Deploying AI: Giving Neural Networks Long-Term Memory with Ontologies
Ontological memory is a bet on the next stage of AI systems after RAG. Instead of storing long logs, the article proposes a world model built from entities, relationships and states. This is especially useful for tutoring bots, corporate assistants, robotics and local LLMs that need stable context without constant recaps. Such a layer also makes answers easier to understand: you can check the system by its nodes and links, not just by polished text.
AI-processed from Habr AI; edited by Hamidun News
Ontologies are increasingly discussed not as a format for a knowledge base, but as a memory layer for neural networks and AI systems, especially when deploying corporate assistants.
Why does a neural network forget information in long dialogues?
Large language models put everything into a temporary context window. When an interaction stretches over weeks or months, the model loses details, asks questions that were already settled, and takes a user's personal traits into account less well.
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