There are many ways to organize data, including vocabularies, taxonomies, thesauri, topic maps, logical models, and relational databases. What makes using ontologies to organize data special is the flexibility they afford. For example, with an ontology, changing a property is as easy as changing the semantic concept that underpins that property. The key here is the original data set or the links or indices that deal with it are retained.
Ontologies are extremely useful for machine learning. Data can be fed into the machine-learning model directly if you are using an ontology. This enables the models to focus on delivering the best possible outcomes. Furthermore, ontologies are one of the building blocks of the semantic web. Together, they ensure interoperability, cross-database search, and seamless knowledge management.
The scope of ontology is not limited to web searches. It is much broader than that. In the pharmaceutical industry, ontology is being used to test early hypotheses. In another use case, financial data was used to uncover financial crime. What all these applications have in common is that they are user-centric and based on real-world problems.
Ontology is sometimes touted as the next big thing in data science. In reality, it is an old discipline. What is new and disruptive is the idea of using it for data. Companies should examine ways and means of implementing ontologies and revolutionizing technology.
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