Knowledge graphs play a pivotal role in structuring human knowledge within artificial intelligence systems. Nonetheless, knowledge distribution is markedly uneven across languages, and linguistic community activity can hinder the performance and scale. Cross-lingual transfer learning emerges as a predominant effective strategy to surmount linguistic barriers, facilitating knowledge transfer across natural languages. This paper reviews cross-lingual knowledge acquisition for knowledge graphs, offering the first systematic integration of cross-lingual transfer paradigms and resources in this field. It critically examines the state of research across subtasks (including named entity recognition, relation extraction, coreference resolution and entity linking). Despite the advancements facilitated by multilingual word embeddings, pre-trained language models and large language models, persistent challenges such as language bias-induced alignment difficulties and low transfer efficiency continue to impede progress. Enhancing model effectiveness through both paradigms and resources will benefit the future construction of multilingual or minor-language knowledge graphs.