This paper provides an overview of the research into current medical vocabularies and their impact on searching the Web for health information. The Web provides growing opportunities for laypersons to gain knowledge about specific health conditions, though research to date has been incomplete. Many studies have examined aspects of controlled medical vocabularies. Other studies have examined asp...

Extracting information from mental health records can be useful for large-scale clinical studies (e.g., to predict medication adherence or to understand medication effects) in this clinical specialty largely underserved by the Natural Language Processing (NLP) community. Vocabularies that contain medical terms for specific clinical use-cases, such as signs, symptoms, histories, social risk fact...

Searching of databases (textual or numeric) is likely to be effective and efficient only if the user is familiar with the classification, categorizing, and indexing schemes (metadata vocabularies) being searched. Therefore, it is obviously beneficial to provide a bridge between the user’s ordinary language and the metadata vocabularies of the unfamiliar database in order to compensate for abbre...

In current Semantic Web community, some researches have been done on ranking ontologies, while very little is paid to ranking vocabularies within ontology. However, finding important vocabularies within a given ontology will bring benefits to ontology indexing, ontology understanding and even ranking vocabularies from a global view. In this paper, Vocabulary Dependency Graph (VDG) is proposed t...

BACKGROUND Large biomedical data sets have become increasingly important resources for medical researchers. Modern biomedical data sets are annotated with standard terms to describe the data and to support data linking between databases. The largest curated listing of biomedical terms is the the National Library of Medicine's Unified Medical Language System (UMLS). The UMLS contains more than 2...