research-article

Knowledge Graph Embedding Based on Multi-View Clustering Framework

Authors:

Xiaodong ShiAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 33, Issue 2

Pages 585 - 596

https://doi.org/10.1109/TKDE.2019.2931548

Published: 01 February 2021 Publication History

Abstract

Knowledge representation is one of the critical problems in knowledge engineering and artificial intelligence, while knowledge embedding as a knowledge representation methodology indicates entities and relations in knowledge graph as low-dimensional, continuous vectors. In this way, knowledge graph is compatible with numerical machine learning models. Major knowledge embedding methods employ geometric translation to design score function, which is weak-semantic for natural language processing. To overcome this disadvantage, in this paper, we propose our model based on multi-view clustering framework, which could generate semantic representations of knowledge elements (i.e., <italic>entities/relations</italic>). With our semantic model, we also present an empowered solution to entity retrieval with entity description. Extensive experiments show that our model achieves substantial improvements against baselines on the task of knowledge graph completion, triple classification, entity classification, and entity retrieval.

References

[1]

J. F. Sowa, “Knowledge representation: Logical, philosophical, and computational foundations,” PWS, 2000.

[2]

D. Lukovnikov, A. Fischer, and J. Lehmann, “Neural network-based question answering over knowledge graphs on word and character level,” in Proc. Int. Conf. World Wide Web, 2017, pp. 1211–1220.

[3]

D. Wen, Y. Liu, K. Yuan, S. Si, and Y. Shen, “Attention-aware path-based relation extraction for medical knowledge graph,” in Proc. Int. Conf. Smart Comput. Commun., 2017, pp. 321–331.

[4]

A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in Proc. Neural Inf. Process. Syst., 2013, pp. 2787–2795.

[5]

R. Socher, D. Chen, C. D. Manning, and A. Ng, “Reasoning with neural tensor networks for knowledge base completion,” in Proc. Neural Inf. Process. Syst., 2013, pp. 926–934.

[6]

H. Xiao, M. Huang, and X. Zhu, “TransG: A generative model for knowledge graph embedding,” in Proc. 54th Annu. Meet. Assoc. Comput. Linguistics (Vol. 1: Long Papers), 2016, vol. 1, pp. 2316–2325.

[7]

Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph embedding by translating on hyperplanes,” in Proc. 28th AAAI Conf. Artif. Intell., 2014, pp. 1112–1119.

[8]

Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in Proc. . 29th AAAI Conf. Artif. Intell., 2015, pp. 2181–2187.

[9]

T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, “Convolutional 2D knowledge graph embeddings,” in Proc. 32nd AAAI Conf. Artif. Intell., 2018, pp. 1811–1818.

[10]

A. Bordes, J. Weston, R. Collobert, Y. Bengio, et al., “Learning structured embeddings of knowledge bases.” in Proc. 25th AAAI Conf. Artif. Intell., 2011, pp. 301–306.

[11]

H. Xiao, M. Huang, L. Meng, and X. Zhu, “SSP: Semantic space projection for knowledge graph embedding with text description,” in Proc. 31st AAAI Conf. Artif. Intell., 2017, pp. 3104–3110.

[12]

S. Guo, Q. Wang, B. Wang, L. Wang, and L. Guo, “Semantically smooth knowledge graph embedding,” in Proc. 53rd Annu. Meet. Assoc. Comput. Linguistics 7th Int. Joint Conf. Natural Lang. Process. (Vol. 1: Long Papers), 2015, vol. 1, pp. 84–94.

[13]

R. Jenatton, N. L. Roux, A. Bordes, and G. R. Obozinski, “A latent factor model for highly multi-relational data,” in Proc. Neural Inf. Process. Syst., 2012, pp. 3167–3175.

[14]

H. Xiao, M. Huang, Y. Hao, and X. Zhu, “TransA: An adaptive approach for knowledge graph embedding,” arXiv preprint arXiv:1509.05490, 2015.

[15]

S. He, K. Liu, G. Ji, and J. Zhao, “Learning to represent knowledge graphs with gaussian embedding,” in Proc. 24th ACM Int. Conf. Inf. Knowl. Manage., 2015, pp. 623–632.

