Learning Representations for Hyper-Relational Knowledge Graphs
This work addresses a bottleneck in hyper-relational knowledge graph completion for AI applications, representing an incremental improvement over prior methods.
The paper tackles the problem of learning representations for hyper-relational knowledge graphs, where existing methods overlook information flow from base triples to qualifiers, leading to suboptimal qualifier representations. The proposed framework uses multiple aggregators to address this, demonstrating effectiveness in hyper-relational knowledge graph completion across multiple datasets.
Knowledge graphs (KGs) have gained prominence for their ability to learn representations for uni-relational facts. Recently, research has focused on modeling hyper-relational facts, which move beyond the restriction of uni-relational facts and allow us to represent more complex and real-world information. However, existing approaches for learning representations on hyper-relational KGs majorly focus on enhancing the communication from qualifiers to base triples while overlooking the flow of information from base triple to qualifiers. This can lead to suboptimal qualifier representations, especially when a large amount of qualifiers are presented. It motivates us to design a framework that utilizes multiple aggregators to learn representations for hyper-relational facts: one from the perspective of the base triple and the other one from the perspective of the qualifiers. Experiments demonstrate the effectiveness of our framework for hyper-relational knowledge graph completion across multiple datasets. Furthermore, we conduct an ablation study that validates the importance of the various components in our framework. The code to reproduce our results can be found at \url{https://github.com/HarryShomer/QUAD}.