frugal
发表于 2025-3-23 11:11:01
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tooth-decay
发表于 2025-3-23 15:20:07
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.
Allergic
发表于 2025-3-23 19:46:10
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Schlemms-Canal
发表于 2025-3-23 23:47:37
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单色
发表于 2025-3-24 03:01:22
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
商业上
发表于 2025-3-24 07:38:44
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过滤
发表于 2025-3-24 13:22:11
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
命令变成大炮
发表于 2025-3-24 15:08:45
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Classify
发表于 2025-3-24 19:16:14
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Fulsome
发表于 2025-3-25 01:23:43
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