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。其优势在于模型所捕获的规则得以显式表达，从而便于融入先验知识并检查学习到的模型。不幸的是，现有方法在建模关系组合方面受到严重限制，因而在规则建模能力上亦显不足，未能兑现区域模型的主要承诺。为克服这些局限，我们研究了由轴对齐八边形构成的区域。这类八边形特别易于处理，交集与组合可以简便计算，同时仍具有足够的表现力来建模任意知识图谱。我们还特别证明，八边形嵌入能够正确捕获一类非平凡的规则库。最后，实验结果表明我们的模型取得了有竞争力的性能。