Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the sequence-to-sequence framework for building knowledge graphs, which is flexible and can be adapted to widespread tasks. In this study, we summarize the recent compelling progress in generative knowledge graph construction. We present the advantages and weaknesses of each paradigm in terms of different generation targets and provide theoretical insight and empirical analysis. Based on the review, we suggest promising research directions for the future. Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.

翻译：生成知识图案(KGC)是指利用顺序到顺序框架来建立知识图案的方法,这些方法具有灵活性,可以适应广泛的任务。在本研究中,我们总结了最近在基因化知识图案构建方面取得的令人信服的进展。我们从不同代代指标的角度介绍每个模式的优缺点,并提供理论见解和经验分析。根据审查,我们建议未来有希望的研究方向。我们的贡献有三个方面:(1) 我们为基因化知识图案方法提出详细、完整的分类;(2) 我们对基因化知识图案方法提供理论和经验分析;(3) 我们提出未来可以制定的若干研究方向。