Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction (1911.09419v3)

Abstract: Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symmetry/antisymmetry, inversion, and composition. However, many existing approaches fail to model semantic hierarchies, which are common in real-world applications. To address this challenge, we propose a novel knowledge graph embedding model -- namely, Hierarchy-Aware Knowledge Graph Embedding (HAKE) -- which maps entities into the polar coordinate system. HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy. Specifically, the radial coordinate aims to model entities at different levels of the hierarchy, and entities with smaller radii are expected to be at higher levels; the angular coordinate aims to distinguish entities at the same level of the hierarchy, and these entities are expected to have roughly the same radii but different angles. Experiments demonstrate that HAKE can effectively model the semantic hierarchies in knowledge graphs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the link prediction task.

• The paper introduces HAKE, a model that leverages polar coordinates to effectively encode semantic hierarchies in knowledge graphs for enhanced link prediction.
• HAKE distinguishes entities through radial and angular components, outperforming traditional models like TransE and DistMult in benchmark evaluations.
• Its design requires no extra hierarchical data, offering improved performance in applications such as natural language processing, recommendation systems, and question answering.

An Expert Perspective on "Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction"

The paper "Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction" by Zhanqiu Zhang et al. introduces an innovative model named Hierarchy-Aware Knowledge Graph Embedding (HAKE), crafted to address the semantic hierarchies in knowledge graphs. Focusing on the capability to represent entities at various hierarchical levels, HAKE utilizes the polar coordinate system to encode entities and relations efficiently, delivering superior performance in link prediction tasks.

The authors highlight a crucial limitation in existing knowledge graph embedding models—namely, their inability to effectively model semantic hierarchies inherent in real-world applications. Traditional methods, including famous translational and bilinear models like TransE and DistMult, are primarily concerned with relational patterns such as symmetry and composition but fall short of capturing hierarchical semantics.

HAKE innovates by leveraging the polar coordinate system, where the radial and angular coordinates correspond to different aspects of hierarchical differentiation. The radial component serves to distinguish entities at varying levels of hierarchy, hypothesizing that entities with smaller radial values reside at higher hierarchical levels. Conversely, entities sharing the same hierarchical level are differentiated through variations in angular coordinates.

Notably, HAKE outperforms existing models by effectively distinguishing and encoding entities at the same hierarchical levels. This advancement is demonstrated through robust experimental results on benchmark datasets such as WN18RR, FB15k-237, and YAGO3-10, with the model significantly surpassing the performance of prior state-of-the-art methods. For instance, HAKE achieves higher Mean Reciprocal Rank (MRR) and Hits at N metrics on these datasets compared to the previous best model, RotatE.

A key aspect of HAKE's architecture is its design for automatic learning of hierarchy without the necessity of additional data or preprocessing steps typical of models like TKRL, which rely on embedding types or clustering to integrate hierarchical information. The integration of modulus and phase components allows HAKE to deliver superior expressivity in capturing both syntactic and semantic properties of knowledge graphs.

The implications of this research extend to practical applications, including enhanced capability for tasks such as natural language processing, recommendation systems, and question answering, where knowledge graphs are widely used. Theoretically, HAKE's approach opens pathways for further exploration into geometric embedding spaces for complex hierarchical structures inherent in various data forms.

Future developments might focus on scaling HAKE for even larger knowledge graphs and exploring its integration with more sophisticated neural architectures, potentially involving attention mechanisms or transformers, thereby enhancing its adaptability and performance across diverse domains.

In conclusion, HAKE represents a substantial progression in knowledge graph embeddings, providing a robust mechanism to model intricate semantic hierarchies effectively. Its proficiency in link prediction tasks underscores its potential impact and applicability across numerous fields reliant on semantic understanding and inference within complex knowledge structures.