Objective and background: The exponential growth of the unstructured data available in
biomedical literature, and Electronic Health Record (EHR), requires powerful novel …

Background Fulfilling the vision of Semantic Web requires an accurate data model for
organizing knowledge and sharing common understanding of the domain. Fitting this …

Successful biomedical data mining and information extraction require a complete picture of
biological phenomena such as genes, biological processes, and diseases; as these exist on …

The explosion of biomedical data and the growing number of disparate data sources are
exposing researchers to a new challenge-how to acquire, maintain and share knowledge …

The abundance of biomedical ontologies is beneficial to the development of biomedical
related systems. However, existing biomedical ontologies such as the National Cancer …

While the biomedical informatics community widely acknowledges the utility of domain
ontologies, there remain many barriers to their effective use. One important requirement of …

Developing ontologies to account for the complexity of biological systems requires the time
intensive collaboration of many participants with expertise in various fields. While each …

Objectives To provide typical examples of biomedical ontologies in action, emphasizing the
role played by biomedical ontologies in knowledge management, data integration and …

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 …

Motivation: Ontologies are essential in biomedical research due to their ability to
semantically integrate content from different scientific databases and resources. Their …