  Rule-induction models have demonstrated great power in the inductive setting
of knowledge graph completion. In this setting, the models are tested on a
knowledge graph entirely composed of unseen entities. These models learn
relation patterns as rules by utilizing subgraphs. Providing the same inputs
with different rules leads to differences in the model's predictions. In this
paper, we focus on the behavior of such models. We propose a re-ranking-based
model called ReDistLP (Re-ranking with a Distinct Model for Link Prediction).
This model enhances the effectiveness of re-ranking by leveraging the
difference in the predictions between the initial retriever and the re-ranker.
ReDistLP outperforms the state-of-the-art methods in 2 out of 3 benchmarks.
