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.Comment: Accepted to EMNLP 2022 (oral) and a public repository is available in
https://github.com/zjunlp/Generative_KG_Construction_Paper