Low-Dimensional Hyperbolic Knowledge Graph Embeddings

By Ines Chami, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi, Christopher R'e. Annual Meeting of the Association for Computational Linguistics, 2020

475 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/2670f82ba385ac3dab469d5f44ddc5c4fe75dbea

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In Artificial Intelligence

Citations in the corpus

Machine Learning on Graphs: A Model and Comprehensive Taxonomy (2020, background): “For instance, hyperbolic embeddings can represent hierarchical data with much smaller distortion than Euclidean embeddings (Sarkar, 2011) and have led to state-of-the-art results in many modern applications such as link prediction in knowledge graphs (Balazevic et al., 2019; Chami et al., 2020) and ”
A Generalization of Transformer Networks to Graphs (2020, background): “…network architectures on graph datasets and have achieved signiﬁcant success on a wide range of applications, such as in knowledge graphs (Schlichtkrull et al. 2018; Chami et al. 2020), in social sciences (Monti et al. 2019), in physics (Cranmer et al. 2019; Sanchez-Gonzalez et al. 2020), etc.”
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “Chami et al. [30] leverages expressive hyperbolic isometries and learns a relation-speciﬁc absolute curvature c r in the hyperbolic space.”
HittER: Hierarchical Transformers for Knowledge Graph Embeddings (2020, background): “…in the eu-clidean space (Nickel et al., 2011; Yang et al., 2015), numerous complicated transformations in various spaces have been hence proposed (Trouil-lon et al., 2016; Ebisu and Ichise, 2018; Sun et al., 2018; Zhang et al., 2019a; Chami et al., 2020; Tang et al., 2020; Chao et al., 2021).”
Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion (2020, unknown): “358 - AttH[7] .”