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An emerging trend in representation learning over knowledge graphs (KGs)moves beyond transductive link prediction tasks over a fixed set of knownentities in favor of inductive tasks that imply training on one graph andperforming inference over a new graph with unseen entities. In inductivesetups, node features are often not available and training shallow entityembedding matrices is meaningless as they cannot be used at inference time withunseen entities. Despite the growing interest, there are not enough benchmarksfor evaluating inductive representation learning methods. In this work, weintroduce ILPC 2022, a novel open challenge on KG inductive link prediction. Tothis end, we constructed two new datasets based on Wikidata with various sizesof training and inference graphs that are much larger than existing inductivebenchmarks. We also provide two strong baselines leveraging recently proposedinductive methods. We hope this challenge helps to streamline community effortsin the inductive graph representation learning area. ILPC 2022 follows bestpractices on evaluation fairness and reproducibility, and is available athttps://github.com/pykeen/ilpc2022.

An Open Challenge for Inductive Link Prediction on Knowledge Graphs