In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-
trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved …

Extracting relational triples from unstructured text is crucial for large-scale knowledge graph
construction. However, few existing works excel in solving the overlapping triple problem …

Joint extraction of entities and relations from unstructured texts is a crucial task in information
extraction. Recent methods achieve considerable performance but still suffer from some …

In this paper, we present GraphRel, an end-to-end relation extraction model which uses
graph convolutional networks (GCNs) to jointly learn named entities and relations. In …

Joint extraction of entities and relations is an important task in information extraction. To
tackle this problem, we firstly propose a novel tagging scheme that can convert the joint …

Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

A relation tuple consists of two entities and the relation between them, and often such tuples
are found in unstructured text. There may be multiple relation tuples present in a text and …

Joint extraction of entities and relations aims to detect entity pairs along with their relations
using a single model. Prior work typically solves this task in the extract-then-classify or …