Relation prediction on knowledge graphs (KGs) aims to infer missing valid triples from
observed ones. Although this task has been deeply studied, most previous studies are …

Active learning methods which present selected examples from the corpus for annotation
provide more efficient learning of supervised relation extraction models, but they leave the …

Abstract Knowledge graph embedding (KGE), which maps entities and relations in a
knowledge graph into continuous vector spaces, has achieved great success in predicting …

Temporal knowledge graph embedding (TKGE) models are commonly utilized to infer the
missing facts and facilitate reasoning and decision-making in temporal knowledge graph …

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in
incomplete triples, whereas the dominant embedding paradigm has a restriction on handling …

Label noise and long-tailed distributions are two major challenges in distantly supervised
relation extraction. Recent studies have shown great progress on denoising, but paid little …

Bi-encoder architectures for distantly-supervised relation extraction are designed to make
use of the complementary information found in text and knowledge graphs (KG). However …

Abstract Multilingual Knowledge Graph Completion (KGC) aims to predict missing links with
multilingual knowledge graphs. However, existing approaches suffer from two main …

In recent years, sentential relation extraction has made remarkable progress with text and
knowledge graphs (KGs). However, existing architectures ignore the valuable information …

The existing knowledge graph embedding (KGE) models based on graph neural networks
(GNNs) typically aggregate unreliable neighboring node information, leading to a decrease …