Eighty-five percent of Artificial Intelligence (AI) projects fail to meet business objectives because of challenges with the information architecture. Ontologies can help overcome these challenges as they can work with unstructured, semi-structured, or structured data formats and connect and qualify data without any need for standardization. Moreover, ontologies streamline the identification of core concepts, improve classification results to collate critical information and make data easy to find and analyze quickly.
In practical business terms, ontology is the architecture that connects numerous sources of information. It builds relationships, qualifies, and interconnects data from multiple domains by automatically tagging and categorizing it. Ontologies are a means of communicating and resolving semantic and organizational differences between databases to enhance their integration.
However, AI specialists and data analysts often view ontologies as a tool to structure data, like a taxonomy. This perspective is a mistake as ontologies are capable of doing more than just structuring data. Ontologies can mimic logical reasoning, and thanks to their ability to automatically tag, categorize and link the information, they streamline the AI training process. Furthermore, ontologies enrich datasets and provide faster analysis.
Therefore, if AI is the vehicle that relies on algorithms to power its engines and propels itself forward, ontologies are the fuel. Hence, ontologies can be used to maximize the value of data without getting overwhelmed by the rising tide of data. Therefore, some forward-looking enterprises use ontologies to stay on top of the ballooning datasets and get more from their investments.
Click here to read the original article published by the E&T magazine.
Please give your feedback on this article or share a similar story for publishing by clicking here.
