gql.

2) Run:

docker exec -ti grakn bash -c '/grakn-core-all-linux/grakn console --keyspace experiment --file /grakn-core-all-linux/server/db/schema.gql'

3) Observe a similar result:

Loading: /grakn-core-all-linux/server/db/schema.gql
...
{}
Successful commit: schema.gql

Attributes and entities can be abstract

Don't forget to define attribute types, such as:

name sub attribute,
    datatype string;

address sub attribute,
    datatype string;

timestamp sub attribute, abstract,
    datatype date;

    created sub timestamp;
    last-modified sub timestamp;
    last-accessed sub timestamp;
    penalty-until sub timestamp;

url sub attribute,
    datatype string;

...

Use of relations to glue different entities:

location-of-user sub relation,
    relates located-user,
    relates user-location;

achievements sub relation,
    has score,
    relates contributor,
    relates award;

Modelling separate entities:

location sub entity,
    key address,
    plays user-location;

badge sub entity,
    key color,
    plays award;

Graql is part of Grakn

In schema we can have three types of things:

• entity
• attribute
• relation

Before using JSON data in the database, we need to define its schema

You can run Grakn with Docker using 3 simple commands (check below this highlight)

Grakn:

• type of a knowledge base
• link to the developer site
• examples