Knowledge graphs (KGs) represent heterogeneous data as triples, including relational triples between entities and attribute triples linking entities to literal values like labels or numbers. Integrating multiple KGs is an essential task for maximizing data utility in downstream tasks. However, the integration process is challenging due to common issues such as noise, incompleteness, and varying coverages, which complicate accurate entity linking and can lead to misclassifications. As the size and the number of available datasets continues to grow, automated approaches to KG integration are indispensable for scalability. Existing methods often rely on KG embedding techniques and struggle when confronted with noisy or contradictory literal data. In this paper, we present Conel, a novel approach for unsupervised link discovery that leverages literal similarities and employs contrastive and entropy based scoring for robust entity matching. Our experiments show that Conel outperforms all baselines on 5 out of 7 datasets exhibiting robustness to the adverse conditions typical in real-world KG linking.