Quaternion Knowledge Graph Embeddings
Shuai Zhang, Yi Tay, Lina Yao, Qi Liu

TL;DR
This paper introduces quaternion-based hypercomplex embeddings for knowledge graphs, offering more expressive and geometrically interpretable representations that outperform previous models on standard benchmarks.
Contribution
It presents a novel quaternion embedding framework that generalizes ComplEx, capturing complex relational patterns with improved expressiveness and interpretability.
Findings
Achieves state-of-the-art results on four benchmarks
Models symmetry, anti-symmetry, and inversion effectively
Provides a more compact and expressive representation of entities and relations
Abstract
In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge graph embeddings. More specifically, quaternion embeddings, hypercomplex-valued embeddings with three imaginary components, are utilized to represent entities. Relations are modelled as rotations in the quaternion space. The advantages of the proposed approach are: (1) Latent inter-dependencies (between all components) are aptly captured with Hamilton product, encouraging a more compact interaction between entities and relations; (2) Quaternions enable expressive rotation in four-dimensional space and have more degree of freedom than rotation in complex plane; (3) The proposed framework is a generalization of ComplEx on hypercomplex space while offering better geometrical interpretations, concurrently…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Graph Theory and Algorithms · Cognitive Computing and Networks
