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@@ -17,7 +17,7 @@ We present the advantages and weaknesses of each paradigm in terms of different
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.
For more resources about knowledge graph construction, please check our tookit [DeepKE](https://github.com/zjunlp/DeepKE) and [PromptKG](https://github.com/zjunlp/PromptKG).
For more resources about knowledge graph construction, please check our tookit [DeepKE](https://github.com/zjunlp/DeepKE).
## *👋 News!*
- Congratulations! Our work has been accepted by the EMNLP2022 main conference.