Few-Shot Relation Prediction of Knowledge Graph via Convolutional Neural Network with Self-Attention

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Data Science and Engineering Pub Date : 2023-09-20 DOI:10.1007/s41019-023-00230-x
Shanna Zhong, Jiahui Wang, Kun Yue, Liang Duan, Zhengbao Sun, Yan Fang
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Abstract

Abstract Knowledge graph (KG) has become the vital resource for various applications like question answering and recommendation system. However, several relations in KG only have few observed triples, which makes it necessary to develop the method for few-shot relation prediction. In this paper, we propose the C onvolutional Neural Network with Self- A ttention R elation P rediction (CARP) model to predict new facts with few observed triples. First, to learn the relation property features, we build a feature encoder by using the convolutional neural network with self-attention from the few observed triples rather than background knowledge. Then, by incorporating the learned features, we give an embedding network to learn the representation of incomplete triples. Finally, we give the loss function and training algorithm of our CARP model. Experimental results on three real-world datasets show that our proposed method improves Hits@10 by 48% on average over the state-of-the-art competitors.
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基于自注意卷积神经网络的知识图谱少镜头关系预测
摘要知识图(KG)已成为问答和推荐系统等各种应用的重要资源。然而,KG中的一些关系只有很少的观测三元组,这使得有必要开发少量关系预测方法。在本文中,我们提出了具有自注意R关系P预测(CARP)模型的C卷积神经网络来预测具有较少观察三元组的新事实。首先,为了学习关系属性特征,我们使用具有自关注的卷积神经网络,从少数观察到的三元组而不是背景知识中构建特征编码器。然后,通过整合学习到的特征,我们给出了一个嵌入网络来学习不完全三元组的表示。最后给出了该模型的损失函数和训练算法。在三个真实数据集上的实验结果表明,我们提出的方法比最先进的竞争对手平均提高了Hits@10 48%。
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来源期刊
Data Science and Engineering
Data Science and Engineering Engineering-Computational Mechanics
CiteScore
10.40
自引率
2.40%
发文量
26
审稿时长
12 weeks
期刊介绍: The journal of Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital. The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains. Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area. It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering. More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to: (a) the nature and quality of data, (b) the computational complexity of data-intensive computing,(c) new methods for the design and analysis of the algorithms for solving problems with big data input,(d) collection and integration of data collected from internet and sensing devises or sensor networks, (e) representation, modeling, and visualization of  big data,(f)  storage, transmission, and management of big data,(g) methods and algorithms of  data intensive computing, such asmining big data,online analysis processing of big data,big data-based machine learning, big data based decision-making, statistical computation of big data, graph-theoretic computation of big data, linear algebraic computation of big data, and  big data-based optimization. (h) hardware systems and software systems for data-intensive computing, (i) data security, privacy, and trust, and(j) novel applications of big data.
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