research-article

Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer

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Huajun ChenAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 2581 - 2590

https://doi.org/10.1145/3543507.3583301

Published: 30 April 2023 Publication History

Abstract

Knowledge graphs (KG) are essential background knowledge providers in many tasks. When designing models for KG-related tasks, one of the key tasks is to devise the Knowledge Representation and Fusion (KRF) module that learns the representation of elements from KGs and fuses them with task representations. While due to the difference of KGs and perspectives to be considered during fusion across tasks, duplicate and ad hoc KRF modules design are conducted among tasks. In this paper, we propose a novel knowledge graph pretraining model KGTransformer that could serve as a uniform KRF module in diverse KG-related tasks. We pretrain KGTransformer with three self-supervised tasks with sampled sub-graphs as input. For utilization, we propose a general prompt-tuning mechanism regarding task data as a triple prompt to allow flexible interactions between task KGs and task data. We evaluate pretrained KGTransformer on three tasks, triple classification, zero-shot image classification, and question answering. KGTransformer consistently achieves better results than specifically designed task models. Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks. The code and datasets are available at https://github.com/zjukg/KGTransformer.

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Zhu DLi ZZhang MYuan JLiu JKuang KWu CBaeza-Yates RBonchi F(2024)Neural Collapse Anchored Prompt Tuning for Generalizable Vision-Language ModelsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671690(4631-4640)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671690
Tang JYang YWei WShi LSu LCheng SYin DHuang CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)GraphGPT: Graph Instruction Tuning for Large Language ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657775(491-500)Online publication date: 10-Jul-2024
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Fang ZLei SZhu XYang CZhang SYin XQin JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge GraphProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657706(70-79)Online publication date: 10-Jul-2024
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Index Terms

Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning

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WWW '23: Proceedings of the ACM Web Conference 2023

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DOI:10.1145/3543507

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Published: 30 April 2023

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Cited By

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https://dl.acm.org/doi/10.1145/3637528.3671690
Tang JYang YWei WShi LSu LCheng SYin DHuang CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)GraphGPT: Graph Instruction Tuning for Large Language ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657775(491-500)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657775
Fang ZLei SZhu XYang CZhang SYin XQin JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge GraphProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657706(70-79)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657706
Liu YCao YWang SWang QBi GAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Generative Models for Complex Logical Reasoning over Knowledge GraphsProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635804(492-500)Online publication date: 4-Mar-2024
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