Knowledge-Enhanced Relation Extraction Dataset
Abstract
Recently, knowledge-enhanced methods leveraging auxiliary knowledge graphs have emerged in relation extraction, surpassing traditional text-based approaches. However, to our best knowledge, there is currently no public dataset available that encompasses both evidence sentences and knowledge graphs for knowledge-enhanced relation extraction. To address this gap, we introduce the Knowledge-Enhanced Relation Extraction Dataset (KERED). KERED annotates each sentence with a relational fact, and it provides knowledge context for entities through entity linking. Using our curated dataset, We compared contemporary relation extraction methods under two prevalent task settings: sentence-level and bag-level. The experimental result shows the knowledge graphs provided by KERED can support knowledge-enhanced relation extraction methods. We believe that KERED offers high-quality relation extraction datasets with corresponding knowledge graphs for evaluating the performance of knowledge-enhanced relation extraction methods. Our dataset is available at: \url{https://figshare.com/projects/KERED/134459}
Cite
@article{arxiv.2210.11231,
title = {Knowledge-Enhanced Relation Extraction Dataset},
author = {Yucong Lin and Hongming Xiao and Jiani Liu and Zichao Lin and Keming Lu and Feifei Wang and Wei Wei},
journal= {arXiv preprint arXiv:2210.11231},
year = {2023}
}
Comments
20 pages, 6 figures, submitted to Neural Computing and Applications