Distantly-supervised relation extraction (RE) is an effective method to scale RE to large
corpora but suffers from noisy labels. Existing approaches try to alleviate noise through multi …

In-depth exploration of the knowledge linkages between science and technology (S&T) is an
essential prerequisite for accurately understanding the S&T innovation laws, promoting the …

A large volume of unstructured data, especially text data, is generated and exchanged daily.
Consequently, the importance of extracting patterns and discovering knowledge from textual …

Continual relation extraction (CRE) is an important task of continual learning, which aims to
learn incessantly emerging new relations between entities from texts. To avoid …

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting
entity relations from texts. However, it usually suffers from the long-tail issue. The training …

工业互联网安全知识图谱能够在丰富安全概念语义关系, 提高安全知识库质量和增强安全态势
可视化分析能力等方面发挥重要作用, 已经成为认知, 溯源和防护针对新能源工业控制系统攻击 …

Relation extraction plays an important role in natural language processing. There is a wide
range of available datasets that benchmark existing relation extraction approaches …

In recent years, creating and managing knowledge bases have become crucial to the retail
product and enterprise domains. We present an automatic knowledge base construction …

Background: The task of relation extraction is a crucial component in the construction of a
knowledge graph. However, it often necessitates a significant amount of manual annotation …

Distantly supervised relation extraction (DSRE) aims to identify the relation between the two
entities (eg name and location). Most existing methods extract semantic features from each …