A step towards quantifying, modelling and exploring uncertainty in biomedical knowledge graphs.

Objective: This study aims at automatically quantifying and modelling the uncertainty of facts in biomedical knowledge graphs (BKGs) based on their textual supporting evidence using deep learning techniques.

Materials And Methods: A sentence transformer is employed to extract deep features of sentences used to classify sentence factuality using a naive Bayes classifier. For each fact and its supporting evidence in a source KG, the deep feature extractor and the classifier are used to quantify the factuality of each sentence which are then transformed to numerical values in [0,1] before being averaged to get the confidence score of the fact.

COVID-19-Related Scientific Literature Exploration: Short Survey and Comparative Study.

The urgency of the COVID-19 pandemic caused a surge in the related scientific literature. This surge made the manual exploration of scientific articles time-consuming and inefficient. Therefore, a range of exploratory search applications have been created to facilitate access to the available literature.