# Knowledge Graphs Published articles for Knowledge Graphs. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why AI coding agents need context graphs DevFeed: [Why AI coding agents need context graphs]() Original publisher: [Read original article]() Author: Talia Kohan Published: 2026-08-25T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [AI-assisted coding](), [Graphs](), [coding](), [API](), [internal developer platform](), [context window](), [long-context]() Tags: [ai-coding-agents](), [api-governance](), [apis](), [context-window](), [developer](), [developer-platform](), [engineering](), [general](), [graphs](), [knowledge-graphs]() ### AI overview The article argues that AI coding agents struggle in real codebases primarily because they lack access to a knowledge graph connecting code, APIs, services, ownership, dependencies, policies, and related organizational context. It describes service catalogs, API registries, ownership maps, dependency graphs, and internal developer platforms as different forms of the same underlying structure. It also argues that simply increasing the context window does not solve retrieval and context-quality problems. ### Source excerpt AI coding agents don't fail from small context windows. They fail without a knowledge graph of your code, APIs, and vendors. The post Why AI coding agents need context graphs appeared first on Postman Blog. ## GraphRAG: How AI Answers Questions Hidden Across Many Documents DevFeed: [GraphRAG: How AI Answers Questions Hidden Across Many Documents]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-19T15:31:18Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Graphs](), [Retrieval-Augmented Generation](), [Artificial Intelligence]() Tags: [graphs](), [knowledge-graphs](), [rag](), [retrieval-augmented-generation](), [search]() ### AI overview This tutorial explains how GraphRAG addresses questions whose answers emerge from patterns across an entire document collection, contrasting it with standard RAG, which is designed to retrieve information from specific documents. ### Source excerpt GraphRAG was designed to handle the second kind of questions, and we are going to learn more about it in this article. ## Data Engineering Weekly #282 DevFeed: [Data Engineering Weekly #282]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-10T01:21:26Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [data-platforms](), [data observability](), [Graphs](), [semantic-layer](), [Large Language Model](), [Apache Iceberg](), [gRPC](), [Concurrency](), [Caching](), [Optimization](), [Streaming]() Tags: [caching](), [chaos](), [concurrency](), [data](), [data-engineering](), [data-platforms](), [data-quality](), [knowledge-graphs](), [llms](), [newsletter](), [observability](), [pipelines](), [semantic-layer](), [streaming]() ### AI overview Data Engineering Weekly #282 is a newsletter covering data platform fundamentals, semantic layers, ontology-backed knowledge graphs, converged databases, AI modernization, and Netflix's real-time distributed graph query architecture. It highlights composable architectures, data quality and observability, evolving schemas supported by LLM-assisted extraction, Iceberg full-text search, and optimization techniques including concurrency control, streaming filters, and caching. ### Source excerpt The Weekly Data Engineering Newsletter ## Knowledge Management Trends in 2026: What's Actually Changing DevFeed: [Knowledge Management Trends in 2026: What's Actually Changing]() Original publisher: [Read original article]() Author: Janeera Published: 2026-07-28T15:44:41Z Content type: article Language: en Sources: [Knowledge Management Tips, Best Practices and More]() Topics: [knowledge-management](), [Artificial Intelligence](), [Generative AI](), [Graphs]() Tags: [2026](), [agentic-ai](), [ai](), [consistency](), [data](), [documentation](), [generative-ai](), [governance](), [graphs](), [knowledge-graphs](), [knowledge-management](), [knowledge-management-software](), [knowledge-management-system](), [maintenance](), [metrics]() ### AI overview This article examines 2026 knowledge management trends, arguing that structured, governed knowledge is becoming foundational infrastructure for AI systems. It discusses semantic layers, knowledge graphs, agentic AI, conversational discovery, governance, and self-healing maintenance, while noting a shift toward metrics such as answer quality, search success, freshness, and consistency. ### Source excerpt According to Mordor Intelligence, the knowledge management software market is on track to ... The post Knowledge Management Trends in 2026: What's Actually Changing appeared first on Document360. ## How to Build Unified Agent Memory from Scratch DevFeed: [How to Build Unified Agent Memory from Scratch]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-07-14T05:01:54Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [AI