Knowledge-Enhanced Relation Extraction Dataset

TL;DR

KERED is a new dataset that combines evidence sentences and knowledge graphs to facilitate the development and evaluation of knowledge-enhanced relation extraction methods, filling a critical gap in available resources.

Contribution

We introduce KERED, the first public dataset providing annotated sentences and knowledge graphs for knowledge-enhanced relation extraction tasks.

Findings

Knowledge graphs in KERED support relation extraction methods.

KERED enables evaluation of knowledge-enhanced relation extraction.

Experimental results validate the usefulness of KERED for the task.

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}