ABSTRACT
Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA. However, existing studies have not thoroughly exploited the advantages of temporal information in TKGs. Also, they perform EA by pre-aligning entity pairs, which can be labor-intensive and thus inefficient. In this paper, we present DualMatch that effectively fuses the relational and temporal information for EA. DualMatch transfers EA on TKGs into a weighted graph matching problem. More specifically, DualMatch is equipped with an unsupervised method, which achieves EA without necessitating the seed alignment. DualMatch has two steps: (i) encoding temporal and relational information into embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii) fusing both information and transforming it into alignment using a novel graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information. Extensive experiments on three real-world TKG datasets offer the insight that DualMatch significantly outperforms the state-of-the-art methods.
- Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In NeurIPS. 2787–2795.Google Scholar
- Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, and Tat-Seng Chua. 2019. Multi-Channel Graph Neural Network for Entity Alignment. In ACL. 1452–1461.Google Scholar
- Muhao Chen, Weijia Shi, Ben Zhou, and Dan Roth. 2021. Cross-lingual entity alignment with incidental supervision. (2021), 645–658.Google Scholar
- Muhao Chen, Yingtao Tian, Mohan Yang, and Carlo Zaniolo. 2017. Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment. In IJCAI. 1511–1517.Google Scholar
- Marco Cuturi. 2013. Sinkhorn Distances: Lightspeed Computation of Optimal Transport. In NeurIPS. 2292–2300.Google Scholar
- Yunjun Gao, Xiaoze Liu, Junyang Wu, Tianyi Li, Pengfei Wang, and Lu Chen. 2022. ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities. In KDD. 421–431.Google Scholar
- Alberto García-Durán, Sebastijan Dumancic, and Mathias Niepert. 2018. Learning Sequence Encoders for Temporal Knowledge Graph Completion. In EMNLP. 4816–4821.Google Scholar
- Congcong Ge, Xiaoze Liu, Lu Chen, Baihua Zheng, and Yunjun Gao. 2021. Make It Easy: An Effective End-to-End Entity Alignment Framework. In SIGIR. 777–786.Google Scholar
- Congcong Ge, Xiaoze Liu, Lu Chen, Baihua Zheng, and Yunjun Gao. 2022. LargeEA: Aligning Entities for Large-scale Knowledge Graphs. PVLDB 15, 2 (2022), 237–245.Google Scholar
- Congcong Ge, Pengfei Wang, Lu Chen, Xiaoze Liu, Baihua Zheng, and Yunjun Gao. 2021. CollaborEM: A Self-supervised Entity Matching Framework Using Multi-features Collaboration. TKDE (2021).Google Scholar
- Steven Gold and Anand Rangarajan. 1996. A Graduated Assignment Algorithm for Graph Matching. TPAMI 18, 4 (1996), 377–388.Google Scholar
- William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1025–1035.Google Scholar
- Harold W. Kuhn. 1955. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly 2, 1, 83–97.Google Scholar
- Xuming Hu, Fukun Ma, Chenyao Liu, Chenwei Zhang, Lijie Wen, and Philip S Yu. 2021. Semi-supervised Relation Extraction via Incremental Meta Self-Training. In Findings of EMNLP. 487–496.Google Scholar
- Xuming Hu, Lijie Wen, Yusong Xu, Chenwei Zhang, and Philip S. Yu. 2020. SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction. In EMNLP. 3673–3682.Google Scholar
- Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, and Philip S. Yu. 2021. Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction. In EMNLP. 2737–2746.Google Scholar
- Prachi Jain, Sushant Rathi, Mausam, and Soumen Chakrabarti. 2020. Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols. In EMNLP. 3733–3747.Google Scholar
- Chengyue Jiang, Wenyang Hui, Yong Jiang, Xiaobin Wang, Pengjun Xie, and Kewei Tu. 2022. Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing. arXiv preprint arXiv:abs/2212.09125(2022).Google Scholar
- Chengyue Jiang, Yong Jiang, Weiqi Wu, Pengjun Xie, and Kewei Tu. 2022. Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field. arXiv preprint arXiv:2212.01581(2022).Google Scholar
- Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR.Google Scholar
- Guillaume Lample, Alexis Conneau, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2018. Word translation without parallel data. In ICLR.Google Scholar
- Jennifer Lautenschlager, Steve Shellman, and Michael Ward. 2015. ICEWS Event Aggregations. https://doi.org/10.7910/DVN/28117Google Scholar
- Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, and Tat-Seng Chua. 2019. Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model. In EMNLP. 2723–2732.Google Scholar
- Tianyi Li, Lu Chen, Christian S Jensen, and Torben Bach Pedersen. 2021. TRACE: Real-time compression of streaming trajectories in road networks. PVLDB 14, 7 (2021), 1175–1187.Google Scholar
- Tianyi Li, Ruikai Huang, Lu Chen, Christian S Jensen, and Torben Bach Pedersen. 2020. Compression of uncertain trajectories in road networks. PVLDB 13, 7 (2020), 1050–1063.Google Scholar
- Bing Liu, Wen Hua, Guido Zuccon, Genghong Zhao, and Xia Zhang. 2022. High-quality Task Division for Large-scale Entity Alignment. arXiv preprint arXiv:2208.10366(2022).Google Scholar
- Fangyu Liu, Muhao Chen, Dan Roth, and Nigel Collier. 2021. Visual Pivoting for (Unsupervised) Entity Alignment. In AAAI. 4257–4266.Google Scholar
