Region based knowledge graph embeddings represent relations as geometric regions. This has the advantage that the rules which are captured by the model are made explicit, making it straightforward to incorporate prior knowledge and to inspect learned models. Unfortunately, existing approaches are severely restricted in their ability to model relational composition, and hence also their ability to model rules, thus failing to deliver on the main promise of region based models. With the aim of addressing these limitations, we investigate regions which are composed of axis-aligned octagons. Such octagons are particularly easy to work with, as intersections and compositions can be straightforwardly computed, while they are still sufficiently expressive to model arbitrary knowledge graphs. Among others, we also show that our octagon embeddings can properly capture a non-trivial class of rule bases. Finally, we show that our model achieves competitive experimental results.

翻译：基于区域的知识图谱嵌入将关系表示为几何区域。这种方法的优势在于模型捕获的规则是显式的，使得融入先验知识和检查学习模型变得直接。然而，现有方法在建模关系组合（进而建模规则）方面存在严重限制，未能实现基于区域模型的主要承诺。为应对这些局限性，我们研究了由轴对齐八边形构成的区域。此类八边形特别易于处理，因为其交集和组合可直接计算，同时仍具有充分表达能力以建模任意知识图谱。此外，我们还证明八边形嵌入能正确捕获非平凡规则基类。最后，实验结果表明我们的模型取得了具有竞争力的性能。