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@@ -17,12 +17,12 @@ 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 paper tookit [DeepKE](https://github.com/zjunlp/DeepKE) and [PromptKG](https://github.com/zjunlp/PromptKG).
## *👋 News!*
- We conducted the development of knowledge extraction toolkits such as [DEEPKE](https://github.com/zjunlp/DeepKE) and [PromptKG](https://github.com/zjunlp/PromptKG).
- Due to the rise of generative extraction methods in the NLP community，we summarize recent progress in generative KGC.
- Congratulations! Our work has been accepted by the EMNLP2022 main conference.
- We release our paper on [arivx](https://arxiv.org/pdf/2210.12714.pdf).
- Due to the rise of generative extraction methods in the NLP community，we summarize recent progress in generative KGC and release our paper on [arivx](https://arxiv.org/pdf/2210.12714.pdf).
### 🚩Citation
If you find this survey useful for your research, please consider citing