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May 19, 2026 · Artificial Intelligence

From RAG to GraphRAG: How Huolala Raised Metadata Retrieval Accuracy from 56% to 78%

The article details Huolala's transition from a basic Retrieval‑Augmented Generation (RAG) system to a GraphRAG architecture, explaining the challenges of traditional RAG, the design of offline and online stages, multi‑index hybrid search, concrete performance metrics (accuracy up to 78%, knowledge recall 91%, Top‑K 90%, MRR 0.73), and future plans such as stronger hybrid retrieval, reranking, and Agentic RAG.

AIGraphRAGHybrid Search
0 likes · 15 min read
From RAG to GraphRAG: How Huolala Raised Metadata Retrieval Accuracy from 56% to 78%
DataFunTalk
DataFunTalk
May 19, 2026 · Artificial Intelligence

How Knora’s Ontology‑Enhanced AI Tackles Hallucinations and Execution Gaps in Enterprise Deployments

The article explains how Knora 4.0 combines enterprise‑level ontologies with large‑model capabilities to overcome six common AI challenges—hallucination, instability, weak planning, poor responsiveness, data integration, and long cold‑start cycles—enabling autonomous, auditable execution illustrated by a LED production‑line case that achieved a 70‑fold efficiency boost.

AI ArchitectureAutonomous AgentsEnterprise AI
0 likes · 16 min read
How Knora’s Ontology‑Enhanced AI Tackles Hallucinations and Execution Gaps in Enterprise Deployments
DataFunTalk
DataFunTalk
May 16, 2026 · Artificial Intelligence

How Knora Combines Ontology and Large Models to Overcome AI Hallucinations and Execution Gaps in Enterprises

The article explains how YueDian Technology's Knora 4.0 platform fuses domain ontologies with large‑model AI to create a unified, trustworthy, and autonomous enterprise AI system that addresses hallucination, data integration, and execution challenges across complex business scenarios.

AI PlatformAutonomous AgentsEnterprise AI
0 likes · 14 min read
How Knora Combines Ontology and Large Models to Overcome AI Hallucinations and Execution Gaps in Enterprises
DataFunTalk
DataFunTalk
May 15, 2026 · Artificial Intelligence

Exploring Multimodal GraphRAG: Combining Document Intelligence, Knowledge Graphs, and Large Models

This article provides a comprehensive technical overview of multimodal GraphRAG, detailing document‑intelligence parsing pipelines, layout analysis, OCR‑pipeline vs OCR‑free approaches, knowledge‑graph integration for chunk relationships, multimodal indexing, retrieval‑generation workflows, and a comparative analysis of RAG, GraphRAG, and KG‑QA solutions.

Document IntelligenceGraphRAGKnowledge Graph
0 likes · 23 min read
Exploring Multimodal GraphRAG: Combining Document Intelligence, Knowledge Graphs, and Large Models
James' Growth Diary
James' Growth Diary
May 12, 2026 · Artificial Intelligence

GraphRAG Deep Dive: Boost Multi‑Hop Reasoning Accuracy from 50% to 85% with Knowledge Graphs

This article explains why traditional vector RAG loses relational information, how GraphRAG reconstructs entity‑relationship triples into a knowledge graph, and provides step‑by‑step code, performance benchmarks, retrieval modes, and practical tips that raise multi‑hop reasoning accuracy from around 50% to 85%.

GraphRAGKnowledge GraphLangChain
0 likes · 14 min read
GraphRAG Deep Dive: Boost Multi‑Hop Reasoning Accuracy from 50% to 85% with Knowledge Graphs
DataFunSummit
DataFunSummit
May 12, 2026 · Artificial Intelligence

15 Critical Questions on Why Enterprise AI Agents Need Business Ontology

The article analyzes why large language models and RAG alone cannot meet enterprise AI needs, argues that a business ontology provides essential semantic grounding for agents, outlines ontology construction methods, demonstrates hybrid search improvements, and shares real‑world case studies showing dramatic efficiency gains.

AI agentsEnterprise AIHybrid Search
0 likes · 16 min read
15 Critical Questions on Why Enterprise AI Agents Need Business Ontology
DeepHub IMBA
DeepHub IMBA
May 11, 2026 · Artificial Intelligence

2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless

This article compares traditional Vector RAG, GraphRAG, and the newer Vectorless RAG, explains why Vector RAG fails on relational and structured queries, presents benchmark results, outlines each architecture's strengths and costs, and offers a decision framework and Adaptive RAG routing strategy for production systems.

Adaptive RetrievalGraphRAGKnowledge Graph
0 likes · 13 min read
2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless
DataFunSummit
DataFunSummit
May 11, 2026 · Artificial Intelligence

How Lance Powers Enterprise Multimodal AI Data Lakes

The article analyzes why 74% of AI projects fail due to feedback gaps and data silos, explains how the open‑source Lance format addresses these issues with unified multimodal storage, outlines a layered Lance‑on‑Ray architecture, and details three real‑world practices—implicit feedback loops, GPU‑accelerated self‑evolution, and semantic knowledge‑graph evolution—to boost R&D efficiency.

CAGRADaftData Lake
0 likes · 13 min read
How Lance Powers Enterprise Multimodal AI Data Lakes
DataFunTalk
DataFunTalk
May 10, 2026 · Artificial Intelligence

Exploring Multimodal GraphRAG: Combining Document Intelligence, Knowledge Graphs, and Large Models

This article presents a detailed technical walkthrough of multimodal GraphRAG, covering document‑intelligence parsing pipelines, multimodal graph index construction, knowledge‑graph‑driven chunk linking, recent research progress, performance trade‑offs, and practical recommendations for deploying RAG solutions.

Document IntelligenceGraphRAGKnowledge Graph
0 likes · 23 min read
Exploring Multimodal GraphRAG: Combining Document Intelligence, Knowledge Graphs, and Large Models
DataFunSummit
DataFunSummit
May 9, 2026 · Artificial Intelligence

DeepEye: Building an Autonomous, Human‑Steerable Data Agent System

The article presents DeepEye, an open‑source autonomous data‑agent platform that combines LLM reasoning, workflow orchestration, and human‑in‑the‑loop control to enable end‑to‑end analysis of heterogeneous data, and introduces a six‑level capability taxonomy to guide its evolution from manual to fully autonomous operation.

Autonomous AIData AgentDeepEye
0 likes · 18 min read
DeepEye: Building an Autonomous, Human‑Steerable Data Agent System
AI Architecture Path
AI Architecture Path
May 9, 2026 · Artificial Intelligence

Struggling with an Unknown Codebase? Claude Code Plugin Maps All Logic in One Graph

Understand‑Anything is a Claude Code plugin that uses a multi‑agent pipeline to turn large, unfamiliar codebases into searchable, interactive knowledge graphs, supporting nine AI coding tools, offering visual dashboards, natural‑language Q&A, incremental diff, and detailed onboarding while noting token costs and large‑graph performance limits.

AI toolClaude CodeKnowledge Graph
0 likes · 11 min read
Struggling with an Unknown Codebase? Claude Code Plugin Maps All Logic in One Graph
Old Zhang's AI Learning
Old Zhang's AI Learning
May 6, 2026 · Artificial Intelligence

Solving RAG’s Biggest Pain Point: Introducing the Open‑Source CocoIndex

RAG and agent contexts suffer from stale data, not chunking or reranking, and CocoIndex—a Rust‑based incremental engine with a declarative Python API—offers fresh, delta‑processed context, automatic schema evolution, and production‑grade features, demonstrated through PDF‑to‑Markdown pipelines and a podcast knowledge‑graph case study.

Knowledge GraphPythonRAG
0 likes · 13 min read
Solving RAG’s Biggest Pain Point: Introducing the Open‑Source CocoIndex
DataFunTalk
DataFunTalk
May 6, 2026 · Artificial Intelligence

Why Palantir’s Ontology, Not Just Large Models, Drives Its Valuation Surge

In a 90‑minute round‑table, experts from banking risk control and cloud observability explain how Palantir’s ontology—viewed as the skeleton and memory that structures massive, heterogeneous data—bridges three data gaps, enables large‑model reasoning, and offers concrete steps for building practical knowledge graphs in enterprises.

Digital TwinEnterprise AIKnowledge Graph
0 likes · 16 min read
Why Palantir’s Ontology, Not Just Large Models, Drives Its Valuation Surge
DataFunTalk
DataFunTalk
May 5, 2026 · Artificial Intelligence

How Knora’s Ontology‑Enhanced AI Tackles Hallucinations and Execution Gaps in Enterprise Deployments

The article analyzes Knora 4.0, an ontology‑enhanced AI platform that combines large‑model capabilities with a structured knowledge graph to overcome hallucinations and execution gaps in enterprise deployments, detailing its architecture, autonomous agent Knora Claw, real‑world case studies, and a three‑year roadmap.

AI ArchitectureAutonomous AgentsBusiness Automation
0 likes · 18 min read
How Knora’s Ontology‑Enhanced AI Tackles Hallucinations and Execution Gaps in Enterprise Deployments
DataFunTalk
DataFunTalk
May 4, 2026 · Artificial Intelligence

Building a Semantic Foundation for Harness Engineering: Ontology‑Driven Controllable Agents

The article analyzes why current AI agents lack reliable control, defines a multi‑dimensional safety framework, and proposes an ontology‑driven architecture—implemented in the Knora platform—that embeds business rules directly into agents, enabling deterministic validation, auditability, and large‑scale efficiency gains.

AIAgentBusiness Control
0 likes · 17 min read
Building a Semantic Foundation for Harness Engineering: Ontology‑Driven Controllable Agents
Geek Labs
Geek Labs
May 4, 2026 · Artificial Intelligence

Turning Any Code Repository into an Interactive Knowledge Graph with GitNexus

GitNexus is an open‑source tool that indexes any code repository into a searchable knowledge graph, enabling AI agents to understand code structure through a CLI‑MCP mode or a web UI, and it differentiates itself from DeepWiki by focusing on deep structural analysis and tool‑use hooks.

AI agentsGitNexusKnowledge Graph
0 likes · 5 min read
Turning Any Code Repository into an Interactive Knowledge Graph with GitNexus
DataFunTalk
DataFunTalk
May 2, 2026 · Industry Insights

Why Palantir’s Ontology Fuels Its Valuation: The Skeleton and Memory Behind AI

In a 90‑minute round‑table, experts from banking risk control and cloud observability explain how Palantir’s ontology bridges three data gaps, turns raw logs into a graph of entities and relationships, and works with large models as a skeleton and memory to make AI trustworthy and scalable.

AI trustworthinessDigital TwinKnowledge Graph
0 likes · 16 min read
Why Palantir’s Ontology Fuels Its Valuation: The Skeleton and Memory Behind AI
DataFunTalk
DataFunTalk
May 1, 2026 · Artificial Intelligence

Why Ontology Is the Semantic Operating System for Large‑Model AI

The article argues that in the era of powerful large models, enterprises lack a unified, computable, and evolvable semantic layer—ontology—that acts as a semantic operating system, bridging business concepts, data, and AI to enable reliable, actionable intelligence.

Enterprise AIKnowledge GraphOntology
0 likes · 16 min read
Why Ontology Is the Semantic Operating System for Large‑Model AI
DataFunSummit
DataFunSummit
Apr 30, 2026 · Industry Insights

Why Palantir’s Edge Isn’t Unique – Chinese Enterprises Can Replicate Its Methodology

A panel of industry experts dissected Palantir’s rapid growth, revealing that its advantage lies in a systematic ontology‑driven methodology rather than exclusive technology, and argued that Chinese firms can adopt the same approach if they first resolve data governance, semantic consistency, and management challenges.

AI agentsCapability vs CompetencyData Governance
0 likes · 26 min read
Why Palantir’s Edge Isn’t Unique – Chinese Enterprises Can Replicate Its Methodology
DataFunSummit
DataFunSummit
Apr 28, 2026 · Artificial Intelligence

How Knora’s Ontology‑Enhanced Large Model Solves Hallucination and Execution Gaps in Enterprise AI

The article explains how Knora 4.0 combines enterprise ontologies with large‑model AI to create a unified, autonomous execution loop, addressing six common AI‑deployment challenges, detailing the platform’s architecture, autonomous agents, real‑world case studies, roadmap, and expert round‑table insights.

AI ArchitectureAutonomous AgentsEnterprise AI
0 likes · 17 min read
How Knora’s Ontology‑Enhanced Large Model Solves Hallucination and Execution Gaps in Enterprise AI
Alibaba Cloud Observability
Alibaba Cloud Observability
Apr 27, 2026 · Artificial Intelligence

From Observability to Understanding: Building an Agent‑Native Code Knowledge Graph with UModel

The article analyzes current AI code agents such as Claude Code and Cursor, highlights their three major limitations—guessing relationships, staying within the code domain, and lacking a temporal dimension—and proposes UModel’s deterministic AST extraction and cross‑domain linking to create a native code knowledge graph that lets agents move from merely finding code to truly understanding its structure.

AI agentsKnowledge GraphObservability
0 likes · 26 min read
From Observability to Understanding: Building an Agent‑Native Code Knowledge Graph with UModel
DataFunTalk
DataFunTalk
Apr 27, 2026 · Artificial Intelligence

Ontology + Large Model: How Knora Tackles Enterprise AI Hallucination and Execution Gaps

The article analyses how Knora 4.0 combines enterprise ontologies with large‑model AI to eliminate hallucinations, provide stable semantic constraints, and enable end‑to‑end autonomous execution across complex business scenarios, illustrated with LED production‑line use cases and a detailed platform architecture.

AI PlatformAutonomous AgentsEnterprise AI
0 likes · 17 min read
Ontology + Large Model: How Knora Tackles Enterprise AI Hallucination and Execution Gaps
AI Large Model Application Practice
AI Large Model Application Practice
Apr 27, 2026 · Artificial Intelligence

How Graphify Becomes the “Second Brain” for AI Coding in Enterprise Legacy Systems

Graphify transforms scattered code, documentation, and business knowledge into a structured knowledge graph that serves as a “second brain” for AI coding assistants, enabling them to navigate and understand complex enterprise legacy systems, reduce token costs, and improve answer quality, as demonstrated by detailed tests on the BettaFish project.

AI CodingGraphifyKnowledge Graph
0 likes · 16 min read
How Graphify Becomes the “Second Brain” for AI Coding in Enterprise Legacy Systems
DeepHub IMBA
DeepHub IMBA
Apr 26, 2026 · Artificial Intelligence

Graphify: Building Codebase Knowledge Graphs to Replace Vector Retrieval

Graphify is a Python tool that parses codebases into a searchable knowledge graph, eliminating the need for costly vector retrieval by traversing explicit entity‑relationship graphs, achieving up to 71.5× token reduction, supporting AST extraction, optional local audio transcription, and AI‑driven semantic extraction with confidence labeling.

ASTClaude CodeKnowledge Graph
0 likes · 14 min read
Graphify: Building Codebase Knowledge Graphs to Replace Vector Retrieval
DataFunTalk
DataFunTalk
Apr 24, 2026 · Artificial Intelligence

Exploring Multimodal GraphRAG: Document Intelligence, Knowledge Graphs, and Large‑Model Integration

This article presents a detailed technical walkthrough of multimodal GraphRAG, covering document‑intelligence parsing pipelines, layout‑analysis models, knowledge‑graph augmentation, multimodal indexing and retrieval, and a comparative analysis of RAG, GraphRAG, and KG‑QA approaches, with concrete examples, model sizes, benchmark scores, and research citations.

Document IntelligenceGraphRAGKnowledge Graph
0 likes · 25 min read
Exploring Multimodal GraphRAG: Document Intelligence, Knowledge Graphs, and Large‑Model Integration
DataFunTalk
DataFunTalk
Apr 23, 2026 · Artificial Intelligence

Why Palantir’s Valuation Soars: Large Models as the Brain, Ontology as the Skeleton and Memory

In a 90‑minute round‑table hosted by DataFun, experts from banking risk control and cloud observability dissect how Palantir’s ontology—structured as a graph that links entities, metrics and logs—complements large‑model AI, solves data chaos, and becomes the practical backbone for trustworthy enterprise AI.

Enterprise AIKnowledge GraphObservability
0 likes · 16 min read
Why Palantir’s Valuation Soars: Large Models as the Brain, Ontology as the Skeleton and Memory
DataFunSummit
DataFunSummit
Apr 23, 2026 · Artificial Intelligence

Ontology + Large Model: How Knora Solves Hallucination and Execution Gaps in Enterprise AI

The article details how Knora 4.0 integrates ontology with large‑model AI to create a reusable, extensible enterprise AI platform that mitigates hallucination, stabilises output, and enables autonomous end‑to‑end execution, illustrated with LED production line case studies, architectural breakdowns, and a roadmap for future intelligent agents.

Autonomous AgentsEnterprise AIKnowledge Graph
0 likes · 17 min read
Ontology + Large Model: How Knora Solves Hallucination and Execution Gaps in Enterprise AI
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 23, 2026 · Artificial Intelligence

From Data‑Driven Insights to a Decision Center: Ontological Engineering with PolarDB‑PG

The article explains how Ontology—an abstract model of objects, relationships, and actions—can be built on PolarDB‑PG’s intelligent engine to overcome semantic ambiguity and logical hallucination in enterprise LLM agents, describing a three‑layer architecture, OAG retrieval, automatic modeling, fine‑grained permission control, and real‑world supply‑chain use cases.

AI AgentEnterprise AIKnowledge Graph
0 likes · 13 min read
From Data‑Driven Insights to a Decision Center: Ontological Engineering with PolarDB‑PG
AI Architecture Path
AI Architecture Path
Apr 23, 2026 · Artificial Intelligence

MemPalace: Offline, Local‑First AI Memory System Built on a Memory‑Palace Architecture

MemPalace is an open‑source, local‑first AI memory library that stores raw conversation and project content without summarisation, uses a hierarchical "memory palace" structure for fast semantic retrieval, provides plug‑in retrieval back‑ends, knowledge‑graph support, and achieves the highest publicly reported offline benchmark scores.

AI memoryBenchmarkKnowledge Graph
0 likes · 17 min read
MemPalace: Offline, Local‑First AI Memory System Built on a Memory‑Palace Architecture
DeepHub IMBA
DeepHub IMBA
Apr 21, 2026 · Artificial Intelligence

Designing Persistent Memory for Production AI Agents: A Five‑Stage Pipeline and Four Design Patterns

Production AI agents require persistent memory to maintain continuity, learn from interactions, and recover from failures, but naïvely stuffing full conversation history into the LLM context incurs prohibitive latency and cost; this article outlines four memory types, a five‑stage pipeline, four design patterns, and practical metrics for building efficient, auditable memory systems.

AI agentsDesign PatternsKnowledge Graph
0 likes · 27 min read
Designing Persistent Memory for Production AI Agents: A Five‑Stage Pipeline and Four Design Patterns
DataFunTalk
DataFunTalk
Apr 21, 2026 · Artificial Intelligence

Will Multimodal GraphRAG Revolutionize Document Intelligence? A Technical Deep Dive

This article provides a comprehensive technical analysis of multimodal GraphRAG, detailing document intelligent parsing pipelines, multimodal graph construction, retrieval generation, and the role of knowledge graphs in enhancing chunk relationships, while comparing traditional RAG, GraphRAG, and KG‑QA approaches.

AIDocument ParsingKnowledge Graph
0 likes · 26 min read
Will Multimodal GraphRAG Revolutionize Document Intelligence? A Technical Deep Dive
DataFunTalk
DataFunTalk
Apr 20, 2026 · Artificial Intelligence

Why Palantir’s Ontology Is the Secret Behind AI Success in Banking and Cloud Ops

In a 90‑minute round‑table hosted by DataFun, experts from Shanghai Bank, Alibaba Cloud, and academia dissect how ontology bridges data chaos, model opacity, and engineering scale, enabling trustworthy AI for financial risk control and cloud observability while outlining practical steps for building usable knowledge graphs.

AIDigital TwinEnterprise AI
0 likes · 17 min read
Why Palantir’s Ontology Is the Secret Behind AI Success in Banking and Cloud Ops
Big Data and Microservices
Big Data and Microservices
Apr 19, 2026 · Artificial Intelligence

Why Do AI Agents Forget? Understanding Short‑Term and Long‑Term Memory

This article explains how AI agents store information using short‑term (context window) and long‑term (vector database, RAG, knowledge graph) memory, illustrates the concepts with everyday analogies, and shows how proper memory design improves real‑world applications like customer service bots and personal assistants.

AI agentsAI memoryKnowledge Graph
0 likes · 6 min read
Why Do AI Agents Forget? Understanding Short‑Term and Long‑Term Memory
DataFunSummit
DataFunSummit
Apr 18, 2026 · Industry Insights

Why Palantir’s Ontology Beats Traditional Data Models – Insights from Industry Leaders

A closed‑door forum gathered experts from academia and leading Chinese tech firms to dissect Palantir’s ontology‑driven approach, comparing it with conventional data modeling, exploring AI integration, and highlighting the managerial and technical challenges that determine its success in enterprise environments.

Data GovernanceEnterprise AIKnowledge Graph
0 likes · 27 min read
Why Palantir’s Ontology Beats Traditional Data Models – Insights from Industry Leaders
DataFunTalk
DataFunTalk
Apr 18, 2026 · Artificial Intelligence

How Ontology Turns AI Agents into Secure, Controllable Executors

The article examines Harness Engineering's ontology‑driven semantic foundation for AI agents, outlining the challenges of uncontrolled agents, multi‑dimensional safety requirements, architectural constraints, context engineering, feedback loops, and the Knora implementation that bridges technical control to business‑level governance.

AI agentsKnowledge GraphOntology
0 likes · 17 min read
How Ontology Turns AI Agents into Secure, Controllable Executors
Code Mala Tang
Code Mala Tang
Apr 17, 2026 · Industry Insights

Beyond Memory: How Context Substrates Are Redefining AI Agents

A comprehensive analysis of over 900 GitHub repositories reveals two distinct paradigms for agent memory—backend storage and context substrates—highlighting their technical differences, strengths, limitations, and the emerging shift toward context engineering for long‑running AI agents.

AIAgent MemoryKnowledge Graph
0 likes · 15 min read
Beyond Memory: How Context Substrates Are Redefining AI Agents
ArcThink
ArcThink
Apr 17, 2026 · Artificial Intelligence

Why AI Forgetting So Much? HyperMem’s Hypergraph Memory Sets New SOTA

The article analyzes why large language models struggle with long‑term memory, introduces the HyperMem hypergraph‑based memory system that organizes information in three hierarchical layers (topic, episode, fact), and shows it achieves 92.73% accuracy on the LoCoMo benchmark, surpassing GraphRAG, Mem0 and other prior methods.

AI memoryHypergraphKnowledge Graph
0 likes · 20 min read
Why AI Forgetting So Much? HyperMem’s Hypergraph Memory Sets New SOTA
Advanced AI Application Practice
Advanced AI Application Practice
Apr 16, 2026 · Artificial Intelligence

Can AI Deliver Scalable, High‑Quality Test Assets for Enterprises?

The article analyzes enterprise testing challenges and presents the AIO intelligent testing platform, which combines cloud‑native architecture, MLLM‑RAG dual engines, and a knowledge‑graph to automate test case generation, improve coverage, and cut maintenance costs, backed by concrete benchmarks and multi‑modal inputs.

AI testingCloud NativeKnowledge Graph
0 likes · 18 min read
Can AI Deliver Scalable, High‑Quality Test Assets for Enterprises?
SuanNi
SuanNi
Apr 13, 2026 · Artificial Intelligence

How AI Researchers Built a 400% Better Multimodal Memory System with AutoResearchClaw

A fully automated AI research pipeline called AutoResearchClaw enabled a team from top universities to redesign a multimodal memory architecture, OMNIMEM, achieving over 400% performance gains on LoCoMo and Mem‑Gallery benchmarks by iteratively fixing code bugs, restructuring the system, and optimizing retrieval strategies.

AI research automationAutoResearchClawBenchmarking
0 likes · 12 min read
How AI Researchers Built a 400% Better Multimodal Memory System with AutoResearchClaw
dbaplus Community
dbaplus Community
Apr 12, 2026 · Artificial Intelligence

Boost RAG Accuracy to 94%: 11 Proven Strategies and How to Combine Them

After struggling with naive RAG that delivered only 60% accuracy, the author outlines eleven advanced strategies—including context-aware chunking, query expansion, re‑ranking, multi‑query, knowledge graphs, and agent‑based retrieval—that together raise performance to 94%, and provides detailed implementation examples, trade‑offs, and a step‑by‑step deployment roadmap.

AIEmbeddingKnowledge Graph
0 likes · 32 min read
Boost RAG Accuracy to 94%: 11 Proven Strategies and How to Combine Them
James' Growth Diary
James' Growth Diary
Apr 10, 2026 · Artificial Intelligence

Designing Agent Memory Systems: Short‑Term, Long‑Term, and Knowledge Graph Layers

The article breaks down how to build a three‑layer memory architecture for AI agents—short‑term context windows with sliding‑window summarization, long‑term semantic retrieval via vector databases with selective storage and time decay, and a knowledge‑graph layer for relational reasoning—plus implementation tips and common pitfalls.

Agent MemoryKnowledge GraphLangChain
0 likes · 19 min read
Designing Agent Memory Systems: Short‑Term, Long‑Term, and Knowledge Graph Layers
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Apr 7, 2026 · Artificial Intelligence

AutoHypo-Fin: Tsinghua's Web-Mining Method to Auto-Generate and Backtest Market Hypotheses

AutoHypo‑Fin is an end‑to‑end framework that harvests large‑scale web financial data, extracts entities via large language models, builds a temporal knowledge graph, uses retrieval‑augmented generation and statistical backtesting to automatically create, test, and iteratively optimize trading hypotheses, achieving superior risk‑adjusted returns compared with baseline strategies in experiments from 2019‑2024.

AutoHypo-FinKnowledge GraphLLM
0 likes · 11 min read
AutoHypo-Fin: Tsinghua's Web-Mining Method to Auto-Generate and Backtest Market Hypotheses
PaperAgent
PaperAgent
Apr 2, 2026 · Artificial Intelligence

Can an LLM Build a Full‑Stack Knowledge Graph System in Under 3 Hours?

Using the GLM‑5.1 large language model, the author automated the end‑to‑end development of an ontology‑based knowledge‑graph extraction and visualization platform—covering backend, frontend, and graph database—in just 2 hours 47 minutes, consuming 747 k tokens and self‑correcting multiple issues.

AI EngineeringFull-Stack DevelopmentGLM-5.1
0 likes · 12 min read
Can an LLM Build a Full‑Stack Knowledge Graph System in Under 3 Hours?
Data STUDIO
Data STUDIO
Apr 2, 2026 · Artificial Intelligence

Building a Dual‑Stack Memory Agent: Situational + Semantic Memory for Long‑Term AI Understanding

This tutorial walks through designing and implementing a dual‑stack memory architecture for AI agents—combining episodic vector‑based situational memory with graph‑based semantic memory—using LangChain, FAISS, and Neo4j, and demonstrates a complete end‑to‑end workflow with code examples.

Agent MemoryFAISSKnowledge Graph
0 likes · 14 min read
Building a Dual‑Stack Memory Agent: Situational + Semantic Memory for Long‑Term AI Understanding
PaperAgent
PaperAgent
Mar 19, 2026 · Artificial Intelligence

How MDER‑DR Boosts Multi‑Hop KG QA with Entity‑Centric Summaries

The article presents the MDER‑DR two‑stage framework that tackles semantic loss in knowledge‑graph triple indexing by generating context‑aware entity summaries and using an LLM‑driven decompose‑parse retrieval loop, achieving up to 66% performance gains on multi‑hop question answering benchmarks.

Entity SummarizationKG QAKnowledge Graph
0 likes · 5 min read
How MDER‑DR Boosts Multi‑Hop KG QA with Entity‑Centric Summaries
Tech Freedom Circle
Tech Freedom Circle
Mar 19, 2026 · Artificial Intelligence

Failed Alibaba Interview: The 4 RAG Modules and 6 Design Principles You Need

The article dissects a failed Alibaba second‑round interview where the candidate answered only “vector‑search‑enhanced” for a RAG design, and then presents a systematic, four‑module RAG architecture together with six design principles, detailed indexing, query understanding, multi‑path recall, and context generation techniques to help candidates demonstrate comprehensive technical depth.

AI ArchitectureKnowledge GraphMulti‑Path Recall
0 likes · 22 min read
Failed Alibaba Interview: The 4 RAG Modules and 6 Design Principles You Need
Huolala Tech
Huolala Tech
Mar 18, 2026 · Artificial Intelligence

Boosting LLM Accuracy: From RAG to GraphRAG for Enterprise Metadata Retrieval

This article explains the fundamentals of Retrieval‑Augmented Generation (RAG), introduces GraphRAG as an advanced architecture using knowledge graphs, details implementation pipelines, evaluates performance improvements, analyzes common pitfalls, and outlines future enhancements for enterprise metadata search.

AIGraphRAGKnowledge Graph
0 likes · 17 min read
Boosting LLM Accuracy: From RAG to GraphRAG for Enterprise Metadata Retrieval
DeepHub IMBA
DeepHub IMBA
Mar 15, 2026 · Artificial Intelligence

BookRAG: A Tree‑Graph Fusion RAG Framework for Hierarchical Documents

BookRAG introduces a tree‑graph fused Retrieval‑Augmented Generation framework that builds a native document index combining hierarchical layout trees with fine‑grained knowledge graphs, and employs an Information‑Foraging‑Theory‑inspired agent to dynamically navigate queries across complex, multi‑section documents.

Knowledge GraphRAGagent-based retrieval
0 likes · 11 min read
BookRAG: A Tree‑Graph Fusion RAG Framework for Hierarchical Documents
AI Engineering
AI Engineering
Mar 15, 2026 · Artificial Intelligence

Why Static Skills Fail and How Cognee Enables AI to Self‑Repair Its Prompts

The article explains silent drift in static AI skills, outlines Cognee’s five‑step loop—Skill Ingestion, Observe, Inspect, Amend, and Evaluate—to let agents automatically detect, analyze, and fix degrading prompts, and discusses community reactions and related self‑help projects.

Agent SkillsKnowledge GraphPrompt engineering
0 likes · 6 min read
Why Static Skills Fail and How Cognee Enables AI to Self‑Repair Its Prompts
AI Tech Publishing
AI Tech Publishing
Mar 12, 2026 · Artificial Intelligence

Why Context Engineering, Not Prompt Engineering, Is the Real Hard Work in the AI Era

The article reveals that while AI tools boost code output, they degrade quality, and that most failures stem from poor context management; it argues that true engineering effort lies in building structured, progressive context architectures—akin to infrastructure—using knowledge graphs, CLAUDE.md, and agent‑driven maintenance.

AI agentsAnthropicCLAUDE.md
0 likes · 14 min read
Why Context Engineering, Not Prompt Engineering, Is the Real Hard Work in the AI Era
DataFunSummit
DataFunSummit
Mar 9, 2026 · Artificial Intelligence

How SkillNet Turns Agentic Skills into Reusable Knowledge for Smarter AI

SkillNet introduces a large‑scale, structured skill knowledge base that lets AI agents capture, share, and reuse procedural abilities, dramatically improving benchmark performance and paving the way for more reliable, evolvable intelligent systems.

AI agentsKnowledge GraphPython SDK
0 likes · 13 min read
How SkillNet Turns Agentic Skills into Reusable Knowledge for Smarter AI
Data STUDIO
Data STUDIO
Mar 9, 2026 · Artificial Intelligence

Boost RAG Accuracy from 60% to 94% with 11 Proven Strategies

This article dissects why naive Retrieval‑Augmented Generation (RAG) often yields only 60% accuracy, then presents eleven concrete ingestion, query, and hybrid techniques—complete with code samples, performance trade‑offs, and real‑world case studies—that together can raise RAG accuracy to 94% while outlining practical implementation roadmaps and common pitfalls.

EmbeddingKnowledge GraphLLM
0 likes · 31 min read
Boost RAG Accuracy from 60% to 94% with 11 Proven Strategies
360 Tech Engineering
360 Tech Engineering
Mar 3, 2026 · Artificial Intelligence

How MMKG‑RDS Generates High‑Quality Multimodal Reasoning Data from Knowledge Graphs

The MMKG‑RDS framework introduced by 360 AI Lab creates a complete pipeline—from multimodal document parsing and knowledge‑graph construction to customizable task synthesis and multi‑dimensional quality assessment—enabling the production of high‑quality reasoning data that significantly boosts large‑model performance across diverse domains.

AI reasoningKnowledge Graphdata synthesis
0 likes · 7 min read
How MMKG‑RDS Generates High‑Quality Multimodal Reasoning Data from Knowledge Graphs
Radish, Keep Going!
Radish, Keep Going!
Mar 2, 2026 · Artificial Intelligence

Why Do Your AI Agents Forget Over Time? A 3‑Layer Memory Architecture to Keep Them Sharp

This article explains why AI agents lose recall after prolonged use, analyzes three core flaws in current markdown‑based memory designs, reviews recent research, and presents a deterministic, zero‑cost three‑layer architecture—including short‑term, daily, and long‑term storage, a lightweight knowledge graph, and active forgetting mechanisms—to maintain reliable agent memory.

Knowledge GraphLLMOpenClaw
0 likes · 16 min read
Why Do Your AI Agents Forget Over Time? A 3‑Layer Memory Architecture to Keep Them Sharp
AI Large Model Application Practice
AI Large Model Application Practice
Mar 2, 2026 · Artificial Intelligence

How to Build Your First Business Ontology for AI Agents – A Step‑by‑Step Guide

This article walks you through why enterprise AI agents need a semantic ontology, explains TBox and ABox concepts, outlines a general modeling workflow, introduces RDF/OWL standards and tools like Protégé and reasoners, and provides a hands‑on example—including Python code with Owlready2—to create and test a business ontology for order‑expedition rules.

Knowledge GraphOWLOntology
0 likes · 18 min read
How to Build Your First Business Ontology for AI Agents – A Step‑by‑Step Guide
IT Services Circle
IT Services Circle
Feb 27, 2026 · Artificial Intelligence

How GitNexus Gives AI a Full‑Code‑Base View to Prevent Hidden Bugs

GitNexus is an open‑source knowledge‑graph tool that indexes an entire codebase, exposing dependencies and call chains so AI assistants can understand global architecture, instantly show impact of changes, and dramatically reduce the risk of introducing new bugs during development.

