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Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning

Authors:

Wayne Xin Zhao,

Ji-Rong WenAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 740 - 751

https://doi.org/10.1145/3366423.3380155

Published: 20 April 2020 Publication History

Abstract

The task of Knowledge Graph Completion (KGC) aims to automatically infer the missing fact information in Knowledge Graph (KG). In this paper, we take a new perspective that aims to leverage rich user-item interaction data (user interaction data for short) for improving the KGC task. Our work is inspired by the observation that many KG entities correspond to online items in application systems. However, the two kinds of data sources have very different intrinsic characteristics, and it is likely to hurt the original performance using simple fusion strategy.

To address this challenge, we propose a novel adversarial learning approach by leveraging user interaction data for the KGC task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. The discriminator takes the learned useful information from user interaction data as input, and gradually enhances the evaluation capacity in order to identify the fake samples generated by the generator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks, which will be jointly optimized with the discriminator. Such an approach is effective to alleviate the issues about data heterogeneity and semantic complexity for the KGC task. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our approach on the KGC task.

References

[1]

Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. Algorithms 11, 9 (2018), 137.

[2]

Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007. DBpedia: A Nucleus for a Web of Open Data. In Proceedings of the 6th International The Semantic Web and 2Nd Asian Conference on Asian Semantic Web Conference (Busan, Korea) (ISWC’07/ASWC’07). 722–735.

Digital Library

[3]

Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States.2787–2795.

Digital Library

[4]

Liwei Cai and William Yang Wang. 2018. KBGAN: Adversarial Learning for Knowledge Graph Embeddings. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 1 (Long Papers). 1470–1480.

[5]

Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Chua Tat-seng. 2019. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preference. In WWW.

[6]

Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018. Convolutional 2D Knowledge Graph Embeddings. In AAAI-18.

[7]

Luis Galárraga, Simon Razniewski, Antoine Amarilli, and Fabian M. Suchanek. 2017. Predicting Completeness in Knowledge Bases. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, United Kingdom, February 6-10, 2017. 375–383.

Digital Library

[8]

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada. 2672–2680.

Digital Library

[9]

Google. 2016. Freebase Data Dumps. https://developers.google.com/freebase/data.

[10]

Kelvin Guu, John Miller, and Percy Liang. 2015. Traversing Knowledge Graphs in Vector Space. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015. 318–327.

[11]

F. Maxwell Harper and Joseph A. Konstan. 2016. The MovieLens Datasets. TiiS 5, 4 (2016), 1–19.

Digital Library

[12]

Ruining He and Julian Mcauley. 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In WWW.

[13]

Binbin Hu, Yuan Fang, and Chuan Shi. 2019. Adversarial Learning on Heterogeneous Information Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019.120–129.

Digital Library

[14]

Jin Huang, Wayne Xin Zhao, Hong-Jian Dou, Ji-Rong Wen, and Edward Y. Chang. 2018. Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018. 505–514.

[15]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings.

[16]

Yankai Lin, Zhiyuan Liu, Huan-Bo Luan, Maosong Sun, Siwei Rao, and Song Liu. 2015. Modeling Relation Paths for Representation Learning of Knowledge Bases. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015. 705–714.

[17]

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning Entity and Relation Embeddings for Knowledge Graph Completion. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA.2181–2187.

Digital Library

[18]

Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011. A Three-Way Model for Collective Learning on Multi-Relational Data. In Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011. 809–816.

Digital Library

[19]

Zhaoqing Pan, Weijie Yu, Xiaokai Yi, Asifullah Khan, Feng Yuan, and Yuhui Zheng. 2019. Recent Progress on Generative Adversarial Networks (GANs): A Survey. IEEE Access 7(2019), 36322–36333.

[20]

Guangyuan Piao and John G. Breslin. 2018. Transfer Learning for Item Recommendations and Knowledge Graph Completion in Item Related Domains via a Co-Factorization Model. In The Semantic Web - 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3-7, 2018, Proceedings. 496–511.

[21]

Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In International Conference on World Wide Web. 811–820.

Digital Library

[22]

Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved Techniques for Training GANs. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. 2226–2234.

Digital Library

[23]

Markus Schedl. 2016. The LFM-1b Dataset for Music Retrieval and Recommendation. In ICMR.

[24]

Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling Relational Data with Graph Convolutional Networks. In The Semantic Web - 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3-7, 2018, Proceedings. 593–607.

[25]

Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, and Bowen Zhou. 2019. End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion. (2019).

[26]

Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MAS) and Applications. In Proceedings of the 24th International Conference on World Wide Web (Florence, Italy) (WWW ’15 Companion). 243–246.

