Multi-perspective Improvement of Knowledge Graph Completion with Large
  Language Models
Derong Xu Ziheng Zhang Zhenxi Lin Xian Wu Zhihong Zhu Tong Xu Xiangyu Zhao Yefeng Zheng Enhong Chen
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.