A Simple Baseline for Inductive Knowledge Base Completion

Inductive knowledge graph completion models aim to perform link prediction on knowledge graphs with completely new entities and/or relations. The motivation and key goal of these methods is to transfer relational knowledge and patterns across disjoint knowledge graphs. Although such methods have shown promising empirical results across a variety of datasets, we argue here that these results do not provide sufficient evidence for the successful transfer of relational knowledge. We identify biases in the construction principles of available benchmark datasets, provide a simple baseline that does not rely on multi-step relational paths or more sophisticated relational patterns, and show empirically that this baseline performs on par with the state-of-the-art zero-shot model ULTRA. To provide evidence that relational knowledge transfer really takes place in inductive knowledge graph completion models, improved benchmarks and stronger empirical results are therefore required.