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As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information.
Jiménez-Ruiz, E., Grau, B.C.: Logmap: Logic-based and scalable ontology matching. In: ISWC (1). Lecture Notes in Computer Science, vol. 7031, pp. 273–288. Springer (2011)
2011
Earlier work this paper cites.
Suchanek, F.M., Abiteboul, S., Senellart, P.: PARIS: probabilistic alignment of relations, instances, and schema. Proc. VLDB Endow. 5
2011
Earlier work this paper cites.
Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS. pp. 2787–2795 (2013)
2013
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR (2014)
2014
Earlier work this paper cites.
Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: Dbpedia - A large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web 6
2015
Earlier work this paper cites.
Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: NIPS. pp. 3483–3491 (2015)
2015
Earlier work this paper cites.
Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: ICLR (Poster) (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. pp. 770–778. IEEE Computer Society (2016)
2016
Earlier work this paper cites.
Sun, Z., Hu, W., Li, C.: Cross-lingual entity alignment via joint attribute-preserving embedding. In: ISWC (1). Lecture Notes in Computer Science, vol. 10587, pp. 628–644. Springer (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: NIPS. pp. 5998–6008 (2017)
2017
Earlier work this paper cites.
Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV. pp. 2242–2251. IEEE Computer Society (2017)
2017
Earlier work this paper cites.
Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: CVPR. pp. 7482–7491. Computer Vision Foundation / IEEE Computer Society (2018)
2018
Earlier work this paper cites.
Sun, Z., Hu, W., Zhang, Q., Qu, Y.: Bootstrapping entity alignment with knowledge graph embedding. In: IJCAI. pp. 4396–4402. ijcai.org (2018)
2018
Earlier work this paper cites.
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: ICLR (Poster). OpenReview.net (2018)
2018
Earlier work this paper cites.
Cao, Y., Liu, Z., Li, C., Li, J., Chua, T.: Multi-channel graph neural network for entity alignment. In: ACL (1). pp. 1452–1461. Association for Computational Linguistics (2019)
2019
Earlier work this paper cites.
Li, C., Cao, Y., Hou, L., Shi, J., Li, J., Chua, T.: Semi-supervised entity alignment via joint knowledge embedding model and cross-graph model. In: EMNLP/IJCNLP (1). pp. 2723–2732. Association for Computational Linguistics (2019)
2019
Earlier work this paper cites.
Liu, Y., Li, H., García-Durán, A., Niepert, M., Oñoro-Rubio, D., Rosenblum, D.S.: MMKG: multi-modal knowledge graphs. In: ESWC. Lecture Notes in Computer Science, vol. 11503, pp. 459–474. Springer (2019)
2019
Earlier work this paper cites.
Sun, Z., Huang, J., Hu, W., Chen, M., Guo, L., Qu, Y.: Transedge: Translating relation-contextualized embeddings for knowledge graphs. In: ISWC (1). Lecture Notes in Computer Science, vol. 11778, pp. 612–629. Springer (2019)
2019
Earlier work this paper cites.
Trisedya, B.D., Qi, J., Zhang, R.: Entity alignment between knowledge graphs using attribute embeddings. In: AAAI. pp. 297–304. AAAI Press (2019)
2019
Earlier work this paper cites.
Wu, Y., Liu, X., Feng, Y., Wang, Z., Yan, R., Zhao, D.: Relation-aware entity alignment for heterogeneous knowledge graphs. In: IJCAI. pp. 5278–5284. ijcai.org (2019)
2019
Earlier work this paper cites.
Yang, H., Zou, Y., Shi, P., Lu, W., Lin, J., Sun, X.: Aligning cross-lingual entities with multi-aspect information. In: EMNLP/IJCNLP (1). pp. 4430–4440. Association for Computational Linguistics (2019)
2019
Earlier work this paper cites.
Zhang, Q., Sun, Z., Hu, W., Chen, M., Guo, L., Qu, Y.: Multi-view knowledge graph embedding for entity alignment. In: IJCAI. pp. 5429–5435. ijcai.org (2019)
2019
Cited alongside, same era.
Zhu, Q., Zhou, X., Wu, J., Tan, J., Guo, L.: Neighborhood-aware attentional representation for multilingual knowledge graphs. In: IJCAI. pp. 1943–1949. ijcai.org (2019)
2019
Cited alongside, same era.
Chen, L., Li, Z., Wang, Y., Xu, T., Wang, Z., Chen, E.: MMEA: entity alignment for multi-modal knowledge graph. In: KSEM (1). Lecture Notes in Computer Science, vol. 12274, pp. 134–147. Springer (2020)
2020
Cited alongside, same era.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. In: ICML. Proceedings of Machine Learning Research, vol. 119, pp. 1597–1607. PMLR (2020)
2020
Cited alongside, same era.
Chen, L., Li, Z., Xu, T., Wu, H., Wang, Z., Yuan, N.J., Chen, E.: Multi-modal siamese network for entity alignment. In: KDD. pp. 118–126. ACM (2022)
2022
Later among the works it cites.
Chen, Z., Huang, Y., Chen, J., Geng, Y., Fang, Y., Pan, J.Z., Zhang, N., Zhang, W.: Lako: Knowledge-driven visual question answering via late knowledge-to-text injection. In: IJCKG. pp. 20–29. ACM (2022)
2022
Later among the works it cites.
Fang, Y., Zhang, Q., Yang, H., Zhuang, X., Deng, S., Zhang, W., Qin, M., Chen, Z., Fan, X., Chen, H.: Molecular contrastive learning with chemical element knowledge graph. In: AAAI. pp. 3968–3976. AAAI Press (2022)
2022
Later among the works it cites.
