论文标题
MEKER：记忆有效的知识嵌入表示链接预测和问题答案的表示
MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering
论文作者
论文摘要
知识图（kgs）是事实的象征性结构化遗产。 KG嵌入包含NLP任务中使用的简洁数据，这些数据需要有关现实世界的隐式信息。此外，在实际的NLP分配中可能有用的kg的大小是巨大的，并且在其上创建嵌入具有内存成本问题。我们将kg表示为三阶二进制张量，并通过使用它的数据特异性广义版本超越了标准的CP分解。标准CP-ALS算法的概括允许在没有反向传播机制的情况下获得优化梯度。它减少了训练所需的记忆，同时提供计算福利。我们提出了一个Meker，这是一种记忆效率高的KG嵌入模型，该模型在链接预测任务和基于KG的问题答案上产生了可相比的性能。
Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG embedding contains concise data used in NLP tasks requiring implicit information about the real world. Furthermore, the size of KGs that may be useful in actual NLP assignments is enormous, and creating embedding over it has memory cost issues. We represent KG as a 3rd-order binary tensor and move beyond the standard CP decomposition by using a data-specific generalized version of it. The generalization of the standard CP-ALS algorithm allows obtaining optimization gradients without a backpropagation mechanism. It reduces the memory needed in training while providing computational benefits. We propose a MEKER, a memory-efficient KG embedding model, which yields SOTA-comparable performance on link prediction tasks and KG-based Question Answering.
