What is bio-ontology?
Bio-ontology is standards:
Modern research generates data at unprecedented rates. Where such data are described and labelled using unconstrained text, different terminology is often used for similar or identical things. Such terminological variance is normal and reflects natural language; usually humans have no difficulty resolving ambiguous usages of terminology and discrepant labels. However, due to the sheer volumes of research data being generated, it is necessary to develop computational methods of aggregating and aligning like with like. One approach to addressing this issue is to adopt shared standards for the categorisation of data. Agreement in annotation across different databases increases the value of a standardised terminology, allowing for easier cross-domain integration and querying.
Bio-ontology is knowledge representation:
Modern biomedical ontologies harness the formal semantics underlying the Web Ontology Language (OWL), which allows complex logical expressions to be built that define knowledge about the domain, in such a fashion that computers can perform automatic reasoning for tasks such as hierarchy management and consistency checking / error detection. OWL is based on Description Logics, a family of decidable logical languages optimised for the expression of large-scale terminological knowledge such as is found within large biomedical vocabularies.
Bio-ontology is interdisciplinarity:
Increasingly, research in the life sciences needs to integrate knowledge and results from multiple disparate fields and methodological approaches in order to gain insight into underlying biological mechanisms. This is the case, for example, when studying the genetic and epigenetic factors in understanding behavioural phenotypes, or in the development of predictive models to enable personalised and translational medicine. Research results from diverse disciplines such as genetics, molecular biology, physiology, chemistry, psychology and medicine have to be integrated in order to build a coherent picture of what is known in order to address key research gaps.