More Web Proxy on the site http://driver.im/

research-article

Open access

TransGCN: Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction

Authors:

Krzysztof Janowicz,

Rui ZhuAuthors Info & Claims

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

Pages 131 - 138

https://doi.org/10.1145/3360901.3364441

Published: 23 September 2019 Publication History

Abstract

Link prediction is an important and frequently studied task that contributes to an understanding of the structure of knowledge graphs (KGs) in statistical relational learning. Inspired by the success of graph convolutional networks (GCN) in modeling graph data, we propose a unified GCN framework, named TransGCN, to address this task, in which relation and entity embeddings are learned simultaneously. To handle heterogeneous relations in KGs, we introduce a novel way of representing heterogeneous neighborhood by introducing transformation assumptions on the relationship between the subject, the relation, and the object of a triple. Specifically, a relation is treated as a transformation operator transforming a head entity to a tail entity. Both translation assumption in TransE and rotation assumption in RotatE are explored in our framework. Additionally, instead of only learning entity embeddings in the convolution-based encoder while learning relation embeddings in the decoder as done by the state-of-art models, e.g., R-GCN, the TransGCN framework trains relation embeddings and entity embeddings simultaneously during the graph convolution operation, thus having fewer parameters compared with R-GCN. Experiments show that our models outperform the-state-of-arts methods on both FB15K-237 and WN18RR.

References

[1]

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems. 2787--2795.

[2]

Tim Dettmers, Minervini Pasquale, Stenetorp Pontus, and Sebastian Riedel. 2018. Convolutional 2D Knowledge Graph Embeddings. In Proceedings of the 32th AAAI Conference on Artificial Intelligence. 1811--1818. https://arxiv.org/abs/1707.01476

[3]

Jun Feng, Minlie Huang, Mingdong Wang, Mantong Zhou, Yu Hao, and Xiaoyan Zhu. 2016. Knowledge graph embedding by flexible translation. In Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning.

Digital Library

[4]

Hongyang Gao, Zhengyang Wang, and Shuiwang Ji. 2018. Large-scale learnable graph convolutional networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1416--1424.

Digital Library

[5]

Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 1263--1272.

Digital Library

[6]

Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034.

[7]

William L. Hamilton, Payal Bajaj, Marinka Zitnik, Daniel Jurafsky, and Jure Leskovec. 2018. Embedding Logical Queries on Knowledge Graphs. In NeurIPS.

[8]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]

Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge graph embedding via dynamic mapping matrix. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Vol. 1. 687--696.

[10]

Guoliang Ji, Kang Liu, Shizhu He, and Jun Zhao. 2016. Knowledge graph completion with adaptive sparse transfer matrix. In Thirtieth AAAI Conference on Artificial Intelligence.

Digital Library

[11]

Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley. 2016. Molecular graph convolutions: moving beyond fingerprints. Journal of computer-aided molecular design 30, 8 (2016), 595--608.

[12]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[13]

Thomas N Kipf and MaxWelling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).

[14]

Daphne Koller, Nir Friedman, Sao Deroski, Charles Sutton, Andrew McCallum, Avi Pfeffer, Pieter Abbeel, Ming-Fai Wong, David Heckerman, Chris Meek, et al. 2007. Introduction to statistical relational learning. MIT press.

[15]

Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In Thirty-Second AAAI Conference on Artificial Intelligence.

[16]

Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. 2018. Adaptive graph convolutional neural networks. In Thirty-Second AAAI Conference on Artificial Intelligence.

[17]

Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, and Song Liu. 2015. Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379 (2015).

[18]

Gengchen Mai, Bo Yan, Krzysztof Janowicz, and Rui Zhu. 2019. Relaxing Unanswerable Geographic Questions Using A Spatially Explicit Knowledge Graph Embedding Model. In Proceedings of 22nd AGILE International Conference on Geographic Information Science, Jun. 17--10, Limassol, Cyprus.

[19]

Arvind Neelakantan, Benjamin Roth, and Andrew McCallum. 2015. Compositional vector space models for knowledge base inference. In 2015 aaai spring symposium series.

[20]

Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, and Dinh Phung. 2018. A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network. In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). 327--333.

[21]

Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011. A Three-Way Model for Collective Learning on Multi-Relational Data. In ICML, Vol. 11. 809-- 816.

Digital Library

[22]

Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and MaxWelling. 2018. Modeling relational data with graph convolutional networks. In European Semantic Web Conference. Springer, 593--607.

Digital Library

[23]

David Shuman, Sunil Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. 2013. The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains. IEEE Signal Processing Magazine 30 (2013), 83--98.

[24]

Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. In International Conference on Learning Representations. https://openreview.net/forum?id= HkgEQnRqYQ

[25]

Kristina Toutanova and Danqi Chen. 2015. Observed versus latent features for knowledge base and text inference. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. 57--66.

[26]

Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In International Conference on Machine Learning. 2071--2080.

Digital Library

[27]

Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. 2017. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering 29, 12 (2017), 2724--2743.

[28]

Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Twenty-Eighth AAAI conference on artificial intelligence.

Digital Library

[29]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu. 2019. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596 (2019).

[30]

Bo Yan, MatthewWalker, and Krzysztof Janowicz. 2019. A Time-Aware Inductive Representation Learning Strategy for Heterogeneous Graphs. (2019).

[31]

Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014).

