Artificial Intelligence (AI) aims to understand and learn the hidden relations in the data in a way that allows it to extract, reproduce and predict. The common practice is to train models on domain-limited data. While the trained model could yield excellent results in that particular domain, it is often found that the model performs poorly when tested on data from a different domain. One of AI's main challenges is the ability to generalize. Ontologies can help solve this problem.
By feeding entire representations of a domain to a model, ontologies help models better understand relations in the data and, as a result, better generalize to different datasets with similar relations. Combining domain-specific ontology with AI can uncover new patterns and produce better results.
Ontologies can help broaden the model's scope by significantly improving the data quality. By doing so, with less data, the performance of a model could exceed the performance of models relying on a more traditional data representation.
Furthermore, ontologies can help enhance data quality for AI. They can support unstructured, semi-structured, or structured data. Adopting an ontological representation of data also means having access to reusable, repeatable, and adaptive structures and a rapid scaling capability.
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