Had to reshare this simple and well articulated defense of ontologies.
⚡️ Building bridges @naas.ai Universal Data & AI Platform | Research Associate in Applied Ontology | Senior Advisor Data & AI Services
Why are ontologies important in data management and AI? 6 key reasons. I think ontologies are very important for the future of data management and AI, but why? 6 things on my mind, feel free to add what's missing: 1. They provide a formal model of concepts and relationships that enable shared understanding. By defining classes, properties, and restrictions, ontologies create a common vocabulary and semantics around a domain. This facilitates interoperability and integration across systems and organizations. 2. They enable automated reasoning and inference. The formal logic-based representations of ontologies allow logical inferences to be made, deriving new knowledge from asserted facts. This kind of automated reasoning allows systems to check consistency, analyze the implications of data, and make recommendations. 3. They structure and organize knowledge for reuse. Ontologies provide an abstract framework for categorizing and relating entities to support explainability and reuse across applications. This semantic structure enables knowledge to be modularized instead of rebuilt from scratch for every use case. 4. They support machine learning transparency and accuracy. Providing context around training data characteristics, relationships, constraints etc. ontologies can improve ML model transparency, fairness, and accuracy. They also support the validation and monitoring of model performance over time. 5. They help ground AI systems and balance the potential for hallucination. Large language models "hallucinate" false information if not properly grounded. Ontologies provide a formal factual framework to map each of our realities and ensure language models align to truth and facts. 6. And last but not least, they help align data engineers and data scientists. Data engineers focus on building data pipelines while data scientists focus more on unlocking potential...this can leave gaps in formal data modeling. Ontologies can provide a unified semantic model spanning the full data lifecycle from integration to analytics and machine learning. This bridges the gap by enabling data engineers to incorporate more meaning and structure upfront, while still supporting flexibility for data scientists downstream. In short, ontologies move from "half messy half organized" data to formally defining a shared map of an organization's reality around data. This additional meaning, which can sit on top of the current data warehouse, data lakes, or traditional database, enables more intelligent systems and more meaningful data integration across platforms and organizations. We need ontologies to create more lean, efficient, and resilient systems. It's not like you are going to tidy up your room with some magic overnight, so let's get to work!