[16]

M. Nickel, L. Rosasco, and T. Poggio, “Holographic embeddings of knowledge graphs,” in Proc. Thirtieth AAAI Conf. Artif. Intell., 2016.

[17]

B. Shi and T. Weninger, “Proje: Embedding projection for knowledge graph completion,” in Proc. 31st AAAI Conf. Artif. Intell., 2017, pp. 1236–1242.

[18]

D. M. Blei, “Probabilistic topic models,” Commun. ACM, vol. 55, no. 4, pp. 77–84, 2012.

Digital Library

[19]

K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, “Freebase: A collaboratively created graph database for structuring human knowledge,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2008, pp. 1247–1250.

[20]

G. Ji, S. He, L. Xu, K. Liu, and J. Zhao, “Knowledge graph embedding via dynamic mapping matrix,” in Proc. 53rd Annu. Meet. Assoc. Comput. Linguistics/7th Int. Joint Conf. Natural Lang. Process. (Vol. 1: Long Papers), 2015, vol. 1, pp. 687–696.

[21]

M. Fan, Q. Zhou, E. Chang, and T. F. Zheng, “Transition-based knowledge graph embedding with relational mapping properties,” in Proc. 28th Pacific Asia Conf. Lang. Inf. Comput., 2014, pp. 328–337.

[22]

Y. Lin, Z. Liu, H. Luan, M. Sun, S. Rao, and S. Liu, “Modeling relation paths for representation learning of knowledge bases,” arXiv preprint arXiv:1506.00379, 2015.

[23]

Q. Wang, B. Wang, and L. Guo, “Knowledge base completion using embeddings and rules,” in Proc. 24th Int. Joint Conf. Artif. Intell., 2015, pp. 1859–1865.

[24]

H. Zhong, J. Zhang, Z. Wang, H. Wan, and Z. Chen, “Aligning knowledge and text embeddings by entity descriptions,” in Proc. Conf. Empirical Methods Natural Lang. Process., 2015, pp. 267–272.

[25]

H. Xiao, M. Huang, and X. Zhu, “From one point to a manifold: Knowledge graph embedding for precise link prediction,” in Proc. Twenty-Fifth Int. Joint Conf. Artif. Intell., 2016.

[26]

M. Nickel, V. Tresp, and H.-P. Kriegel, “Factorizing yago: Scalable machine learning for linked data,” in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 271–280.

[27]

B. Shi and T. Weninger, “Open-world knowledge graph completion,” in Proc. 32nd AAAI Conf. Artif. Intell., 2018, pp. 1957–1964.

[28]

K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, USA: MIT Press, 2012.

Digital Library

[29]

Z. Wang, W. Hamza, and R. Florian, “Bilateral multi-perspective matching for natural language sentences,” in Proc. Twenty-Sixth Int. Joint Conf. Artif. Intell., 2017.

[30]

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 10, pp. 2222–2232, Oct. 2017.

[31]

M. Sundermeyer, R. Schlter, and H. Ney, “Lstm neural networks for language modeling,” in Proc. Int. Conf. Text Speech Dialogue, 2012, pp. 601–608.

[32]

Z. Huang, W. Xu, and K. Yu, “Bidirectional LSTM-CRF models for sequence tagging,” arXiv preprint arXiv:1508.01991, 2015.

[33]

G. A. Miller, “Wordnet: A lexical database for english,” Commun. ACM, vol. 38, no. 11, pp. 39–41, 1995.

Digital Library

[34]

R. Xie, Z. Liu, J. Jia, H. Luan, and M. Sun, “Representation learning of knowledge graphs with entity descriptions,” in Proc. 13th AAAI Conf. Artif. Intell., 2016, pp. 2659–2665.

[35]

A. Neelakantan and M.-W. Chang, “Inferring missing entity type instances for knowledge base completion: New dataset and methods,” arXiv:1504.06658, 2015.