Engineering](), [Claude Code](), [Graphs](), [LangChain](), [Large Language Model](), [Database](), [Model Context Protocol]() Tags: [agent](), [ai-engineering](), [claude-code](), [graph](), [graph-database](), [knowledge-graphs](), [langchain](), [llm](), [mcp]() ### AI overview A tutorial on building a unified agent memory layer from scratch using knowledge graphs, including ingestion, querying, and serving. It discusses trade-offs among vector databases, graph databases, temporality, versioning, MCP servers, CLIs, and skills, and explains why understanding the underlying memory layer matters. ### Source excerpt Ingest, query, and serve a unified memory from a single database. ## How to Start a Knowledge-Graph Memory Layer with an Extendable Ontology DevFeed: [How to Start a Knowledge-Graph Memory Layer with an Extendable Ontology]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-05-26T05:00:51Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [Graphs](), [Retrieval Augmented Generation (RAG)](), [data]() Tags: [ai-engineering](), [article](), [how-to](), [knowledge-graph](), [knowledge-graphs](), [rag](), [schema-design]() ### AI overview This tutorial explains how to begin building a knowledge-graph memory layer without designing a complete ontology upfront. It recommends a small, generic, extendable POLE+O model with Preferences and Facts, then extending it as domain-specific data reveals gaps. ### Source excerpt Start with a fixed, generic base and extend only when your data demands it. ## Building Agentic GraphRAG Systems DevFeed: [Building Agentic GraphRAG Systems]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-05-05T05:01:08Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [Retrieval Augmented Generation (RAG)](), [data-modeling](), [MCP Server](), [AI Agent](), [context window]() Tags: [ai-agent](), [context-window](), [data-modeling](), [knowledge-graphs](), [langchain](), [latency](), [mcp-server](), [mongodb](), [rag]() ### AI overview The article explains agentic GraphRAG as a data-modeling problem involving knowledge graphs, ontologies, append-only data models, extraction modes, and hybrid retrieval. It describes exposing the resulting unified memory layer through an MCP server for AI agents. ### Source excerpt From knowledge graphs and ontologies to a unified memory as an MCP server for your AI agent. ## How Amazon Uses LLMs and COSMO to Improve Product Recommendations DevFeed: [How Amazon Uses LLMs and COSMO to Improve Product Recommendations]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-05-04T15:30:20Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [amazon](), [recommendation systems](), [Large Language Model](), [data]() Tags: [amazon](), [data](), [knowledge-graphs](), [llm](), [products](), [recommendation-systems]() ### AI overview The article explains how Amazon built COSMO, a commonsense knowledge graph for product recommendations. It describes using an LLM to generate candidate explanations from shopping behavior, filtering and evaluating those candidates, deriving a structured ontology, and distilling the result into COSMO-LM. ### Source excerpt PLUS: How Shazam Works 🎼 , Agent Harness 🧇, Vercel Breach 🔥 ## Why the Author Argues Knowledge Graphs Improve RAG Over Vector Stores DevFeed: [Why the Author Argues Knowledge Graphs Improve RAG Over Vector Stores]() Original publisher: [Read original article]() Published: 2024-08-12T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [Retrieval Augmented Generation (RAG)](), [graph-database](), [Embeddings]() Tags: [embeddings](), [knowledge-graphs](), [openai](), [rag](), [redis]() ### AI overview The author argues that vector-based retrieval-augmented generation has limited utility because embeddings represent text as single points and chunking can lose context or mix unrelated ideas. The post proposes using knowledge graphs to represent ideas and relationships more structurally for RAG. ### Source excerpt Vector stores are used to implement the RAG pattern, but they're of limited utility. In this post I explain why I think vector-based RAG is limited and why using knowledge graphs to implement RAG is better. ## Mitigating Social Bias in Knowledge Graphs DevFeed: [Mitigating Social Bias in Knowledge Graphs]() Original publisher: [Read original article]() Author: Adam Published: 2020-12-04T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Graphs](), [Embeddings](), [amazon]() Tags: [academic](), [amazon](), [bias](), [embeddings](), [graph](), [graphs](), [knowledge-graph](), [knowledge-graphs](), [paper]() ### AI overview The article discusses an Amazon team's paper on mitigating social bias in knowledge graph embeddings. It views the approaches as a useful starting point while noting that they are not panaceas. ### Source excerpt Something to consider