While textual information significantly enhances the performance of
pre-trained language models (PLMs) in knowledge graph completion (KGC), the
static and noisy nature of existing corpora collected from Wikipedia articles
or synsets definitions often limits the potential of PLM-based KGC models. To
surmount these challenges, we introduce the Contextualization Distillation
strategy, a versatile plug-in-and-play approach compatible with both
discriminative and generative KGC frameworks. Our method begins by instructing
large language models (LLMs) to transform compact, structural triplets into
context-rich segments. Subsequently, we introduce two tailored auxiliary tasks,
reconstruction and contextualization, allowing smaller KGC models to assimilate
insights from these enriched triplets. Comprehensive evaluations across diverse
datasets and KGC techniques highlight the efficacy and adaptability of our
approach, revealing consistent performance enhancements irrespective of
underlying pipelines or architectures. Moreover, our analysis makes our method
more explainable and provides insight into generating path selection, as well
as the choosing of suitable distillation tasks. All the code and data in this
work will be released at
this https URL

通过引入上下文化蒸馏策略，将大型语言模型转化为更加丰富的上下文片段，进而通过引入定制的辅助任务，使得较小的知识图谱补全模型能够吸收这些丰富的三元组，并在各种数据集和知识图谱补全技术的综合评估中展现出卓越的性能提升和适应性，这为路径选择和适当的蒸馏任务提供了可解释性和洞察力。