3 accepted papers
Embedding methods are among the most efficient approaches for learning to reason about relational knowledge. In this paper, we focus on the framework of region-based embeddings, where relations are encoded as geometric regions. The spatial arrangement of these regions allows such models to capture s…
In recent years, the field of knowledge graph completion has focused on increasingly sophisticated models, which perform well on link prediction tasks, but are less scalable than earlier methods and are not suitable for learning entity embeddings. As a result, shallow models such as TransE and Compl
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 se…