Efficient access to chemical information contained in scientific literature, patents, technical
reports, or the web is a pressing need shared by researchers and patent attorneys from …

Abstract The sheer amount of information about potential adverse drug events publishedin
medical case reports pose major challenges for drug safety experts toperform timely …

This paper presents an overview of automatic methods for building domain knowledge
structures (domain models) from text collections. Applications of domain models have a long …

Extracting drug-drug interaction (DDI) relations is one of the most typical tasks in the field of
biomedical relation extraction. Automatic DDI extraction from the biomedical corpus is …

The typical way for relation extraction is fine-tuning large pre-trained language models on
task-specific datasets, then selecting the label with the highest probability of the output …

In practical scenario, relation extraction needs to first identify entity pairs that have relation
and then assign a correct relation class. However, the number of non-relation entity pairs in …

Extracting knowledge and constructing domain knowledge graphs from used vehicle
maintenance records can reveal the mechanism of faults. However, maintenance records …

Purpose The aim of this study was to assess the impact of automatically detected adverse
event signals from text and open‐source data on the prediction of drug label changes …

We present an approach for extracting relations between named entities from natural
language documents. The approach is based solely on shallow linguistic processing, such …

This paper presents a method to integrate external knowledge sources such as DBpedia
and OpenCyc into an ontology learning system that automatically suggests labels for …