TripleRE：基于三重关系向量的知识图谱嵌入
TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors
摘要：
基于翻译的知识图谱嵌入是transE提出以来知识表示学习的重要分支之一。虽然近年来许多基于预处理的方法取得了一定的进展，但其性能仍然不尽如人意。提出了一种新的知识图谱嵌入方法TripleRE，该方法具有两个版本。TripleRE的第一个版本创造性地将关系向量分为三个部分。第二个版本利用了残差的概念，并实现了更好的性能。此外，尝试使用NodePiece来编码实体在减少参数大小方面取得了令人满意的结果，并解决了可扩展性问题。实验结果表明，该方法在大规模知识图谱数据集上取得了较好的性能，在其他数据集上也取得了较好的性能。
英文摘要：
Translation-based knowledge graph embedding has been one of the most important branches for knowledge representation learning since TransE came out. Although many translation-based approaches have achieved some progress in recent years, the performance was still unsatisfactory. This paper proposes a novel knowledge graph embedding method named TripleRE with two versions. The first version of TripleRE creatively divide the relationship vector into three parts. The second version takes advantage of the concept of residual and achieves better performance. In addition, attempts on using NodePiece to encode entities achieved promising results in reducing the parametric size, and solved the problems of scalability. Experiments show that our approach achieved state-of-the-art performance on the large-scale knowledge graph dataset, and competitive performance on other datasets.
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