Inductive link prediction aims to predict missing triplets involving unseen entities in Knowledge Graphs (KGs). Despite advancements in this field, inferring comparative relationships for newly emerged entities with numeric attributes remains challenging, primarily due to variations in numeric distributions. Moreover, the inherent asymmetric nature of numeric comparison relations (e.g., “is_taller_than”) complicates the direct application of existing inductive and numeric encoding models, which often fail to distinguish between such asymmetric pairs. To address these challenges, we propose Relation-aware Relative Numeric Encoding (RRNE), a novel approach enhancing inductive numerical reasoning. Our method effectively mitigates the aforementioned challenges by computing relative differences with respect to the target triplet. As a result, entity representations become more robust in an unseen numerical setting, and they effectively capture the asymmetry inherent in numeric comparison relations. We conduct extensive experiments on three benchmark datasets—Credit, Spotify, and US-Cities—for inductive numerical reasoning. Our empirical results demonstrate that RRNE significantly outperforms existing baselines, achieving enhancements of up to 24.81% in AUC-PR and 20.69% in AUC. These outcomes validate the effectiveness of our approach in tackling inductive numerical reasoning.