This paper identifies and formalizes the "semantic training gap" in LLM-based AI agents deployed in manufacturing, where statistical fluency lacks grounded operational understanding. This gap causes incorrect outputs and compounding failures like semantic drift, which the proposed ontology-grounded architectures aim to address.
This article details the evolution of 2asy.ai's retrieval system, transitioning from basic vector search to a cross-domain ontology Graph RAG. This rebuild aims to overcome the limitations of similarity-based retrieval and better understand complex causal relationships within tariff news.
This practical guide explores ontology in computer science and AI, describing it as an essential framework for organizing knowledge. It enables machines to interpret relationships and make more accurate decisions, being crucial for semantic systems and next-generation AI applications. Leaders like Salesforce emphasize its importance for personalization and decision intelligence.
This content introduces the "Algebra of Contexts," which organizes meaning into nested "spaces" based on set laws. It provides a unified ontology for platforms like .me, cleaker, and monad.ai, enabling them to manage and persist semantic regions.
O título sugere 'OntoKG', um método para construção de grafos de conhecimento orientado a ontologias com roteamento intrínseco-relacional. Contudo, a ausência de conteúdo impede qualquer análise aprofundada do material.