Capturing Knowledge Graphs and Rules with Octagon Embeddings
CoRR(2024)
摘要
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.
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