Why do we need Ontology in an AI or Data Science Framework?

Why do we need Ontology in an AI or Data Science Framework?

Why do we need Ontology in an AI or Data Science Framework? While the adoption of data science in artificial intelligence and machine learning is increasing, leveraging the ontology model can help ease processes.

Understanding Ontologies in Data Science: What? Why?

The rapid advancement of Artificial intelligence and its branches like machine learning, deep learning, which function on extracting relevant information and generating insights from data to find sustainable and decisive solutions, is nothing new. But to run these algorithms, organizations need data and code. To translate this necessity into something meaningful, we need data science. While this discipline proliferates into an exciting and diverse technology that incorporates a mixture of deep specialization and broad applications, we also realize the value it brings to the table. Further, data science helps organizations communicate with stakeholders, customers, track and analyze trends, and determine if the collected data is actually of any help or simply a waste of a database farm. So, having an ontology consisting of the relevant terms and connections from a specific domain, the process of identifying core concepts, improving classification results, and unifying data to collate critical information becomes streamlined.

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