Relphormer: Relational Graph Transformer for Knowledge Graph Representations (2205.10852v6)

Abstract: Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer architectures have not yielded promising improvements in the Knowledge Graph (KG) representations, where the translational distance paradigm dominates this area. Note that vanilla Transformer architectures struggle to capture the intrinsically heterogeneous structural and semantic information of knowledge graphs. To this end, we propose a new variant of Transformer for knowledge graph representations dubbed Relphormer. Specifically, we introduce Triple2Seq which can dynamically sample contextualized sub-graph sequences as the input to alleviate the heterogeneity issue. We propose a novel structure-enhanced self-attention mechanism to encode the relational information and keep the semantic information within entities and relations. Moreover, we utilize masked knowledge modeling for general knowledge graph representation learning, which can be applied to various KG-based tasks including knowledge graph completion, question answering, and recommendation. Experimental results on six datasets show that Relphormer can obtain better performance compared with baselines. Code is available in https://github.com/zjunlp/Relphormer.

• The paper introduces Relphormer, a Transformer model effectively capturing knowledge graph structure using Triple2Seq and structure-enhanced attention.
• Relphormer demonstrates superior performance over existing models in knowledge graph completion and other tasks on benchmark datasets like FB15K-237 and WN18RR.
• This work advances semantic representation in knowledge-intensive applications like search engines and recommendation systems, opening new research avenues for graph-based Transformer models.

An Analytical Overview of Relphormer: Relational Graph Transformer for Knowledge Graph Representations

The paper “Relphormer: Relational Graph Transformer for Knowledge Graph Representations” presents a novel approach to leverage Transformer architectures in the domain of Knowledge Graphs (KGs). This paper proposes a mechanism to enhance the representation capabilities of Transformers by addressing inherent challenges of the KG landscape, such as heterogeneity, topological structure, and task universality. The proposed model, Relphormer, introduces several innovations to address these challenges and demonstrates competitive performance across various benchmark datasets.

Key Contributions

1. Triple2Seq Mechanism: The authors introduce the concept of Triple2Seq to tackle the significant heterogeneity present in KGs. This mechanism dynamically samples contextualized sub-graph sequences, which are then fed into the transformer to improve the capture of heterogeneous structural and semantic information. By treating relations as nodes, Triple2Seq allows for maintaining critical contextual and semantic features within sub-graph structures.
2. Structure-Enhanced Self-Attention: A novel structure-enhanced self-attention mechanism is proposed to incorporate the relational and topological intricacies of knowledge graphs into the Transformer model. This enhancement aids in bridging the gap between structural encoding and pure sequence modeling, thus preserving essential structural information that could be lost due to the fully connected nature of traditional self-attention mechanisms.
3. Masked Knowledge Modeling: Drawing an analogy from the success of Masked LLMing in NLP, the authors propose masked knowledge modeling as a unified optimization objective for KG representation learning. This approach optimizes for entity and relation prediction simultaneously, which contrasts with the traditionally costly scoring of all possible triples in inference stages.

Experimental Insights

The Relphormer demonstrates superior performance in several tasks including knowledge graph completion, question answering, and recommendation systems. It outperforms existing models such as TransE, ComplEx, and RotatE across datasets like FB15K-237, WN18RR, and UMLS by efficiently modeling complex relationships and offering considerable inference speed advantages. Notably, in entity prediction tasks on WN18RR, Relphormer achieves an improved Hits@1 rate compared to competing models, indicating its capacity to make precise predictions.

Implications and Potential Developments

Practically, the Relphormer pushes forward the capabilities of semantic representation within knowledge-intensive applications such as search engines and recommendation systems. Theoretically, it contributes to the evolving discourse on the application of Transformer models within graph-based data structures.

The approach not only demonstrates an increased efficiency in the transformational representation of KGs but also opens avenues for future research. This includes exploring further optimizations in sampling strategies, possibly using anchor-based methods to refine contextual input sequences, and improving cross-task generalization capabilities.

Conclusion

This paper effectively addresses key limitations in applying vanilla Transformers to KGs by proposing the Relphormer model, designed to embrace the structural richness of knowledge graphs. As such, it paves a path forward for developing Transformer variants tailored for graph representation tasks, promising significant utility for diverse knowledge-based applications. This work stands as an illustrative example of bridging cutting-edge machine learning techniques with classical graph representation challenges, likely inspiring sustained research into the optimization and practical utility of Transformer architectures within heterogeneous domains.