Abstract
Knowledge graph (KG) embeddings learn low- dimensional representations of entities and relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which must be preserved in the embedding space. For hierarchical data, hyperbolic embedding methods have shown promise for high-fidelity and parsimonious representations. However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs. In this work, we introduce a class of hyperbolic KG embedding models that simultaneously capture hierarchical and logical patterns. Our approach combines hyperbolic reflections and rotations with attention to model complex relational patterns. Experimental results on standard KG benchmarks show that our method improves over previous Euclidean- and hyperbolic-based efforts by up to 6.1% in mean reciprocal rank (MRR) in low dimensions. Furthermore, we observe that different geometric transformations capture different types of relations while attention- based transformations generalize to multiple relations. In high dimensions, our approach yields new state-of-the-art MRRs of 49.6% on WN18RR and 57.7% on YAGO3-10.
Highlights
Knowledge graphs (KGs), consisting of triples, are popular data structures for representing factual knowledge to be queried and used in downstream applications such as word sense disambiguation, question answering, and information extraction
We evaluate the performance of our approach, ATTH, on the KG link prediction task using the standard WN18RR (Dettmers et al, 2018; Bordes et al, 2013), FB15k-237 (Toutanova and Chen, 2015) and YAGO3-10 (Mahdisoltani et al, 2013) benchmarks
Ablations To analyze the benefits of hyperbolic geometry, we evaluate the performance of ATTE, which is equivalent to ATTH with curvatures set to zero
Summary
Knowledge graphs (KGs), consisting of (head entity, relationship, tail entity) triples, are popular data structures for representing factual knowledge to be queried and used in downstream applications such as word sense disambiguation, question answering, and information extraction.
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