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Google doesn’t rank pages anymore. It ranks understanding. Behind every search result is the knowledge graph, a system that maps entities, relationships, and meaning across the web...
Over 80% of enterprise data lives in unstructured form — PDFs, emails, reports, regulatory filings. Most of the time, such sources contain critical business information, yet they r...
Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, em...
Ask your vector RAG pipeline "what are the main themes in this corpus?" and watch it return three random chunks that share a keyword. Flat vector retrieval is built for "find me th...
In this tutorial, we will generate knowledge graphs from plain text, conversations, and multiple source documents using kg-gen. We start by setting up the required dependencies and...
Юридический домен требует понимания многочисленных связей между сущностями, рассеянными по множеству документов. Поэтому кажется, что область знаний, организованная таким образом,...
A scalable semantic localization layer for entity and relationship reconciliation The post Proxy-Pointer RAG: Solving Entity and Relationship Sprawl in Large Knowledge Graphs appe...
Traditional data catalogs were built as manual inventories for technical users, focusing on table structures rather than the deep context that AI agents need. When agents lack busi...
Today, we're thrilled to announce that BigQuery Graph is now available in preview. With BigQuery Graph, we’ve built an easy-to-use, highly scalable graph analytics solution for dat...
We’ve Been Building The Foundation For 40 Years A year ago, I wrote about the graphic future of IT management — IT management platforms converging on graph architectures to create...
GraphTalker advances Neuro-Symbolic AI with an agentic natural-language interface
Most organizations believe they have an AI problem when the real issue is their knowledge architecture. Microsoft Copilot deployments are exposing a deeper enterprise challenge: or...
Traditional RAG evaluation relies on human-annotated "standard answers," but in the GraphRAG era, this approach is losing its relevance. What Is a Gold Answer? A Gold...
Key Takeaways An LLM wiki is a structured, AI-maintained knowledge base that grows smarter every time you add a source — unlike RAG, which rediscovers knowledge from scratch on eve...
SciGraph показывает, почему GraphRAG для научных статей — это не только про графы и LLM, но и про честные метрики. В статье — разбор системы, которая связывает PDF, авторов, методы...
LLM-поиск товаров: R&D применения технологий RAG и Knowledge Graph Search для продвинутого поиска товаров по сложным текстовым запросам. Как LLM и Knowledge Graph ищут товары
Building a knowledge base for AI models isn’t a one-time task but an iterative process of refinement. The post How to Build an Efficient Knowledge Base for AI Models appeare...
Keyword search struggles with natural language and exploratory questions. Daniel walked the DrupalSouth 2026 audience through how OpenSearch and Skpr enable semantic search that un...
AI search does not start with an algorithm. It starts with structure. Before BFS, DFS, A*, or heuristics can work, the problem must first become something searchable. That is wh...
A clear mental model and a practical foundation you can build on The post Grounding Your LLM: A Practical Guide to RAG for Enterprise Knowledge Bases appeared first on Towards Data...
Jaya Gupta and Ashu Garg of Foundation Capital on December 23rd, 2025, introduced the term "con...
Beyond the Chatbot: Why Financial Services Firms Need Causal Knowledge Graphs — Not Just Language
Most enterprise systems are very good at answering one question: “What happened?” They are surprisingly bad at answering a more important one: “Why did it happen?”
AI vibe coders have yet another reason to thank Andrej Karpathy, the coiner of the term. The former Director of AI at Tesla and co-founder of OpenAI, now running his own independen...
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