🏆 Inductive Link Prediction Challenge 2022

Team PyKEEN launches an open Inductive Link Prediction Challenge (ILPC 2022) for Knowledge Graphs to streamline community efforts in developing inductive graph representation learning methods.

For years, link prediction in KGs was exclusively done in the transductive setup, i.e., when training and inference is performed on the same graph and one could train a shallow entity embedding matrix. What do you do if your graph gets updated? Usually, retrain the whole pipeline. The emergence of GNNs paved a way for inductive models that do not necessarily need trainable entity embeddings to perform standard graph tasks.

In the inductive setup, training and inference graphs are disjoint - having trained a model on a training graph, participants are asked to predict links over a new unseen inference graph. This renders shallow embeddings from the training graph rather useless - you can’t make use of them in the new disconnected graph. Hence, we need better ways to obtain entity embeddings that would work for unseen nodes as well as for seen trainable ones. Looks like a job for GNNs, right?

The challenge offers two new inductive link prediction datasets - small and large - where the larger one is challenging even for modern GNNs; two baselines; a standardized evaluation protocol; and a codebase to start from.

More details on the inductive setup and submission details:

- Medium blog post
- Official Github repo
- arxiv pre-print