Abstract Biomedical Entity Linking (BEL) is the task of map** of spans of text within
biomedical documents to normalized, unique identifiers within an ontology. This is an …

Motivation With the exponential growth of the life sciences literature, biomedical text mining
(BTM) has become an essential technology for accelerating the extraction of insights from …

The existence of unlinkable (NIL) entities is a major hurdle affecting the performance of
Named Entity Linking approaches, and, consequently, the performance of downstream …

Semantic data integration provides the ability to interrelate and analyze information from
multiple heterogeneous resources. With the growing complexity of medical ontologies and …

With the exponential growth of the life science literature, biomedical text mining (BTM) has
become an essential technology for accelerating the extraction of insights from publications …

Background Identifying chemical mentions within the Alzheimer's and dementia literature
can provide a powerful tool to further therapeutic research. Leveraging the Chemical Entities …

The large, and increasing, number of chemical compounds poses challenges to the
exploration of such datasets. In this work, we propose the usage of recommender systems to …

State-of-the-art deep learning entity linking methods rely on extensive human-labelled data,
which is costly to acquire. Current datasets are limited in size, leading to inadequate …

Natural language processing (NLP) is a field of study that focuses on data analysis on texts
with certain methods. NLP includes tasks such as sentiment analysis, spam detection, entity …

Recommender systems (RS) have been successfully explored in a vast number of domains,
eg movies and tv shows, music, or e-commerce. In these domains we have a large number …