Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP applications. Recently many embedding-based approaches were proposed for cross-lingual entity alignment. However, almost all of them are based on TransE or its variants, which have been demonstrated by many studies to be unsuitable for encoding multi-mapping relations such as 1-N, N-1 and N-N relations, thus these methods obtain low alignment precision. To solve this issue, we propose a new embedding-based framework. Through defining dot product-based functions over embeddings, our model can better capture the semantics of both 1-1 and multi-mapping relations. We calibrate embeddings of different KGs via a small set of pre-aligned seeds. We also propose a weighted negative sampling strategy to generate valuable negative samples during training and we regard prediction as a bidirectional problem in the end. Experimental results (especially with the metric Hits@1) on real-world multilingual datasets show that our approach significantly outperforms many other embedding-based approaches with state-of-the-art performance.