Digital Library

[27]

Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: A Core of Semantic Knowledge. In Proceedings of the 16th International Conference on World Wide Web (Banff, Alberta, Canada) (WWW ’07). 697–706.

Digital Library

[28]

Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, and Chi Xu. 2018. Recurrent knowledge graph embedding for effective recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2-7, 2018. 297–305.

Digital Library

[29]

Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour. 1999. Policy Gradient Methods for Reinforcement Learning with Function Approximation. In Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29 - December 4, 1999]. 1057–1063.

[30]

Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. 2015. Representing Text for Joint Embedding of Text and Knowledge Bases. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015. 1499–1509.

[31]

Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016. Complex Embeddings for Simple Link Prediction. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016. 2071–2080.

Digital Library

[32]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. International Conference on Learning Representations (2018).

[33]

Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. GraphGAN: Graph Representation Learning With Generative Adversarial Nets. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18). 2508–2515.

[34]

Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2019. Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019. 2000–2010.

Digital Library

[35]

PeiFeng Wang, Shuangyin Li, and Rong Pan. 2018. Incorporating GAN for Negative Sampling in Knowledge Representation Learning. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18). 2005–2012.

[36]

Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD.

Digital Library

[37]

Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge Graph Embedding by Translating on Hyperplanes. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada.1112–1119.

Digital Library

[38]

Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings.

[39]

Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative Knowledge Base Embedding for Recommender Systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016. 353–362.

Digital Library

[40]

Wayne Xin Zhao, Hong-Jian Dou, Yuanpei Zhao, Daxiang Dong, and Ji-Rong Wen. 2019. Neural Network Based Popularity Prediction by Linking Online Content with Knowledge Bases. In Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part II. 16–28.

[41]

Wayne Xin Zhao, Gaole He, Kunlin Yang, Hong-Jian Dou, Jin Huang, Siqi Ouyang, and Ji-Rong Wen. 2019. KB4Rec: A Data Set for Linking Knowledge Bases with Recommender Systems. Data Intelligence 1, 2 (2019), 121–136.

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Anelli VFerrara AMusto CNarducci FRagone AZanker M(2024)Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687114(1245-1249)Online publication date: 8-Oct-2024
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Bin Ahmadon MYamaguchi SMahamad ASaon S(2023)Refining Preference-Based Recommendation with Associative Rules and Process Mining Using Correlation DistanceBig Data and Cognitive Computing10.3390/bdcc70100347:1(34)Online publication date: 10-Feb-2023
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Index Terms

Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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Published: 20 April 2020

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April 20 - 24, 2020

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Cited By

Anelli VFerrara AMusto CNarducci FRagone AZanker M(2024)Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687114(1245-1249)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687114
Hu QSong YAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)User Consented Federated Recommender System Against Personalized Attribute Inference AttackProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635830(276-285)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635830
Bin Ahmadon MYamaguchi SMahamad ASaon S(2023)Refining Preference-Based Recommendation with Associative Rules and Process Mining Using Correlation DistanceBig Data and Cognitive Computing10.3390/bdcc70100347:1(34)Online publication date: 10-Feb-2023
https://doi.org/10.3390/bdcc7010034
Anelli VBasile PDe Melo GDonini FFerrara AMusto CNarducci FRagone AZanker M(2023)Fifth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Proceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608759(1259-1262)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608759
Ferrara AAnelli VMancino ADi Noia TDi Sciascio E(2023)KGFlex: Efficient Recommendation with Sparse Feature Factorization and Knowledge GraphsACM Transactions on Recommender Systems10.1145/35889011:4(1-30)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3588901
Dong XSong XZheng NWei YZhao Z(2023)Dual Preference Distribution Learning for Item RecommendationACM Transactions on Information Systems10.1145/356579841:3(1-22)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3565798
Lan YHe GJiang JJiang JZhao WWen J(2023)Complex Knowledge Base Question Answering: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322385835:11(11196-11215)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TKDE.2022.3223858
Alotaibi FZhou D(2023)Towards Bi-Level Out-of-Distribution Logical Reasoning on Knowledge Graphs2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386470(1-5)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386470
Yang LZhao JLong JHuang JWang ZChen T(2023)Path-KGE: Preference-Aware Knowledge Graph Embedding with Path Semantics for Link PredictionWeb Information Systems Engineering – WISE 202310.1007/978-981-99-7254-8_32(409-424)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-981-99-7254-8_32
Cao JCao J(2023)IntroductionE-Commerce Big Data Mining and Analytics10.1007/978-981-99-3588-8_1(1-18)Online publication date: 30-Jul-2023
https://doi.org/10.1007/978-981-99-3588-8_1
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