Cross-lingual entity alignment algorithms are designed to look for identical real-world objects in multilingual knowledge graphs. This problem occurs, for example, when searching for drugs manufactured in different countries under different names, or when searching for imported equipment. At the moment, there are several open-source libraries that collect implementations of entity alignment algorithms as well as test data sets for various languages. This paper describes experiments with several popular entity alignment algorithms applied to a Russian-English dataset. In addition to translating entity names from Russian to English, experiments on combining the various generators of entity name embeddings with the various generators of relational information embeddings have been conducted. In order to obtain more detailed information about the results of the EA approaches, an assessment by entity types, the number of relationships and attributes have been made. These experiments allowed us to significantly improve the accuracy of several EA algorithms on the English-Russian dataset.