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Titlebook: Entity Alignment; Concepts, Recent Adv Xiang Zhao,Weixin Zeng,Jiuyang Tang Book‘‘‘‘‘‘‘‘ 2023 The Editor(s) (if applicable) and The Author(s

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樓主: CLAST
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發(fā)表于 2025-3-23 11:11:01 | 只看該作者
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發(fā)表于 2025-3-23 15:20:07 | 只看該作者
https://doi.org/10.1007/978-3-319-97244-2ast different views of entity representations and augment the limited supervision signals by exploiting the vast unlabeled data. We empirically evaluate our proposal on eight popular KG pairs, and the results demonstrate that our proposed model and its components consistently boost the alignment performance under scarce supervision.
13#
發(fā)表于 2025-3-23 19:46:10 | 只看該作者
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發(fā)表于 2025-3-23 23:47:37 | 只看該作者
15#
發(fā)表于 2025-3-24 03:01:22 | 只看該作者
Translating Statistics to Make Decisionsoses a degree-aware . network that dynamically adjusts the significance of features in a degree-aware manner. Finally, we propose using confident entity alignment results as anchors to complement original KGs with facts from their counterparts via . training during post-alignment. Experimental evalu
16#
發(fā)表于 2025-3-24 07:38:44 | 只看該作者
17#
發(fā)表于 2025-3-24 13:22:11 | 只看該作者
The Toledo School of Translation,ew approach for aligning entities across multiple modalities, which we call hyperbolic multi-modal entity alignment (.). This method expands upon the conventional Euclidean representation by incorporating a hyperboloid manifold. Initially, we utilize hyperbolic graph convolutional networks(.) to acq
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發(fā)表于 2025-3-24 15:08:45 | 只看該作者
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發(fā)表于 2025-3-24 19:16:14 | 只看該作者
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發(fā)表于 2025-3-25 01:23:43 | 只看該作者
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