[36]

R. Hoffmann, C. Zhang, X. Ling, L. Zettlemoyer, and D. S. Weld, “Knowledge-based weak supervision for information extraction of overlapping relations,” in Proc. 49th Annu. Meet. Assoc. Comput. Linguistics: Human Lang. Technol.-Vol. 1, 2011, pp. 541–550.

Cited By

Liu ALi WLi TLi BHuang HZhou PWooldridge MDy JNatarajan S(2024)Towards inductive robustnessProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i12.29292(13855-13863)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i12.29292
Zhou LDu GLü KWang LDu J(2024)A Survey and an Empirical Evaluation of Multi-View Clustering ApproachesACM Computing Surveys10.1145/364510856:7(1-38)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3645108
Qin WWang XHu ZWang LLan YHong R(2024)Math Word Problem Generation via Disentangled Memory RetrievalACM Transactions on Knowledge Discovery from Data10.1145/363956918:5(1-21)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3639569
Show More Cited By

Index Terms

Knowledge Graph Embedding Based on Multi-View Clustering Framework
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining

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

Recommendations

Knowledge Graph Embedding via Metagraph Learning
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Knowledge graph embedding aims to represent entities and relations in a continuous feature space while preserving the structure of a knowledge graph. Most existing knowledge graph embedding methods either focus only on a flat structure of the given ...
Knowledge graph embedding via multiplicative interaction
ICIAI '18: Proceedings of the 2nd International Conference on Innovation in Artificial Intelligence

Knowledge graphs are playing a crucial role in many machine learning applications. Since most of the knowledge graphs are far from complete, many knowledge graph completion models have been proposed. TransE and its extended models all model knowledge ...
Explainable Knowledge Reasoning Framework Using Multiple Knowledge Graph Embedding
IJCKG '21: Proceedings of the 10th International Joint Conference on Knowledge Graphs

Knowledge reasoning using knowledge graphs has attracted much attention. However, there is difficulty in integrating various related works to realize complex reasoning with explanation using multiple knowledge graphs. To do this, I propose a reasoning ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 33, Issue 2

Feb. 2021

494 pages

ISSN:1041-4347

Issue’s Table of Contents

1041-4347 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 February 2021

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liu ALi WLi TLi BHuang HZhou PWooldridge MDy JNatarajan S(2024)Towards inductive robustnessProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i12.29292(13855-13863)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i12.29292
Zhou LDu GLü KWang LDu J(2024)A Survey and an Empirical Evaluation of Multi-View Clustering ApproachesACM Computing Surveys10.1145/364510856:7(1-38)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3645108
Qin WWang XHu ZWang LLan YHong R(2024)Math Word Problem Generation via Disentangled Memory RetrievalACM Transactions on Knowledge Discovery from Data10.1145/363956918:5(1-21)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3639569
Li X(2024)A faster deep graph clustering network based on dynamic graph weight update mechanismCluster Computing10.1007/s10586-024-04549-627:9(12123-12140)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10586-024-04549-6
Li JSi GTian PAn ZZhou F(2024)Overview of indoor scene recognition and representation methods based on multimodal knowledge graphsApplied Intelligence10.1007/s10489-023-05235-754:1(899-923)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05235-7
Xu JRen YTang HYang ZPan LYang YPu XYu PHe L(2023)Self-Supervised Discriminative Feature Learning for Deep Multi-View ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.319356935:7(7470-7482)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3193569
Ma TLin XSong BYu PZeng X(2023)KG-MTL: Knowledge Graph Enhanced Multi-Task Learning for Molecular InteractionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.318815435:7(7068-7081)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3188154
Liang TLiu YLiu XZhang HSharma GGuo M(2023)Distantly-Supervised Long-Tailed Relation Extraction Using Constraint GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.317722635:7(6852-6865)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3177226
Zhu JZhao WYang HNie F(2023)Joint Learning of Anchor Graph-Based Fuzzy Spectral Embedding and Fuzzy K-MeansIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.328326131:11(4097-4108)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3283261
Schramm SWehner CSchmid U(2023)Comprehensible Artificial Intelligence on Knowledge GraphsWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2023.10080679:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.websem.2023.100806
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents