Over 50 hand-curated vocabularies/ontologies containing more than 20 million synonyms

VOCabs - Semantic Ontologies Powering Search

Why use an ontology in text mining?  Scientists today simply don’t have time to read all of the available content relevant to their research.  Computational approaches help to sift through and identify relevant material from multiple sources yet without the support of an ontology or controlled vocabulary they struggle to deal with the ambiguity of scientific literature.  Multiple terms can be used to describe the same topic making any key word search difficult.

Biomedical ontologies for entity identification

 

A foundation to text mining success

Accurate detection of important topics within biomedical text relies on highly tuned vocabularies (thesauri) which contain all of the known terms for the same real world “thing”. These vocabularies may just be flat lists (e.g. a list of all known drugs) or they may be organised into a hierarchy, often as an Ontology.

An Ontology structures topics in scientifically-related groups, things like ‘all anti-inflammatories’ or ‘all DNA replication proteins’.  The availability of high quality vocabularies and ontologies is a critical foundation to any text analysis methodology.  

SciBite SBIO ontology

A small excerpt from the SciBite Biopharma ontology

We use a variety of public and in-house ontologies/vocabularies as reference tools for the TERMite engine. Each vocabulary is enhanced by a combination of our in-house and experienced manual curation team and our proprietary ontology enrichment software.  

Our VOCabs cover many more topics in far greater depth that any publicly available ontologies such as MeSH, Uniprot and MeDDRA.  Put simply, if you’re not using SciBite VOCabs in your text analytics, you’re not going to capture the information your users need.