DisenE：解开知识图谱嵌入
DisenE: Disentangling Knowledge Graph Embeddings
摘要：
知识图谱嵌入是将实体和关系嵌入到低维向量中的一种方法，近年来受到了广泛的关注。然而，现有的研究主要是基于黑盒神经模型，这使得它很难解释的学习表示。在本文中，我们介绍了DisenE，一个端到端的框架，学习解开知识图谱嵌入。特别地，我们引入了一种基于注意力的机制，使模型能够根据给定的关系显式地关注实体嵌入的相关组件。此外，我们引入了两个新的正则化，以鼓励实体表示的每个组件独立地反映一个孤立的语义方面。实验结果表明，我们提出的DisenE调查的角度，以解决KGE的可解释性，并被证明是一种有效的方法，以提高性能的链接预测任务。
英文摘要：
Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently. However, the existing research is mainly based on the black-box neural models, which makes it difficult to interpret the learned representation. In this paper, we introduce DisenE, an end-to-end framework to learn disentangled knowledge graph embeddings. Specially, we introduce an attention-based mechanism that enables the model to explicitly focus on relevant components of entity embeddings according to a given relation. Furthermore, we introduce two novel regularizers to encourage each component of the entity representation to independently reflect an isolated semantic aspect. Experimental results demonstrate that our proposed DisenE investigates a perspective to address the interpretability of KGE and is proved to be an effective way to improve the performance of link prediction tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
