This article explores the practical application of ontologies in AI, particularly for Large Language Models (LLMs). It argues that LLMs often lack a true understanding of concepts like 'customer,' relying instead on statistical patterns. The author proposes a minimal practical ontology sample using MCP and Claude, which defines object types, link types, and action types. This approach aims to pass business definitions, not just data, to AI, enabling it to understand constraints like 'only unshipped orders can be canceled,' thereby protecting the business definition itself rather than just the data or application. AI
IMPACT This approach could enable AI systems to better understand and enforce business rules, moving beyond simple data processing to a more definition-aware operational capacity.
RANK_REASON The article discusses a practical application of ontologies in AI and proposes a sample implementation, which falls under research and development in AI concepts.
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