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Think of an ontology as a cheat sheet for quickly identifying key pieces of information and their relationships within a specific field. Ontologies are a kind of data model that describes concepts and knowledge concisely. Ontologies can show interconnections and interoperabilities between pieces of data, and because of that, they are useful for understanding data relationships and how data can be accessed, queried, and navigated.
Jane Lomax, Head of Ontologies at SciBite, said that “implementing any ontology is a specialized task. Successfully doing so requires data collated from many sources to be consistently formatted, structured, and harmonized. In any data-heavy field, this is a challenge.”
Ontologies are useful in data management for enabling capabilities like Master Data Management and automatic classification. Ontologies are equally important in Artificial Intelligence and Machine Learning in specifying data relationships and for cleaning AI training data.
Lomax commented that “crucially, ontologies ensure data are machine-readable, harmonizing them for analysis with AI and machine learning. With data structured in an ontology, companies can be sure their algorithms are learning from the full picture of information, reducing the risk of error, and improving the accuracy of results.”