research-article

Ontology-enhanced Prompt-tuning for Few-shot Learning

Authors:

Huajun ChenAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 778 - 787

https://doi.org/10.1145/3485447.3511921

Published: 25 April 2022 Publication History

Abstract

Few-shot Learning (FSL) is aimed to make predictions based on a limited number of samples. Structured data such as knowledge graphs and ontology libraries has been leveraged to benefit the few-shot setting in various tasks. However, the priors adopted by the existing methods suffer from challenging knowledge missing, knowledge noise, and knowledge heterogeneity, which hinder the performance for few-shot learning. In this study, we explore knowledge injection for FSL with pre-trained language models and propose ontology-enhanced prompt-tuning (OntoPrompt). Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text. We further introduce span-sensitive knowledge injection via a visible matrix to select informative knowledge to handle the knowledge noise issue. To bridge the gap between knowledge and text, we propose a collective training algorithm to optimize representations jointly. We evaluate our proposed OntoPrompt in three tasks, including relation extraction, event extraction, and knowledge graph completion, with eight datasets. Experimental results demonstrate that our approach can obtain better few-shot performance than baselines.

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Cited By

Dai LYin YChen EXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Unifying Graph Retrieval and Prompt Tuning for Graph-Grounded Text ClassificationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657934(2682-2686)Online publication date: 10-Jul-2024
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Zhu YYin ZTyson GHaq ELee LHui PChua TNgo CKa-Wei Lee RKumar RLauw H(2024)APT-Pipe: A Prompt-Tuning Tool for Social Data Annotation using ChatGPTProceedings of the ACM on Web Conference 202410.1145/3589334.3645642(245-255)Online publication date: 13-May-2024
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Lin XSu THuang ZXue SLiu HChen EChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Knowledge-Injected Curriculum Pretraining Framework for Question AnsweringProceedings of the ACM on Web Conference 202410.1145/3589334.3645406(1986-1997)Online publication date: 13-May-2024
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Index Terms

Ontology-enhanced Prompt-tuning for Few-shot Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing
  2. Machine learning
    1. Learning settings
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

Dai LYin YChen EXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Unifying Graph Retrieval and Prompt Tuning for Graph-Grounded Text ClassificationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657934(2682-2686)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657934
Zhu YYin ZTyson GHaq ELee LHui PChua TNgo CKa-Wei Lee RKumar RLauw H(2024)APT-Pipe: A Prompt-Tuning Tool for Social Data Annotation using ChatGPTProceedings of the ACM on Web Conference 202410.1145/3589334.3645642(245-255)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645642
Lin XSu THuang ZXue SLiu HChen EChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Knowledge-Injected Curriculum Pretraining Framework for Question AnsweringProceedings of the ACM on Web Conference 202410.1145/3589334.3645406(1986-1997)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645406
Pan SLuo LWang YChen CWang JWu X(2024)Unifying Large Language Models and Knowledge Graphs: A RoadmapIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.335210036:7(3580-3599)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3352100
Zhang YPeng HZhang YLi YHuang L(2024)Retrieving memory as prompts for continual relation extractionExpert Systems with Applications10.1016/j.eswa.2024.124542255(124542)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124542
Mumuni FMumuni A(2024)Improving deep learning with prior knowledge and cognitive modelsCognitive Systems Research10.1016/j.cogsys.2023.10118884:COnline publication date: 17-Apr-2024
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Li ZLi YLiu YLiu CZhou N(2023)K-CTIAA: Automatic Analysis of Cyber Threat Intelligence Based on a Knowledge GraphSymmetry10.3390/sym1502033715:2(337)Online publication date: 25-Jan-2023
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Liu XMao SWang XBu J(2023)Generative Transformer with Knowledge-Guided Decoding for Academic Knowledge Graph CompletionMathematics10.3390/math1105107311:5(1073)Online publication date: 21-Feb-2023
https://doi.org/10.3390/math11051073
Hu RLiu HZhou H(2023)Role Knowledge Prompting for Document-Level Event Argument ExtractionApplied Sciences10.3390/app1305304113:5(3041)Online publication date: 27-Feb-2023
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Liu JChen TLiang ZJiang HXiao YWei FQian YHao ZHan BFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Hierarchical Prompt Tuning for Few-Shot Multi-Task LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614913(1556-1565)Online publication date: 21-Oct-2023
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