CLIKnowledge GraphSoftware Engineering
0 likes · 6 min read
How GitNexus Gives AI a Full‑Code‑Base View to Prevent Hidden Bugs
PaperAgent
PaperAgent
Feb 27, 2026 · Artificial Intelligence

How HyperRAG Uses N‑ary Hypergraphs to Overcome Binary KG Limitations

HyperRAG introduces an n‑ary hypergraph retrieval framework that replaces binary knowledge‑graph triples with hyperedges, addressing semantic fragmentation and path‑explosion while delivering superior accuracy and efficiency across multiple closed‑ and open‑domain QA benchmarks.

HyperRAGHypergraphKnowledge Graph
0 likes · 6 min read
How HyperRAG Uses N‑ary Hypergraphs to Overcome Binary KG Limitations
AI Large Model Application Practice
AI Large Model Application Practice
Feb 19, 2026 · Artificial Intelligence

When Should You Add a Knowledge Graph? 6 Practical Decision Criteria

This article outlines six concrete criteria—relationship‑centric data, reproducible reasoning, evolving schemas, multi‑hop queries, explainable decisions, and cross‑system data integration—to help engineers decide whether a knowledge graph is the right solution or if a relational database will suffice.

AI EngineeringData IntegrationKnowledge Graph
0 likes · 15 min read
When Should You Add a Knowledge Graph? 6 Practical Decision Criteria
AI Tech Publishing
AI Tech Publishing
Feb 8, 2026 · Artificial Intelligence

Why Bigger Context Windows Fail and How Structured Graphs Deliver Precise Fact Retrieval

The article argues that large language models struggle with exact factual answers and that extending context windows often degrades performance, while knowledge graphs provide structured, traceable retrieval; it proposes a unified graph monograph and small, focused context slices to empower LLMs with accurate information.

Context RetrievalKnowledge GraphLLM
0 likes · 10 min read
Why Bigger Context Windows Fail and How Structured Graphs Deliver Precise Fact Retrieval
PaperAgent
PaperAgent
Feb 3, 2026 · Artificial Intelligence

Relink: Turning GraphRAG into a Dynamic, Query‑Driven Knowledge Graph

Relink introduces a ‘reason‑and‑construct’ paradigm that builds knowledge‑graph paths during inference, combining a high‑precision factual graph with a high‑recall potential‑relation pool, using query‑driven dynamic path expansion and contrastive alignment to markedly improve multi‑hop QA performance and robustness to sparse knowledge.

Dynamic RetrievalGraphRAGKnowledge Graph
0 likes · 8 min read
Relink: Turning GraphRAG into a Dynamic, Query‑Driven Knowledge Graph
PaperAgent
PaperAgent
Jan 6, 2026 · Artificial Intelligence

How Ontology‑Driven GraphRAG Eliminates Noise in AI Knowledge Graphs

This article examines the shortcomings of naïve GraphRAG implementations on clinical data and explains how an ontology‑driven, zero‑noise GraphRAG architecture can create self‑improving, conflict‑free knowledge graphs for AI applications.

AIData QualityGraphRAG
0 likes · 3 min read
How Ontology‑Driven GraphRAG Eliminates Noise in AI Knowledge Graphs
Advanced AI Application Practice
Advanced AI Application Practice
Jan 6, 2026 · Artificial Intelligence

Enterprise-Grade AI + Knowledge Graph for Automating Complex API Test Scenarios

The article details how an AI‑driven test platform combines large language models with a corporate‑level knowledge graph to automatically generate end‑to‑end API test scripts for complex business flows, overcoming context loss, dependency gaps, and scalability limits of single‑interface generation.

AIAPI testingKnowledge Graph
0 likes · 12 min read
Enterprise-Grade AI + Knowledge Graph for Automating Complex API Test Scenarios
AI Architecture Hub
AI Architecture Hub
Dec 27, 2025 · Artificial Intelligence

How GraphRAG Turns Knowledge Graphs into Smarter Retrieval for LLMs

GraphRAG extends traditional Retrieval‑Augmented Generation by building a knowledge graph from documents, extracting entities and relationships, performing community detection, and supporting both local and global searches, offering detailed step‑by‑step guidance, code examples, configuration tips, and a comparison with classic RAG approaches.

GraphRAGKnowledge GraphLLM
0 likes · 28 min read
How GraphRAG Turns Knowledge Graphs into Smarter Retrieval for LLMs
Architect
Architect
Dec 25, 2025 · Artificial Intelligence

How GraphRAG Boosts Retrieval Accuracy with Knowledge Graphs – A Complete Guide

This article explains why traditional RAG suffers from hallucinations, introduces GraphRAG’s knowledge‑graph‑based approach, walks through its indexing and query pipelines—including text splitting, entity‑relation extraction, graph construction, community detection, and local vs. global retrieval—provides practical setup commands, Neo4j visualization steps, and compares its performance with classic RAG.

EmbeddingGraphRAGKnowledge Graph
0 likes · 27 min read
How GraphRAG Boosts Retrieval Accuracy with Knowledge Graphs – A Complete Guide
DataFunSummit
DataFunSummit
Dec 19, 2025 · Cloud Native

How HiSilicon Uses Cloud‑Native Architecture to Build a Multi‑Modal Data Lake

Amid the AI wave, HiSilicon’s digital transformation tackles fragmented industrial data by adopting a cloud‑native, open‑source stack centered on Paimon, creating a unified metadata model, knowledge graph, and elastic scheduling that balances performance and cost while powering AI‑ready services across nine business domains.

AIKnowledge Graphbig-data
0 likes · 12 min read
How HiSilicon Uses Cloud‑Native Architecture to Build a Multi‑Modal Data Lake
PaperAgent
PaperAgent
Dec 18, 2025 · Artificial Intelligence

Can Ontology‑Aware KG‑RAG Double Table QA Performance on Industrial Standards?

This article presents an ontology‑aware knowledge‑graph RAG framework that transforms complex, hierarchical industrial standard documents into a graph of sections, atomic propositions, and refined triples, achieving nearly double F1 scores on table‑based QA tasks and robust performance on long documents.

Knowledge GraphLLMOntology
0 likes · 6 min read
Can Ontology‑Aware KG‑RAG Double Table QA Performance on Industrial Standards?
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 16, 2025 · Artificial Intelligence

How We Built an AI‑Powered Data Agent to Automate Data Retrieval at Scale

This article details the design and implementation of Matra, an AI‑driven data assistant for a large e‑commerce platform, covering the challenges of legacy data assets, knowledge‑base construction, GraphRAG integration, multi‑stage agent frameworks, practical results, and future plans for continuous improvement.

AIData RetrievalKnowledge Graph
0 likes · 22 min read
How We Built an AI‑Powered Data Agent to Automate Data Retrieval at Scale
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Dec 5, 2025 · R&D Management

Linking Zotero and Obsidian: From Paper Collection to Visual Knowledge Graph

This guide walks graduate researchers through a step‑by‑step workflow—collecting papers with Zotero, translating them via an LLM plugin, generating structured markdown notes, and then using Obsidian’s bidirectional links and Canvas to build a local, visual knowledge graph that ties individual citations into a coherent research map.

