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Knowledge Base Embedding for Sampling-Based Prediction
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DOI：10.1145/3533769.png)
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摘要
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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.
Keyword:
Link prediction
Knowledge Base Embedding
Conditional Variational AutoEncoder
期刊
IF:
9.1
论文数:
1.2K
被引数:
4.7K
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