The difference between taxonomies and ontologies is a topic that can confuse even the most seasoned data professionals. But it is important to remember that taxonomies and ontologies form the underpinnings of how machines learn and understand. DATAVERSITY brought this news to us in their article, “Taxonomy vs Ontology: Machine Learning Breakthroughs.”
Cognitive computing technologies are responsible for many changes throughout the data industry and in turn throughout other industries who utilize and put in place the new technology.
But let’s back up. Taxonomies provide machines with ordered representations. A taxonomy represents the formal structure of classes or types of objects within a domain. Ontology is a subset of taxonomy, but with more information about the behavior of the entities and the relationships between them. Clear as mud?
If machines learn efficiently using taxonomies and ontologies, we can then apply these tools to a system’s architecture. Taxonomies can be stored using a variety of different data structures. By adding ontologies to a computer’s representations, machines can process the content of information instead of just presenting the information to humans.
Professionals should look for an experienced builder of solid standards-based taxonomies to associate content for appropriate machine-assisted indexing. Access Innovations can provide solutions that are ANSI-compliant.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.