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：生成式知识图谱构建（Generative Knowledge Graph Construction, KGC）是指利用序列到序列框架构建知识图谱的方法，该方法具有灵活性，并能适应广泛的任务。在本研究中，我们总结了生成式知识图谱构建领域近期取得的显著进展。我们从不同生成目标的角度阐述了每种范式的优势与不足，并提供了理论见解与实证分析。基于该综述，我们提出了未来具有前景的研究方向。我们的贡献包含三点：（1）为生成式KGC方法构建了详细且完整的分类体系；（2）对生成式KGC方法进行了理论与实证分析；（3）提出了若干可在未来发展的研究方向。