- Shuliang Liu, Xuming Hu, Chenwei Zhang, Shu’ang Li, Lijie Wen, and Philip S. Yu. 2022. HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction. In NAACL. 5970–5980.Google Scholar
- Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, and Tat-Seng Chua. 2020. Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment. In EMNLP. 6355–6364.Google Scholar
- Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, ZongYu Wang, Rui Xie, Wei Wu, and Man Lan. 2022. An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism. In ACL. 5888–5898.Google Scholar
- Xin Mao, Wenting Wang, Yuanbin Wu, and Man Lan. 2021. Boosting the Speed of Entity Alignment 10 × : Dual Attention Matching Network with Normalized Hard Sample Mining. In WWW. 821–832.Google Scholar
- Xin Mao, Wenting Wang, Yuanbin Wu, and Man Lan. 2021. From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment. In EMNLP. 2843–2853.Google Scholar
- Xin Mao, Wenting Wang, Huimin Xu, Man Lan, and Yuanbin Wu. 2020. MRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge Graph. In WSDM. 420–428.Google Scholar
- Xin Mao, Wenting Wang, Huimin Xu, Yuanbin Wu, and Man Lan. 2020. Relational Reflection Entity Alignment. In CIKM. 1095–1104.Google Scholar
- Gonzalo E. Mena, David Belanger, Scott W. Linderman, and Jasper Snoek. 2018. Learning Latent Permutations with Gumbel-Sinkhorn Networks. In ICLR.Google Scholar
- Alvin E Roth. 2008. Deferred acceptance algorithms: History, theory, practice, and open questions. international Journal of game Theory 36, 3 (2008), 537–569.Google Scholar
- Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt. 2011. Weisfeiler-Lehman Graph Kernels. JMLR 12(2011), 2539–2561.Google Scholar
- Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: a core of semantic knowledge. In WWW. 697–706.Google Scholar
- Zequn Sun, Muhao Chen, and Wei Hu. 2021. Knowing the No-match: Entity Alignment with Dangling Cases. In ACL. 3582–3593.Google Scholar
- Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, and Wei Zhang. 2020. Knowledge Association with Hyperbolic Knowledge Graph Embeddings. In EMNLP. 5704–5716.Google Scholar
- Zequn Sun, Wei Hu, and Chengkai Li. 2017. Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding. In ISWC. 628–644.Google Scholar
- Zequn Sun, Wei Hu, Qingheng Zhang, and Yuzhong Qu. 2018. Bootstrapping Entity Alignment with Knowledge Graph Embedding. In IJCAI. 4396–4402.Google Scholar
- Zequn Sun, Chengming Wang, Wei Hu, Muhao Chen, Jian Dai, Wei Zhang, and Yuzhong Qu. 2020. Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation. In AAAI. 222–229.Google Scholar
- Zequn Sun, Qingheng Zhang, Wei Hu, Chengming Wang, Muhao Chen, Farahnaz Akrami, and Chengkai Li. 2020. A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs. PVLDB 13, 11 (2020), 2326–2340.Google Scholar
- Bayu Distiawan Trisedya, Jianzhong Qi, and Rui Zhang. 2019. Entity Alignment between Knowledge Graphs Using Attribute Embeddings. In AAAI. 297–304.Google Scholar
- Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR.Google Scholar
- Denny Vrandecic and Markus Krötzsch. 2014. Wikidata: a free collaborative knowledgebase. Commun. ACM 57, 10 (2014), 78–85.Google Scholar
- Pengfei Wang, Xiaocan Zeng, Lu Chen, Fan Ye, Yuren Mao, Junhao Zhu, and Yunjun Gao. 2022. PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching. PVLDB 16, 2 (2022), 369–378.Google Scholar
- Zhichun Wang, Qingsong Lv, Xiaohan Lan, and Yu Zhang. 2018. Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. In EMNLP. 349–357.Google Scholar
- Boris Weisfeiler and Andrei Leman. 1968. The reduction of a graph to canonical form and the algebra which appears therein. NTI, Series 2, 9 (1968), 12–16.Google Scholar
- Kexuan Xin, Zequn Sun, Wen Hua, Wei Hu, Jianfeng Qu, and Xiaofang Zhou. 2022. Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding. arXiv preprint arXiv:2208.11125(2022).Google Scholar
- Chenyan Xiong, Russell Power, and Jamie Callan. 2017. Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding. In WWW. 1271–1279.Google Scholar
- Chengjin Xu, Fenglong Su, and Jens Lehmann. 2021. Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs. In EMNLP. 8999–9010.Google Scholar
- Chengjin Xu, Fenglong Su, Bo Xiong, and Jens Lehmann. 2022. Time-aware Entity Alignment using Temporal Relational Attention. In WWW. 788–797.Google Scholar
- Donghan Yu, Yiming Yang, Ruohong Zhang, and Yuexin Wu. 2021. Knowledge Embedding Based Graph Convolutional Network. In WWW. 1619–1628.Google Scholar
- Weixin Zeng, Xiang Zhao, Xinyi Li, Jiuyang Tang, and Wei Wang. 2022. On entity alignment at scale. VLDBJ (2022), 1–25.Google Scholar
- Weixin Zeng, Xiang Zhao, Jiuyang Tang, and Xuemin Lin. 2020. Collective Entity Alignment via Adaptive Features. In ICDE. 1870–1873.Google Scholar
- Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Xiaobin Wang, and Min Zhang. 2022. Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations. In ACL. 2801–2813.Google Scholar
- Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, and Pengjun Xie. 2021. Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition. In ACL. 5558–5570.Google Scholar
- Hao Zhu, Ruobing Xie, Zhiyuan Liu, and Maosong Sun. 2017. Iterative Entity Alignment via Joint Knowledge Embeddings. In IJCAI. 4258–4264.Google Scholar
Index Terms
- Unsupervised Entity Alignment for Temporal Knowledge Graphs
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