Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …

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

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 …

Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

In this paper, we present a novel method named RECON, that automatically identifies
relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG) …

With the development of the social Internet of Things (IoT) and multimedia communications,
our daily lives in computational social systems have become more convenient; for example …

Distantly supervised relation extraction intrinsically suffers from noisy labels due to the
strong assumption of distant supervision. Most prior works adopt a selective attention …

Fault diagnosis is the key concern in the operation and maintenance of industrial assets. A
fault diagnosis knowledge graph (KG) can provide decision support to the engineers to …

The task of relation extraction is about identifying entities and relations among them in free
text for the enrichment of structured knowledge bases (KBs). In this paper, we present a …

The goal of biomedical relation extraction is to obtain structured information from electronic
medical records by identifying relations among clinical entities. By integrating the …