HyCubE:高效知识超图三维环形卷积嵌入

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhao Li;Xin Wang;Jun Zhao;Wenbin Guo;Jianxin Li
{"title":"HyCubE:高效知识超图三维环形卷积嵌入","authors":"Zhao Li;Xin Wang;Jun Zhao;Wenbin Guo;Jianxin Li","doi":"10.1109/TKDE.2025.3531372","DOIUrl":null,"url":null,"abstract":"Knowledge hypergraph embedding models are usually computationally expensive due to the inherent complex semantic information. However, existing works mainly focus on improving the effectiveness of knowledge hypergraph embedding, making the model architecture more complex and redundant. It is desirable and challenging for knowledge hypergraph embedding to reach a trade-off between model effectiveness and efficiency. In this paper, we propose an end-to-end efficient knowledge hypergraph embedding model, HyCubE, which designs a novel <i>3D circular convolutional neural network</i> and the <i>alternate mask stack</i> strategy to enhance the interaction and extraction of feature information comprehensively. Furthermore, our proposed model achieves a better trade-off between effectiveness and efficiency by adaptively adjusting the 3D circular convolutional layer structure to handle <inline-formula><tex-math>$n$</tex-math></inline-formula>-ary knowledge tuples of different arities with fewer parameters. In addition, we use a knowledge hypergraph 1-N multilinear scoring way to accelerate the model training efficiency further. Finally, extensive experimental results on all datasets demonstrate that our proposed model consistently outperforms state-of-the-art baselines, with an average improvement of 8.22% and a maximum improvement of 33.82% across all metrics. Meanwhile, HyCubE is 6.12x faster, GPU memory usage is 52.67% lower, and the number of parameters is reduced by 85.21% compared with the average metric of the latest state-of-the-art baselines.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1902-1914"},"PeriodicalIF":8.9000,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding\",\"authors\":\"Zhao Li;Xin Wang;Jun Zhao;Wenbin Guo;Jianxin Li\",\"doi\":\"10.1109/TKDE.2025.3531372\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Knowledge hypergraph embedding models are usually computationally expensive due to the inherent complex semantic information. However, existing works mainly focus on improving the effectiveness of knowledge hypergraph embedding, making the model architecture more complex and redundant. It is desirable and challenging for knowledge hypergraph embedding to reach a trade-off between model effectiveness and efficiency. In this paper, we propose an end-to-end efficient knowledge hypergraph embedding model, HyCubE, which designs a novel <i>3D circular convolutional neural network</i> and the <i>alternate mask stack</i> strategy to enhance the interaction and extraction of feature information comprehensively. Furthermore, our proposed model achieves a better trade-off between effectiveness and efficiency by adaptively adjusting the 3D circular convolutional layer structure to handle <inline-formula><tex-math>$n$</tex-math></inline-formula>-ary knowledge tuples of different arities with fewer parameters. In addition, we use a knowledge hypergraph 1-N multilinear scoring way to accelerate the model training efficiency further. Finally, extensive experimental results on all datasets demonstrate that our proposed model consistently outperforms state-of-the-art baselines, with an average improvement of 8.22% and a maximum improvement of 33.82% across all metrics. Meanwhile, HyCubE is 6.12x faster, GPU memory usage is 52.67% lower, and the number of parameters is reduced by 85.21% compared with the average metric of the latest state-of-the-art baselines.\",\"PeriodicalId\":13496,\"journal\":{\"name\":\"IEEE Transactions on Knowledge and Data Engineering\",\"volume\":\"37 4\",\"pages\":\"1902-1914\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-01-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Knowledge and Data Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10845179/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10845179/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

知识超图嵌入模型由于其固有的复杂语义信息,通常计算量很大。然而,现有的工作主要集中在提高知识超图嵌入的有效性上,使得模型架构更加复杂和冗余。如何在模型有效性和效率之间取得平衡是知识超图嵌入所需要的,也是具有挑战性的。本文提出了一种端到端高效的知识超图嵌入模型HyCubE,该模型设计了一种新颖的三维圆形卷积神经网络和交替掩码堆栈策略,全面增强了特征信息的交互和提取能力。此外,我们提出的模型通过自适应调整三维圆卷积层结构来处理具有更少参数的n个不同度的知识元组,从而更好地平衡了有效性和效率。此外,我们还采用了知识超图1-N多元线性评分的方法,进一步提高了模型的训练效率。最后,在所有数据集上的广泛实验结果表明,我们提出的模型始终优于最先进的基线,所有指标的平均改进为8.22%,最大改进为33.82%。与此同时,HyCubE的速度比最新基线的平均指标快6.12倍,GPU内存使用率低52.67%,参数数量减少85.21%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding
Knowledge hypergraph embedding models are usually computationally expensive due to the inherent complex semantic information. However, existing works mainly focus on improving the effectiveness of knowledge hypergraph embedding, making the model architecture more complex and redundant. It is desirable and challenging for knowledge hypergraph embedding to reach a trade-off between model effectiveness and efficiency. In this paper, we propose an end-to-end efficient knowledge hypergraph embedding model, HyCubE, which designs a novel 3D circular convolutional neural network and the alternate mask stack strategy to enhance the interaction and extraction of feature information comprehensively. Furthermore, our proposed model achieves a better trade-off between effectiveness and efficiency by adaptively adjusting the 3D circular convolutional layer structure to handle $n$-ary knowledge tuples of different arities with fewer parameters. In addition, we use a knowledge hypergraph 1-N multilinear scoring way to accelerate the model training efficiency further. Finally, extensive experimental results on all datasets demonstrate that our proposed model consistently outperforms state-of-the-art baselines, with an average improvement of 8.22% and a maximum improvement of 33.82% across all metrics. Meanwhile, HyCubE is 6.12x faster, GPU memory usage is 52.67% lower, and the number of parameters is reduced by 85.21% compared with the average metric of the latest state-of-the-art baselines.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信