Grakn lets us create Knowledge Graphs from our data. But what challenges do we encounter where querying alone won’t cut it? What library can address these challenges?
Below is a set of tasks to be conducted over Knowledge Graphs (KGs) that we have identified from real Grakn use cases. The objective of KGLIB is to implement a portfolio of solutions for these tasks for Grakn Knowledge Graphs.
This project introduces a novel model: the Knowledge Graph Convolutional Network (KGCN), available free to use from the GitHub repo under Apache licensing. It’s written in Python, and available to install via pip from PyPi.
A KGCN can be used to create vector representations, embeddings, of any labelled set of Grakn Things via supervised learning.
It seems quite clear that machines should be able to outperform humans in many more tasks than they currently can, or at least that they should be able to make truly smart predictions. I’m sure we can all relate to a moment when an app made us a recommendation that didn’t make any logical sense. You only have to look as far as the recommender system that recommended the product you only just bought, or the spam filter that stole a reply from someone you messaged. In practice, we find that we can’t trust machines with decision-making on our behalf.
We know Grakn can be leveraged to model highly complex data, but how do we go about building a detailed model of a real-world system?
Here we delve in to Transport for London (TFL) data to understand and gain insights into the operation of the London Underground Network.
We go on to build surely the most desirable tool for such a network: a journey planner. (Because who doesn’t want to shave 0.3 minutes off their commute?)
In case you really…