Grakn

Figure 1 – Type inheritance modelled using Graql

Grakn is a platform for developing cognitive and other applications leveraging artificial intelligence. In order to support this, the company has developed its own declarative language, called Graql. This has a syntax that is similar to SQL but is much sparser. Like Grakn Core, Graql is open source.

There are effectively five things that you can do with Graql. Firstly, you can model your domain using an entity-relationship paradigm but including hyper-entities and hyper-relationships, as discussed above, as well as rules to build complex knowledge models. Secondly, you can model type inheritance into your knowledge model. An example of this is shown in Figure 1. Thirdly, you can define rules within your knowledge schema. Rules can be chained if required.

Fourthly, the product supports both type-based and rule-based inferencing. That is, the discovery of previously unknown relationships. In this context, it is worth commenting on one of the major differences between Graql and OWL (web ontology language). This is that changing your database schema is a major exercise when using OWL (or SQL for that matter), and while Grakn requires a schema, it has been built to be more flexible: for example, you can add/update/delete new entities, attributes and relation types in varying orders and combinations, even after data has already been loaded into the database, without breaking your existing model, which you can’t do in OWL.

Figure 2 – A clustering algorithm in Graql

Fifth, Grakn has various distributed analytic algorithms, such as community detection, built into Graql as native functionalities of the language. An example – a clustering algorithm – is provided in Figure 2. In effect, what Grakn has done here is to provide an abstraction layer that hides the complexity of deploying these algorithms. There is no need to import a relevant model, configure it or do anything else, you simply use the appropriate Graql statement. 
We are especially impressed with this feature.

Finally, and not related to Graql, there is a migration facility that Grakn uses that can help you to migrate existing, or industry-standard ontologies, into your Grakn knowledge base.

Grakn merges two hot topics: graph databases and cognitive/analytic platforms. The latter are of interest because of the increasing levels of automation they bring to the business, both in support of internal decision-making and in customer-facing applications. However, building cognitive applications is a non-trivial exercise. Just the analytics processes involved are complex. Grakn aims to make this process much simpler than would otherwise be the case, regardless of whether it is in operational environments (using inferencing) or for more complex analytics (using Graql’s abstraction layer).

Thought of as a graph database, Grakn is different from other vendors in this sector because it will never be beating its chest about how big the graphs are that it supports. This is because the use of hypergraphs will significantly reduce the sort of node and relationship proliferation that is characteristic of typical graph database products. And smaller graphs means fewer hardware resources and that, in turn, means lower costs.

The Bottom Line

Grakn is, as far as we aware, a unique product. This is both a good thing: the company has no competitors; and a bad thing: the company has to evangelise its approach, which tends to lead to long sales cycles. It is clear that the company’s success or failure will depend on whether its sales and marketing can match its technology.

Grakn is a platform for developing applications that work with complex data leveraging artificial intelligence. In order to support this, the company has developed its own declarative language, called Graql. This has a syntax that is similar to SQL but is much sparser. Like Grakn Core, Graql is open source.

Fig 02 - Defining the meaning of relationships

There are effectively five things that you can do with Graql. Firstly, you can model your domain using a concept level schema with an entity-relationship paradigm but including hyper-entities and hyper-relationships, as well as rules to build complex knowledge models. Further, you can define the meaning of the relationship your entities have with those relationships: in other words, the roles they play in them (for example, husband and wife, or employer and employee – see Figure 2). A variety of relationship styles can be modelled, including symmetric, asymmetric, reflexive, ternary and infinitary. Nested, transitive and equivalence relations are all supported as well. Moreover, the roles specified by your relationships act as constraints that prevent you from using inappropriately typed entities within them.

Secondly, you can model type inheritance into your knowledge model.  Again, an example of this is shown in Figure 2. Thirdly, you can define rules within your knowledge schema. Rules can be chained if required.

Fourthly, the product supports both type-based and rule-based inferencing (with backward chaining – the company is working on an implementation of forward chaining). This is enabled by its automated reasoning engine, which is also notable for readily handling highly permutative queries (that is, queries that deal with a significant amount of permutation).

In this context, it is worth commenting on one of the major differences between Graql and OWL (Web Ontology Language). This is that changing your database schema is a major exercise when using OWL (or SQL for that matter), and while Grakn requires a schema, it has been built to be more flexible: for example, you can add/update/ delete new entities, attributes and relation types in varying orders and combinations, even after data has already been loaded into the database, without breaking your existing model, which you can’t do in OWL.

Fifth, Grakn has various distributed analytic algorithms, such as community detection, built into Graql as native functionalities of the language. In effect, what Grakn has done here is to provide an abstraction layer that hides the complexity of deploying these algorithms. There is no need to import a relevant model, configure it or do anything else, you simply use the appropriate Graql statement. We are especially impressed with this feature.

Finally, and not related to Graql, there is a migration facility that Grakn uses that can help you to migrate existing, into your Grakn knowledge base. This is also important for companies wishing to migrate to Grakn from other RDF-based graph databases.

Grakn Labs sees its offering as providing a platform for building cognitive applications. This is otherwise a non-trivial exercise. Just the analytics processes involved are complex. Grakn aims to make this process much simpler than would otherwise be the case, regardless of whether it is in operational environments (using inferencing) or for more complex analytics (using Graql’s abstraction layer).

The degree of abstraction that Grakn and Graql provide is powerful: by taking care of low-level concepts for you, it leaves you free to operate at a much higher level and handle much more complex problems. This is particularly noticeable when it comes to building queries, where 5 lines in Graql might accomplish as much as 500 lines in SQL.

While all of the above is true we should comment that Grakn KGMS is not yet fully mature. Despite the fact that Grakn has enterprise customers the company is still in the process of optimising the product’s performance and developing its analytics capabilities at scale.

The Bottom Line

Grakn is a unique product and it provides an innovative and effective offering. In particular, its abstraction layer, together with the richness of the Graql language, removes the complexity that is often involved when using competitive products.