Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion

TL;DR

This paper introduces a multi-level shared knowledge guided learning method for knowledge graph completion that leverages shared dataset and task-level knowledge to improve performance across multiple subtasks.

Contribution

The paper proposes a novel multi-level learning framework that utilizes shared knowledge at dataset and task levels, with a dynamic multi-task architecture for better subtask performance.

Findings

Outperforms existing text-based methods on three datasets.

Achieves significant improvement on WN18RR.

Effectively mitigates knowledge sharing imbalance among subtasks.

Abstract

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.