Background Ontologies in biomedicine facilitate information integration, data exchange,
search and query of biomedical data, and other critical knowledge-intensive tasks. The OBO …

The increasing interest in biomedical ontologies has provoked the development of a
significant number of ontologies, and many more are expected to be produced in the near …

Background: Automatic recognition of medical concepts in unstructured text is an important
component of many clinical and research applications, and its accuracy has a large impact …

Background Named entity recognition is critical for biomedical text mining, where it is not
unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are …

The automatic processing of clinical documents, such as Electronic Health Records (EHRs),
could benefit substantially from the enrichment of medical terminologies with terms …

We investigate the current extent of term reuse and overlap among biomedical ontologies.
We use the corpus of biomedical ontologies stored in the BioPortal repository, and analyze …

Text Mining in biology and biomedicine requires a large amount of domain-specific
knowledge. Publicly accessible resources hold much of the information needed, yet their …

The ShARe/CLEF eHealth Evaluation Lab (SHEL) organized a challenge on natural
language processing (NLP) and information retrieval (IR) in the medical domain in 2013 …

The field of biomedicine has embraced the Semantic Web probably more than any other
field. As a result, there is a large number of biomedical ontologies covering overlapping …

We motivate the need for challenging problems in the evaluation of ontology matching tools.
To address this need, we propose mapping sets between well-known biomedical ontologies …