Background Biomedical ontologies pose several challenges to ontology matching due both
to the complexity of the biomedical domain and to the characteristics of the ontologies …

The use of similarity measures in various domains is cornerstone for different tasks ranging
from ontology alignment to information retrieval. To this end, existing metrics can be …

Background Although there is an enormous number of textual resources in the biomedical
domain, currently, manually curated resources cover only a small part of the existing …

Motivation Concept recognition in biomedical text is an important yet challenging task. The
two main approaches to recognize concepts in text are dictionary-based approaches and …

Medical Terminologies play a vital role in clinical data capture, reporting, information
integration, indexing and retrieval. The Web Ontology language (OWL)[1] provides an …

Advances in natural language processing techniques, such as named entity recognition and
normalization to widely used standardized terminologies like UMLS or SNOMED-CT, along …

Ontologies are recognised as important tools, not only for effective and efficient information
sharing, but also for information extraction and text mining. In the biomedical domain, the …

The Semantic Web can be conceived as an extension of the current Web where information
is given well-defined meaning. In this scenario ontologies are crucial since they provide …

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 …

Ontologies are now pervasive in biomedicine, where they serve as a means to standardize
terminology, to enable access to domain knowledge, to verify data consistency and to …