Background Databases are fundamental to advance biomedical science. However, most of
them are populated and updated with a great deal of human effort. Biomedical Relation …

Injecting external knowledge can improve the performance of pre-trained language models
(PLMs) on various downstream NLP tasks. However, massive retraining is required to …

Disease knowledge graphs have emerged as a powerful tool for artificial intelligence to
connect, organize, and access diverse information about diseases. Relations between …

Neural models for distantly supervised relation extraction (DS-RE) encode each sentence in
an entity-pair bag separately. These are then aggregated for bag-level relation prediction …

Sentence-level relation extraction (RE) has a highly imbalanced data distribution that about
80% of data are labeled as negative, ie, no relation; and there exist minority classes (MC) …

Distantly supervised relation extraction (DSRE) aims to identify semantic relations from
massive plain texts. A broad range of the prior research has leveraged a series of selective …

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 …

Distantly supervised relation extraction aims to extract relational facts from texts but suffers
from noisy instances. Existing methods usually select reliable sentences that rely on …

Due to the advancement of technology, cybercrime has increased considerably, making
digital forensics essential for any organisation. One of the most critical challenges is to …

Relation extraction in the biomedical domain is challenging due to the lack of labeled data
and high annotation costs, needing domain experts. Distant supervision is commonly used …