Progressively Modality Freezing for Multi-Modal Entity Alignment (2407.16168v1)
Abstract: Multi-Modal Entity Alignment aims to discover identical entities across heterogeneous knowledge graphs. While recent studies have delved into fusion paradigms to represent entities holistically, the elimination of features irrelevant to alignment and modal inconsistencies is overlooked, which are caused by inherent differences in multi-modal features. To address these challenges, we propose a novel strategy of progressive modality freezing, called PMF, that focuses on alignmentrelevant features and enhances multi-modal feature fusion. Notably, our approach introduces a pioneering cross-modal association loss to foster modal consistency. Empirical evaluations across nine datasets confirm PMF's superiority, demonstrating stateof-the-art performance and the rationale for freezing modalities. Our code is available at https://github.com/ninibymilk/PMF-MMEA.
- Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26.
- Mmea: entity alignment for multi-modal knowledge graph. In Knowledge Science, Engineering and Management: 13th International Conference, KSEM 2020, Hangzhou, China, August 28–30, 2020, Proceedings, Part I 13, pages 134–147. Springer.
- Multi-modal siamese network for entity alignment. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pages 118–126.
- Meaformer: Multi-modal entity alignment transformer for meta modality hybrid. In Proceedings of the 31st ACM International Conference on Multimedia, pages 3317–3327.
- Rethinking uncertainly missing and ambiguous visual modality in multi-modal entity alignment. In International Semantic Web Conference, pages 121–139. Springer.
- Utilizing knowledge graphs for text-centric information retrieval. In The 41st international ACM SIGIR conference on research & development in information retrieval, pages 1387–1390.
- Contrastive multi-modal knowledge graph representation learning. IEEE Transactions on Knowledge and Data Engineering.
- Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
- Attribute-consistent knowledge graph representation learning for multi-modal entity alignment. arXiv preprint arXiv:2304.01563.
- Multi-modal contrastive representation learning for entity alignment. arXiv preprint arXiv:2209.00891.
- Visual pivoting for (unsupervised) entity alignment. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 4257–4266.
- Mmkg: multi-modal knowledge graphs. In The Semantic Web: 16th International Conference, ESWC 2019, Portorož, Slovenia, June 2–6, 2019, Proceedings 16, pages 459–474. Springer.
- From alignment to assignment: Frustratingly simple unsupervised entity alignment. arXiv preprint arXiv:2109.02363.
- A multimodal translation-based approach for knowledge graph representation learning. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 225–234.
- Psnea: Pseudo-siamese network for entity alignment between multi-modal knowledge graphs. In Proceedings of the 31st ACM International Conference on Multimedia, pages 3489–3497.
- Embedding multimodal relational data for knowledge base completion. arXiv preprint arXiv:1809.01341.
- Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR.
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
- Multi-modal knowledge graphs for recommender systems. In Proceedings of the 29th ACM international conference on information & knowledge management, pages 1405–1414.
- Cross-lingual entity alignment via joint attribute-preserving embedding. In The Semantic Web–ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I 16, pages 628–644. Springer.
- A benchmarking study of embedding-based entity alignment for knowledge graphs. arXiv preprint arXiv:2003.07743.
- Image-embodied knowledge representation learning. arXiv preprint arXiv:1609.07028.
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