@article{MCF4D0439,
title = "Generating Relation Descriptions with Large Language Model for Link Prediction",
journal = "Journal of KIISE, JOK",
year = "2024",
issn = "2383-630X",
doi = "10.5626/JOK.2024.51.10.908",
author = "Hyunmook Cha, Youngjoong Ko",
keywords = "link prediction, knowledge graph, large language model, pre-trained language model",
abstract = "The Knowledge Graph is a network consisting of entities and the relations between
them. It is used for various natural language processing tasks. One specific task related to the
Knowledge Graph is Knowledge Graph Completion, which involves reasoning with known facts in the
graph and automatically inferring missing links. In order to tackle this task, studies have been
conducted on both link prediction and relation prediction. Recently, there has been significant interest
in a dual-encoder architecture that utilizes textual information. However, the dataset for link prediction
only provides descriptions for entities, not for relations. As a result, the model heavily relies on
descriptions for entities. To address this issue, we utilized a large language model called
GPT-3.5-turbo to generate relation descriptions. This allows the baseline model to be trained with
more comprehensive relation information. Moreover, the relation descriptions generated by our proposed method are expected to improve the performance of other language model-based link
prediction models. The evaluation results for link prediction demonstrate that our proposed method
outperforms the baseline model on various datasets, including Korean ConceptNet, WN18RR,
FB15k-237, and YAGO3-10. Specifically, we observed improvements of 0.34%p, 0.11%p, 0.12%p, and
0.41%p in terms of Mean Reciprocal Rank (MRR), respecitvely."
}