  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.
