Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion
This addresses the challenge of incomplete knowledge graphs for AI applications, but it is incremental as it builds on existing KGC methods by focusing on shared knowledge.
The paper tackles the problem of Knowledge Graph Completion (KGC) by leveraging shared knowledge from datasets and subtasks, resulting in a method that significantly outperforms existing text-based methods, with the most notable improvement on the WN18RR dataset.
In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance. However, no current studies specifically address the shared knowledge within KGC. To bridge this gap, we introduce a multi-level Shared Knowledge Guided learning method (SKG) that operates at both the dataset and task levels. On the dataset level, SKG-KGC broadens the original dataset by identifying shared features within entity sets via text summarization. On the task level, for the three typical KGC subtasks - head entity prediction, relation prediction, and tail entity prediction - we present an innovative multi-task learning architecture with dynamically adjusted loss weights. This approach allows the model to focus on more challenging and underperforming tasks, effectively mitigating the imbalance of knowledge sharing among subtasks. Experimental results demonstrate that SKG-KGC outperforms existing text-based methods significantly on three well-known datasets, with the most notable improvement on WN18RR.