Knowledge GraphLLM translationObsidian
0 likes · 4 min read
Linking Zotero and Obsidian: From Paper Collection to Visual Knowledge Graph
DataFunSummit
DataFunSummit
Dec 1, 2025 · Artificial Intelligence

Why Palantir’s Ontology Approach Could Transform Enterprise AI – Insights from Industry Leaders

A detailed transcript of a closed‑door forum reveals how Palantir’s ontology methodology, combined with AI agents, addresses data semantics, knowledge governance, and enterprise‑level decision making, while highlighting practical challenges, evaluation frameworks, and the need for strong management and high‑quality data foundations.

Data GovernanceEnterprise AIKnowledge Graph
0 likes · 27 min read
Why Palantir’s Ontology Approach Could Transform Enterprise AI – Insights from Industry Leaders
JD Tech Talk
JD Tech Talk
Dec 1, 2025 · Artificial Intelligence

How JoyAgent Enables Multimodal RAG for Enterprise Knowledge Management

JoyAgent, JD's open‑source intelligent‑agent platform, now adds multimodal Retrieval‑Augmented Generation (RAG) capabilities, combining graph‑based knowledge, hierarchical chunking, and vision‑language models to handle text, images, tables, and API data for enterprise knowledge processing and evaluation.

Enterprise AIKnowledge GraphMultimodal RAG
0 likes · 11 min read
How JoyAgent Enables Multimodal RAG for Enterprise Knowledge Management
HyperAI Super Neural
HyperAI Super Neural
Nov 20, 2025 · Artificial Intelligence

From 9,874 Papers to 15,000 Structures: MOF‑ChemUnity Rebuilds MOF Knowledge for Explainable AI

MOF‑ChemUnity constructs a scalable, extensible knowledge graph that links millions of MOF names and synonyms to over 15,000 crystal structures using LLM‑driven entity matching, enabling accurate, explainable AI‑assisted material discovery, water‑stability prediction, expert recommendation validation, and graph‑enhanced retrieval across diverse applications.

Graph RAGKnowledge GraphMOF
0 likes · 17 min read
From 9,874 Papers to 15,000 Structures: MOF‑ChemUnity Rebuilds MOF Knowledge for Explainable AI
Data Party THU
Data Party THU
Nov 15, 2025 · Artificial Intelligence

How Reinforcement Learning Powers Intelligent AI Agents and LangGraph Workflows

This article explains how reinforcement learning (RL) underpins intelligent AI agents, covering the Markov Decision Process fundamentals, key RL components, multi‑hop reasoning on knowledge graphs, and a step‑by‑step LangGraph example that integrates an RL‑driven tutoring policy with Python code.

AI agentsKnowledge GraphLangGraph
0 likes · 17 min read
How Reinforcement Learning Powers Intelligent AI Agents and LangGraph Workflows
DataFunSummit
DataFunSummit
Nov 5, 2025 · Artificial Intelligence

How Alibaba’s Aivis Agent Is Transforming Cloud Customer Support

This article explores Alibaba Cloud’s digital employee Aivis, detailing why it was created, its multi‑layer architecture, core modules, agent‑driven reasoning, data processing, model training, autonomous workflow, trust‑building measures, and the collaborative human‑machine loop that boosts service efficiency.

Cloud ServicesKnowledge GraphPrompt engineering
0 likes · 18 min read
How Alibaba’s Aivis Agent Is Transforming Cloud Customer Support
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Oct 17, 2025 · Industry Insights

How Semantic Governance Fuels AI-Ready Data Management: A Practical Roadmap

This article outlines a comprehensive, three‑stage implementation framework for semantic governance, details the essential supporting technologies, proposes new organizational roles and collaborative mechanisms, and explores future trends such as agent integration and LLM‑driven ontology evolution to empower AI‑centric enterprise data strategies.

AIKnowledge Graphenterprise transformation
0 likes · 26 min read
How Semantic Governance Fuels AI-Ready Data Management: A Practical Roadmap
DataFunSummit
DataFunSummit
Sep 28, 2025 · Artificial Intelligence

Unlocking Enterprise Knowledge: Building Multimodal AI Systems with LLMs

This article examines the challenges of processing massive multimodal data in enterprises and presents a knowledge‑augmentation framework that leverages Retrieval‑Augmented Generation, memory‑inspired architecture, and feedback loops to enable reliable, scalable AI‑driven decision making across diverse business scenarios.

Enterprise KnowledgeKnowledge GraphLLM
0 likes · 29 min read
Unlocking Enterprise Knowledge: Building Multimodal AI Systems with LLMs
Tech Freedom Circle
Tech Freedom Circle
Sep 25, 2025 · Artificial Intelligence

RAGFlow Deep Dive: Data Parsing and Knowledge Graph Construction

This article examines RAGFlow's end‑to‑end pipeline for turning diverse documents into structured knowledge, detailing the TaskExecutor factory, the DeepDoc layout‑aware parser, chunking strategies, embedding and storage mechanisms, and the GraphRAG‑based knowledge‑graph extraction that together enable high‑precision retrieval and reasoning.

Data ParsingDeepDocElasticsearch
0 likes · 15 min read
RAGFlow Deep Dive: Data Parsing and Knowledge Graph Construction
DataFunTalk
DataFunTalk
Sep 25, 2025 · Big Data

How Tencent Cloud’s AI‑Ready Data Platform Redefines Big Data for AI

This article outlines the challenges of high‑quality data for AI, introduces Tencent Cloud’s AI‑Ready data platform with three core capabilities—DIaaS, Setats, and ES‑based knowledge search—covers the end‑to‑end WeData integration, intelligent agents for automation, and showcases ecosystem partnerships driving industry‑wide intelligent transformation.

AIBig DataData Platform
0 likes · 14 min read
How Tencent Cloud’s AI‑Ready Data Platform Redefines Big Data for AI
DataFunSummit
DataFunSummit
Sep 17, 2025 · Artificial Intelligence

How Tencent’s Large Language Model Powers Real-World AI Applications

This article explores Tencent’s large language model across diverse business scenarios—content generation, intelligent customer service, role‑playing, and more—detailing the principles and practical uses of Retrieval‑Augmented Generation (RAG), GraphRAG, and Agent technologies, and how they enhance model intelligence and user experience.

AIAgentKnowledge Graph
0 likes · 4 min read
How Tencent’s Large Language Model Powers Real-World AI Applications
Liangxu Linux
Liangxu Linux
Sep 12, 2025 · Artificial Intelligence

Explore 6 Cutting-Edge Open-Source AI Tools and Visual Guides

This article introduces six open‑source projects—including a visual guide for large‑model reinforcement learning, Alibaba's WebAgent suite, a 12‑factor AI‑agent handbook, Google’s MCP database toolbox, the Graphiti knowledge‑graph engine, and a Rust‑based distributed object store—each with key features and GitHub links.

