Knowledge graph completion (KGC) is a widely used method to tackle
incompleteness in knowledge graphs (KGs) by making predictions for missing
links. Description-based KGC leverages pre-trained language models to learn
entity and relation representations with their names or descriptions, which
shows promising results. However, the performance of description-based KGC is
still limited by the quality of text and the incomplete structure, as it lacks
sufficient entity descriptions and relies solely on relation names, leading to
sub-optimal results. To address this issue, we propose MPIKGC, a general
framework to compensate for the deficiency of contextualized knowledge and
improve KGC by querying large language models (LLMs) from various perspectives,
which involves leveraging the reasoning, explanation, and summarization
capabilities of LLMs to expand entity descriptions, understand relations, and
extract structures, respectively. We conducted extensive evaluation of the
effectiveness and improvement of our framework based on four description-based
KGC models and four datasets, for both link prediction and triplet
classification tasks.

MPIKGC 是一个通用框架，利用大型语言模型从不同的角度查询以补偿环境化知识的不足，改进了基于描述的知识图补全方法，扩展了实体描述，理解关系，并提取结构，通过四种基于描述的知识图补全模型和四个数据集进行了广泛评估，适用于链接预测和三元组分类任务。