论文标题
TransHer：翻译知识图嵌入具有超层次限制的嵌入
TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction
论文作者
论文摘要
知识图嵌入方法对于知识图完成（或链接预测）任务很重要。一种现有的有效方法，配对，利用两个单独的向量来模拟知识图中的复杂关系（即1--N，N-to-1和N-to-N）。但是，这种方法严格限制了实体对限制实体分布的优化的超elipsoid表面，从而导致知识图完成的次优性能。为了解决这个问题，我们提出了一种新颖的分数函数transher，该函数利用了头部和尾部实体之间的特定于关系的翻译，以放大超elipsoid限制的约束。通过引入直观且简单的特定关系翻译，Transher可以为优化提供更直接的指导，并捕获具有复杂关系的实体的更多语义特征。实验结果表明，Transher在链路预测上实现了显着的性能改进，并将其概括地概括为不同领域和尺度的数据集。我们的代码可在https://github.com/yizhilll/transher上获得公开。
Knowledge graph embedding methods are important for the knowledge graph completion (or link prediction) task. One existing efficient method, PairRE, leverages two separate vectors to model complex relations (i.e., 1-to-N, N-to-1, and N-to-N) in knowledge graphs. However, such a method strictly restricts entities on the hyper-ellipsoid surfaces which limits the optimization of entity distribution, leading to suboptimal performance of knowledge graph completion. To address this issue, we propose a novel score function TranSHER, which leverages relation-specific translations between head and tail entities to relax the constraint of hyper-ellipsoid restrictions. By introducing an intuitive and simple relation-specific translation, TranSHER can provide more direct guidance on optimization and capture more semantic characteristics of entities with complex relations. Experimental results show that TranSHER achieves significant performance improvements on link prediction and generalizes well to datasets in different domains and scales. Our codes are public available at https://github.com/yizhilll/TranSHER.