[32]

Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). ACM, New York, NY, USA, 974--983. https://doi.org/10.1145/3219819.3219890

Digital Library

Cited By

Fang YLiu XLu WPedrycz WLang QYang J(2025)Knowledge graph completion with low-dimensional gated hierarchical hyperbolic embeddingKnowledge-Based Systems10.1016/j.knosys.2024.112804309(112804)Online publication date: Jan-2025
https://doi.org/10.1016/j.knosys.2024.112804
Liu YWang PYang DQiu N(2024)A knowledge graph embedding model based attention mechanism for enhanced node information integrationPeerJ Computer Science10.7717/peerj-cs.180810(e1808)Online publication date: 22-Jan-2024
https://doi.org/10.7717/peerj-cs.1808
Yu JTongxin WZhiwei YTing YJunting LYude H(2024)Power equipment defect prediction based on time series knowledge graphScientific Insights and Discoveries Review10.59782/sidr.v2i1.652:1(105-114)Online publication date: 7-Oct-2024
https://doi.org/10.59782/sidr.v2i1.65
Show More Cited By

Index Terms

TransGCN: Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Machine learning approaches
      1. Learning latent representations

Recommendations

Degree aware based adversarial graph convolutional networks for entity alignment in heterogeneous knowledge graph
Abstract
Entity alignment, as the vital technique for knowledge graph construction and integration, aims to match entities that refer to the same real-world identity in different knowledge graphs (KGs). Recently, much effort has been devoted to ...
RIECN: learning relation-based interactive embedding convolutional network for knowledge graph
Abstract
Most knowledge graphs(KGs) are large and incomplete graph-structure database, which can be completed by predicting miss links according to the existing knowledge. The mainstream method is knowledge graph embedding (KGE) which is designed to learn ...
Affine Transformation-Based Knowledge Graph Embedding
Knowledge Science, Engineering and Management
Abstract
Real-world knowledge graphs are often incomplete. The challenge in knowledge graph completion primarily stems from complex relations and various relation patterns within the knowledge graph (KG). Traditional geometric translation methods in ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

September 2019

281 pages

ISBN:9781450370080

DOI:10.1145/3360901

General Chairs:
Mayank Kejriwal
University of Southern California Information Sciences Institute, USA
,
Pedro Szekely
University of Southern California Information Sciences Institute, USA
,
Program Chair:
Raphaël Troncy
EURECOM, France

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Best Paper

Author Tags

Qualifiers

Research-article

Conference

K-CAP '19

Sponsor:

SIGAI

K-CAP '19: Knowledge Capture Conference

November 19 - 21, 2019

CA, Marina Del Rey, USA

Acceptance Rates

Overall Acceptance Rate 55 of 198 submissions, 28%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

53
Total Citations
View Citations
1,810
Total Downloads

Downloads (Last 12 months)322
Downloads (Last 6 weeks)45

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Fang YLiu XLu WPedrycz WLang QYang J(2025)Knowledge graph completion with low-dimensional gated hierarchical hyperbolic embeddingKnowledge-Based Systems10.1016/j.knosys.2024.112804309(112804)Online publication date: Jan-2025
https://doi.org/10.1016/j.knosys.2024.112804
Liu YWang PYang DQiu N(2024)A knowledge graph embedding model based attention mechanism for enhanced node information integrationPeerJ Computer Science10.7717/peerj-cs.180810(e1808)Online publication date: 22-Jan-2024
https://doi.org/10.7717/peerj-cs.1808
Yu JTongxin WZhiwei YTing YJunting LYude H(2024)Power equipment defect prediction based on time series knowledge graphScientific Insights and Discoveries Review10.59782/sidr.v2i1.652:1(105-114)Online publication date: 7-Oct-2024
https://doi.org/10.59782/sidr.v2i1.65
Meng SZhou JChen XLiu YLu FHuang X(2024)Structure-Information-Based Reasoning over the Knowledge Graph: A Survey of Methods and ApplicationsACM Transactions on Knowledge Discovery from Data10.1145/367114818:8(1-42)Online publication date: 16-Aug-2024
https://dl.acm.org/doi/10.1145/3671148
Liang KMeng LLiu MLiu YTu WWang SZhou SLiu XSun FHe K(2024)A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-ModalIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.341745146:12(9456-9478)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3417451
Zhang QXu Y(2024)Knowledge graph embedding with inverse function representation for link predictionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107225127(107225)Online publication date: Jan-2024
https://doi.org/10.1016/j.engappai.2023.107225
Xu GRao GZhang LCong Q(2024)Entity-relation aggregation mechanism graph neural network for knowledge graph embeddingApplied Intelligence10.1007/s10489-024-05907-y55:1Online publication date: 28-Nov-2024
https://doi.org/10.1007/s10489-024-05907-y
Ma RGao JCheng LZhang YPetrosian O(2024)DAGCN: hybrid model for efficiently handling joint node and link prediction in cloud workflowsApplied Intelligence10.1007/s10489-024-05828-w54:23(12505-12530)Online publication date: 18-Sep-2024
https://doi.org/10.1007/s10489-024-05828-w
Bao LWang YSong XSun T(2024)HGCGE: hyperbolic graph convolutional networks-based knowledge graph embedding for link predictionKnowledge and Information Systems10.1007/s10115-024-02247-8Online publication date: 3-Oct-2024
https://doi.org/10.1007/s10115-024-02247-8
Zhang YSun LWang YLiu QLiu L(2024)One-shot knowledge graph completion based on disentangled representation learningNeural Computing and Applications10.1007/s00521-024-10236-936:32(20277-20293)Online publication date: 12-Aug-2024
https://doi.org/10.1007/s00521-024-10236-9
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents