Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …

Pre-trained language representation models (PLMs) cannot well capture factual knowledge
from text. In contrast, knowledge embedding (KE) methods can effectively represent the …

Neural language representation models such as BERT pre-trained on large-scale corpora
can well capture rich semantic patterns from plain text, and be fine-tuned to consistently …

The existing methods for relation classification (RC) primarily rely on distant supervision
(DS) because large-scale supervised training datasets are not readily available. Although …

Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

Graph-structured data arise naturally in many different application domains. By representing
data as graphs, we can capture entities (ie, nodes) as well as their relationships (ie, edges) …

We propose a distance supervised relation extraction approach for long-tailed, imbalanced
data which is prevalent in real-world settings. Here, the challenge is to learn accurate" few …

Abstract Knowledge Graphs (KGs), as a structured human knowledge, manage data in an
ease-of-store, recognizable, and understandable way for machines and provide a rich …

Entity alignment aims at integrating complementary knowledge graphs (KGs) from different
sources or languages, which may benefit many knowledge-driven applications. It is …