The article explains how ontologies serve as a formal conceptual register for AI, covering description logic, the TBox/ABox distinction, ontology alignment, real‑world applications like Google Knowledge Graph and Siri, and practical advice for newcomers.
The article analyzes why OWL, designed for logical completeness and reasoning in the Semantic Web, becomes a performance and complexity burden in industrial knowledge‑graph projects, detailing which features are academically valuable and which turn into engineering traps, and offering practical usage guidelines.
The article argues that while LLM‑based techniques like RAG, agents, and function calling excel at retrieval and task orchestration, only ontology provides precise semantic logic, sound inference, consistency checking, and auditable proof trees essential for high‑risk domains.
This article surveys knowledge‑graph reasoning, explaining deductive and inductive reasoning fundamentals, description‑logic and logic‑programming approaches, and modern embedding techniques such as TransE, TransH, TransR and TransD, while highlighting their theoretical bases, practical implementations and recent research progress.