AIAgentKnowledge Graph
0 likes · 6 min read
Explore 6 Cutting-Edge Open-Source AI Tools and Visual Guides
DaTaobao Tech
DaTaobao Tech
Sep 12, 2025 · Artificial Intelligence

How Multi‑Agent AI Transforms Financial Loss Prevention in E‑Commerce

This article explains how a multi‑agent AI system shifts asset‑loss control from reactive to proactive by building a full‑link protection framework that extracts knowledge, identifies risks, automatically deploys safeguards, and continuously learns from incidents, delivering faster, more accurate financial security for e‑commerce platforms.

AIKnowledge GraphMulti-Agent
0 likes · 19 min read
How Multi‑Agent AI Transforms Financial Loss Prevention in E‑Commerce
Architecture & Thinking
Architecture & Thinking
Sep 12, 2025 · Artificial Intelligence

How Knowledge Graphs Turn Large Language Models into Trustworthy Experts

Integrating structured knowledge graphs with generative AI provides traceable, explainable, and high‑precision reasoning across domains such as medicine, finance, and law, through techniques like Retrieval‑Augmented Generation, graph neural networks, and adaptive planning, dramatically reducing hallucinations and boosting expert‑level performance.

AI hallucinationGraph Neural NetworkKnowledge Graph
0 likes · 12 min read
How Knowledge Graphs Turn Large Language Models into Trustworthy Experts
Model Perspective
Model Perspective
Sep 8, 2025 · Artificial Intelligence

How to Build a Dynamically Updating Knowledge Graph for Mathematical Modeling

This article explains how to construct and continuously update a knowledge graph from mathematical modeling solutions, detailing extraction of entities, relations, attributes, and strategies, and showing how dynamic graphs enable intelligent recommendation, research support, and teaching assistance.

Dynamic UpdateKnowledge GraphNLP
0 likes · 9 min read
How to Build a Dynamically Updating Knowledge Graph for Mathematical Modeling
DataFunTalk
DataFunTalk
Aug 26, 2025 · Artificial Intelligence

Exploring Cutting-Edge AI & Knowledge Graph Applications: A Curated Resource Guide

This resource guide presents a curated list of cutting‑edge topics—including multimodal GraphRAG, knowledge‑graph‑driven large‑model applications in finance, traditional Chinese medicine, automotive manufacturing, and knowledge‑management trends—offering insights into AI‑powered knowledge services, and invites readers to scan the QR code to download the full e‑book.

AIData IntegrationKnowledge Graph
0 likes · 2 min read
Exploring Cutting-Edge AI & Knowledge Graph Applications: A Curated Resource Guide
360 Tech Engineering
360 Tech Engineering
Aug 12, 2025 · Artificial Intelligence

How Knowledge Graphs Are Reinventing AI Security: Insights from ISC.AI 2025

At the 13th ISC.AI 2025 Knowledge Graphs Reshaping Intelligent Security Summit in Beijing, leading experts from academia and industry highlighted how knowledge graphs enhance AI model accuracy, explainability, and trust, offering comprehensive data integration and risk monitoring to fortify intelligent systems across sectors.

Data IntegrationKnowledge Graphrisk monitoring
0 likes · 6 min read
How Knowledge Graphs Are Reinventing AI Security: Insights from ISC.AI 2025
Amap Tech
Amap Tech
Aug 7, 2025 · Artificial Intelligence

Boosting Codebase Upgrades with Code RAG and Agent‑Driven Fine‑Tuning

This article describes how the Gaode terminal team tackled large‑scale repository upgrades by building a code‑RAG and code‑Agent tool, addressing recall and stability issues, then fine‑tuning a small LLM (Qwen3‑4B) with LoRA and custom datasets to achieve reliable, low‑cost, on‑device code‑query performance.

Code AgentKnowledge GraphLLM
0 likes · 11 min read
Boosting Codebase Upgrades with Code RAG and Agent‑Driven Fine‑Tuning
Model Perspective
Model Perspective
Aug 4, 2025 · Databases

How to Build a Comprehensive Mathematical Modeling Knowledge Graph

This article explains why a mathematical modeling knowledge graph is needed, outlines its multi‑layer structure, and provides step‑by‑step guidance—from defining scope and collecting concepts to modeling nodes and relationships and visualizing the graph with Neo4j—highlighting its educational and research benefits.

AIKnowledge GraphNeo4j
0 likes · 8 min read
How to Build a Comprehensive Mathematical Modeling Knowledge Graph
Model Perspective
Model Perspective
Aug 1, 2025 · Artificial Intelligence

Why Tree Structures Limit Math Knowledge Graphs and How Network Thinking Helps

The article examines the shortcomings of using hierarchical tree models for K‑12 math knowledge graphs and proposes a network‑based graph approach that better captures cross‑topic relationships, supports flexible learning paths, and combines the strengths of both structures for richer educational design.

Curriculum DesignEducational TechnologyKnowledge Graph
0 likes · 8 min read
Why Tree Structures Limit Math Knowledge Graphs and How Network Thinking Helps
AI Large Model Application Practice
AI Large Model Application Practice
Jul 29, 2025 · Artificial Intelligence

8 Memory Strategies for AI Agents: From Full Recall to Vector Stores

The article examines eight common AI memory techniques—from simple full‑history retention to sophisticated vector‑store and knowledge‑graph approaches—detailing their principles, Python‑style implementations, advantages, drawbacks, and ideal application scenarios for large‑language‑model agents in production environments.

AI memoryKnowledge GraphLLM context management
0 likes · 23 min read
8 Memory Strategies for AI Agents: From Full Recall to Vector Stores
Model Perspective
Model Perspective
Jul 25, 2025 · Databases

How to Model and Deploy Knowledge Graphs with Neo4j and Python

This article explains the fundamentals of knowledge graph representation, including entities, concepts, relationships, and triple structures, and provides step‑by‑step instructions for installing Neo4j, configuring Python with py2neo, and importing CSV‑based triples into a graph database for querying and reasoning.

Knowledge GraphNeo4jPython
0 likes · 12 min read
How to Model and Deploy Knowledge Graphs with Neo4j and Python
DataFunTalk
DataFunTalk
Jul 21, 2025 · Artificial Intelligence

Top AI & Knowledge Graph Resources: A Curated Guide to Emerging Research

This article presents a curated list of cutting‑edge resources covering multimodal GraphRAG, knowledge‑graph‑driven large‑model applications in finance, healthcare, automotive, and more, offering insights into the evolving synergy between AI and knowledge graphs.

AIKnowledge GraphLarge Model
0 likes · 2 min read
Top AI & Knowledge Graph Resources: A Curated Guide to Emerging Research
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Jul 12, 2025 · Artificial Intelligence

Why GraphRAG Is the Future of Retrieval‑Augmented Generation

This article explains how GraphRAG combines knowledge graphs with retrieval‑augmented generation to overcome the limitations of vector‑only RAG, delivering higher accuracy, better explainability, easier development, and stronger governance for generative AI applications across various domains.

AIGraphRAGKnowledge Graph
0 likes · 23 min read
Why GraphRAG Is the Future of Retrieval‑Augmented Generation