Recent knowledge graph (KG) embedding methods explore parameter-efficient representations for large-scale KGs. These techniques learn entity representation using a fixed size vocabulary. Such a vocabulary consists of all the relations and a small subset of the full entity set, referred to as anchors. An entity is hence expressed as a function of reachable anchors and immediate relations. The performance of these methods is, therefore, largely dependent on the entity tokenization strategy. Especially in inductive settings, the representation capacity of these embeddings is limited due to the absence of anchor entities, as unseen entities have no connection with training graph entities. In this work, we propose a novel entity tokenization strategy that tokenizes an entity into a set of anchors based on relation similarity and relational paths. Our model MH-RARe overcomes the challenge of unseen entities not being directly connected to the anchors by selecting informative anchors from the training graph using relation similarity. Experiment results show that our model outperforms the baselines on multiple datasets for inductive knowledge graph completion task, attaining upto 5% improvement, while maintaining parameter efficiency.