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.

TripleRE 是一种新的知识图谱嵌入方法，其创新性地将关系向量分为三部分并利用残差概念实现更好的性能。此外，使用 NodePiece 对实体进行编码取得了良好的效果，缩小了参数大小并解决了可扩展性的问题，实验表明我们的方法达到了大规模知识图谱数据集的最先进水平，且在其他数据集上也有竞争力。

TripleRE: 通过三倍关系向量进行知识图谱嵌入

TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors

Conventional representation learning algorithms for knowledge graphs (KG) map
each entity to a unique embedding vector. Such a shallow lookup results in a
linear growth of memory consumption for storing the embedding matrix and incurs
high computational costs when working with real-world KGs. Drawing parallels
with subword tokenization commonly used in NLP, we explore the landscape of
more parameter-efficient node embedding strategies with possibly sublinear
memory requirements. To this end, we propose NodePiece, an anchor-based
approach to learn a fixed-size entity vocabulary. In NodePiece, a vocabulary of
subword/sub-entity units is constructed from anchor nodes in a graph with known
relation types. Given such a fixed-size vocabulary, it is possible to bootstrap
an encoding and embedding for any entity, including those unseen during
training. Experiments show that NodePiece performs competitively in node
classification, link prediction, and relation prediction tasks while retaining
less than 10% of explicit nodes in a graph as anchors and often having 10x
fewer parameters. To this end, we show that a NodePiece-enabled model
outperforms existing shallow models on a large OGB WikiKG 2 graph having 70x
fewer parameters.

本文通过借鉴 NLP 中常用的次词元素处理技术，探索更具参数效率的节点嵌入策略，提出了一种基于锚节点的方法 NodePiece，构建了一个定长的子实体单元词汇表，展示了该方法的性能在节点分类、链路预测和关系预测任务中具有竞争力且参数更少。