A list of potential projects that could use GRAKN.AI.


This page lists potential research projects that could be based upon Grakn. We have estimated the amount of work involved in them as T-shirt sizes (small, medium and large) and indicated the level of expertise we think would be required. If you are unsure of whether an idea would be suitable for you, please do talk to us. We are always happy to discuss your ideas, either based on these suggestions or for other projects. Just see our Community page for ways to get in touch with us!


The following would particularly suit an undergraduate project.

Rule-based Game Solver

Define a simple schema, which is just a specification of the components of a system and how they relate, and then devise logical rules to construct a simple application. An example may be to produce a set of rules that can validate that a Sudoku puzzle has been filled correctly.

Add a new feature to Moogi that allows users to “like” concepts. Then produce a visualisation that displays the link between the things that a user likes. For more information about Moogi, the film discovery engine that is built on Grakn, please see https://moogi.co/.

Moogi Data Cleanup

Clean Moogi data and port it to Mindmaps. This requires dabbling a bit with Gremlin, scripting, exploring the knowledge graph and ETL probably.

knowledge graph Database Pros and Cons

Write a review of knowledge graph Database technologies with pros and cons and example apps.

JavaScript Driver for Grakn

Create a prototype to interface with the Grakn REST API in a simple manner.

Create a knowledge graph Representation of a Website

Write a simple tool that follows links in a website and constructs a knowledge graph representation of it in Grakn. This could simply be pages and links, but could even pull out structural information such as containers and side panes inside pages. Follow on projects could be:

  • mapping click stream data onto the knowledge graph - and analysing
  • analysing clusters and shortest paths through the website.

Sports Results

For example, a knowledge graph of the Rio Olympics stats dataset, Premier League results etc.

Recommendation Engine

Shows how to leverage connections in your data to gather insights and start recommending currently unrelated but relevant facts.


The following would particularly suit an undergraduate project.

Game Search Engine

Develop an application that shows how a base database can excel over RDBMS for certain domains. For example, a game search engine that has features like search by: genre, ratings, price, keywords, etc. You could pick any area you are interested in (books, music, recipes - the list goes on - the main requirement is something with a lot of highly connected data).

Once this project has been tried and tested, it may be interesting to abstract it further and look at building a factory that creates the basic search engine, and can then be customised according to subject area and features.


The following would be most suitable for postgraduate investigation.

Semantic Machine Learning

Using Machine Learning to work with the semantics of the knowledge graph.

Publication Topology Based Ranking

Semantic search engine for computer science publications. How do ranking results based on topology compare to user data? Can we use an ensemble measure to get “better” results?

Life Sciences Knowledge Graph

There is potential to use the open data from Elixir and Bioschemas.org to build a life science knowledge graph.

European Broadcast Union

The European Broadcast Union are interested interested in semantics and have documented what they are pursuing here.