Gao, Y., Liu, X., Wu, J., Li, T., Wang, P., Chen, L.: Clusterea: Scalable entity alignment with stochastic training and normalized mini-batch similarities. In: KDD. pp. 421–431. ACM (2022)
2022
Later among the works it cites.
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Jing, M., Li, J., Zhu, L., Lu, K., Yang, Y., Huang, Z.: Incomplete cross-modal retrieval with dual-aligned variational autoencoders. In: ACM Multimedia. pp. 3283–3291. ACM (2020)
2020
Cited alongside, same era.
Lee, H., Nam, T., Yang, E., Hwang, S.J.: Meta dropout: Learning to perturb latent features for generalization. In: ICLR. OpenReview.net (2020)
2020
Cited alongside, same era.
Liu, Z., Cao, Y., Pan, L., Li, J., Chua, T.: Exploring and evaluating attributes, values, and structures for entity alignment. In: EMNLP (1). pp. 6355–6364. Association for Computational Linguistics (2020)
2020
Cited alongside, same era.
Sun, Z., Wang, C., Hu, W., Chen, M., Dai, J., Zhang, W., Qu, Y.: Knowledge graph alignment network with gated multi-hop neighborhood aggregation. In: AAAI. pp. 222–229. AAAI Press (2020)
2020
Cited alongside, same era.
Sun, Z., Zhang, Q., Hu, W., Wang, C., Chen, M., Akrami, F., Li, C.: A benchmarking study of embedding-based entity alignment for knowledge graphs. Proc. VLDB Endow. 13
2020
Cited alongside, same era.
Tang, X., Zhang, J., Chen, B., Yang, Y., Chen, H., Li, C.: BERT-INT: A bert-based interaction model for knowledge graph alignment. In: IJCAI. pp. 3174–3180. ijcai.org (2020)
2020
Cited alongside, same era.
Wang, M., Wang, H., Qi, G., Zheng, Q.: Richpedia: A large-scale, comprehensive multi-modal knowledge graph. Big Data Res. 22
2020
Cited alongside, same era.
Wu, Y., Liu, X., Feng, Y., Wang, Z., Zhao, D.: Neighborhood matching network for entity alignment. In: ACL. pp. 6477–6487. Association for Computational Linguistics (2020)
2020
Cited alongside, same era.
Lin, Z., Zhang, Z., Wang, M., Shi, Y., Wu, X., Zheng, Y.: Multi-modal contrastive representation learning for entity alignment. In: COLING. pp. 2572–2584. International Committee on Computational Linguistics (2022)
2022
Later among the works it cites.
Sun, Z., Hu, W., Wang, C., Wang, Y., Qu, Y.: Revisiting embedding-based entity alignment: A robust and adaptive method. IEEE Transactions on Knowledge and Data Engineering pp. 1–14 (2022). https://doi.org/10.1109/TKDE.2022.3200981
2022
Later among the works it cites.
Wang, Y., Cui, Y., Liu, W., Sun, Z., Jiang, Y., Han, K., Hu, W.: Facing changes: Continual entity alignment for growing knowledge graphs. In: ISWC. Lecture Notes in Computer Science, vol. 13489, pp. 196–213. Springer (2022)
2022
Later among the works it cites.
Xin, K., Sun, Z., Hua, W., Hu, W., Zhou, X.: Informed multi-context entity alignment. In: WSDM. pp. 1197–1205. ACM (2022)
2022
Later among the works it cites.
Zhong, Z., Zhang, M., Fan, J., Dou, C.: Semantics driven embedding learning for effective entity alignment. In: ICDE. pp. 2127–2140. IEEE (2022)
2022
Later among the works it cites.
Chen, J., Geng, Y., Chen, Z., Pan, J.Z., He, Y., Zhang, W., Horrocks, I., Chen, H.: Zero-shot and few-shot learning with knowledge graphs: A comprehensive survey. Proc. IEEE 111
2023
Closest in time.
Chen, Z., Chen, J., Zhang, W., Guo, L., Fang, Y., Huang, Y., Zhang, Y., Geng, Y., Pan, J.Z., Song, W., Chen, H.: Meaformer: Multi-modal entity alignment transformer for meta modality hybrid. In: ACM Multimedia. ACM (2023)
2023
Closest in time.
Chen, Z., Huang, Y., Chen, J., Geng, Y., Zhang, W., Fang, Y., Pan, J.Z., Chen, H.: Duet: Cross-modal semantic grounding for contrastive zero-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 405–413 (2023)
2023
Closest in time.
Fang, Y., Zhang, Q., Zhang, N., Chen, Z., Zhuang, X., Shao, X., Fan, X., Chen, H.: Knowledge graph-enhanced molecular contrastive learning with functional prompt. Nature Machine Intelligence pp. 1–12 (2023)
2023
Closest in time.
2023
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Hama, K., Matsubara, T.: Multi-modal entity alignment using uncertainty quantification for modality importance. IEEE Access 11
2023
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2023
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2023
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Wang, M., Shi, Y., Yang, H., Zhang, Z., Lin, Z., Zheng, Y.: Probing the impacts of visual context in multimodal entity alignment. Data Sci. Eng. 8
2023
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2023
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Yuan, S., Lu, Z., Li, Q., Gu, J.: A multi-modal entity alignment method with inter-modal enhancement. Big Data and Cognitive Computing 7
2023
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