Every field creates ontologies to limit complexity and organize information into data and knowledge. As new ontologies are made, their use tends to significantly improve problem solving within that domain. For example, translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their respective languages.
In recent years, the development of ontologies has found a very broad range of applications, reaching much farther scopes than its origins in the realm of Artificial-Intelligence laboratories. Ontologies have in particular become ubiquitous across the World-Wide Web. The ontologies on the Web can vary for instance from large taxonomies categorizing the results delivered by search engine websites (such as Google), to the classification of products for sale together with their features and qualities (such as on Amazon.com).
In summary, an ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain, and the relations among them. Why would someone want to develop an ontology? Some of the most common reasons are:
– To share common understanding of the structure of information among people or software agents
– To enable reuse of domain knowledge
– To make domain assumptions explicit
– To separate domain knowledge from the operational knowledge
– To analyze domain knowledge
An example representation of a simple ontology, describing an instance of people-workplace relation, is shown in the image below, for illustrative purposes: