Relation Prompting with Large Language Models for Text-based Knowledge Graph Completion
Authors: Guangqian Yang, Lei Zhang, Zhendong Mao


Reviewer paper summaries: This paper introduces a method for knowledge graph completion. The overall task proposed by the authors solely involves predicting the tail entity when given the head entity and the relation. To tackle the problem, the authors first used LLMs to generate textual relation descriptions, then used contrastive learning to fine-tune a BERT-based model.  The authors tested on several datasets and their method is near or has beaten SOTA. Overall, the paper is written clearly, but there are some ambiguities and deficiencies in the method section and the experiments section.
Summary of strengths: The paper is written clearly and professionally overall, with rigorous mathematical formulas supporting its claims. It also conducted extensive experiments and studies to show the proposed framework's effectiveness.
Summary of weaknesses: 1.	The authors did not thoroughly discuss the related works. There are some recent works that utilizes LLMs on the task of KGC, including KICGPT (Wei et al., 2023) and Think-on-Graph (Sun et al., 2023).
2.	The paper did not clearly describe its overall framework. In section 3, The authors only have independent sections describing their modules, but not an “overall” section that puts them all together. 
3.	According to the author’s description, they only used BERT as the fine-tuning PLM. There many other larger and better-performing PLMs that are fairly easy to finetune, such as BART and T5, and also RoBERTa which is listed by the authors in the background section. BERT is under-performing compared with the larger PLMs listed above.
4.	The authors should have included more ablation studies and error analysis. Currently, they only tested one hyperparameter, batch size, in section 4.4 as one of the ablation parameters, but there could be much more to test out. For the error analysis and case study, there is no clear guidelines or explanations for the examples provided, and the readers could easily get lost with the tables.
Comments, suggestions and typos: None
Overall assessment scores: 2