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• W4313426558 abstract "Each link prediction task requires different degrees of answer diversity. While a link prediction task may expect up to a couple of answers, another may expect nearly a hundred answers. Given this fact, the performance of a link prediction model can be estimated more accurately if a flexible number of obtained answers are estimated instead of a predefined number of answers. Inspired by this, in this article, we analyze two evaluation criteria for link prediction tasks, respectively ranking-based protocol and sampling-based protocol. Furthermore, we study two classes of models on link prediction task, direct model and latent-variable model respectively, to demonstrate that latent-variable model performs better under the sampling-based protocol. We then propose a latent-variable model where the framework of Conditional Variational AutoEncoder (CVAE) is applied. Experimental study suggests that the proposed model performs comparably to the current state-of-the-art even under the conventional rank-based protocol. Under the sampling-based protocol, the proposed model is shown to outperform various state-of-the-art models." @default.
• W4313426558 created "2023-01-06" @default.
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• W4313426558 date "2023-04-08" @default.
• W4313426558 modified "2023-10-17" @default.
• W4313426558 title "Knowledge Base Embedding for Sampling-Based Prediction" @default.
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• W4313426558 doi "https://doi.org/10.1145/3533769" @default.
• W4313426558 hasPublicationYear "2023" @default.
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