The article explains why AI agents need a dedicated memory system, describes a three‑layer memory architecture (working, short‑term, long‑term), and outlines four critical design decisions—what to write, when to read, how to update/forget, and isolation—illustrated with real‑world examples and industry solutions.
This article outlines a comprehensive, multi‑layered approach—including data anchoring, architectural guardrails, prompt engineering, and LLMOps—to mitigate hallucinations in large language models for enterprise applications.
The article introduces IDAKE, a three‑layer, five‑step methodology that combines intent‑driven testing, specification‑driven contracts, multi‑agent collaboration, knowledge‑graph guidance, and complex‑adaptive system theory to address the imbalance between unconstrained AI coding and over‑specification in modern software engineering.
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.
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.
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.
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.
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.
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.
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.
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.
The article argues that Software Engineering 3.0 is fundamentally a continuous entropy‑reduction process, using information theory to explain how large language models turn ambiguous human intent into precise executable code, why knowledge graphs outperform documents, and how multi‑agent systems improve quality assurance.
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.
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.
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.
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.
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.
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.
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.
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.
Clawdbot (now Moltbot) upgrades its memory from a static file‑based store to a three‑layer dynamic knowledge graph that automatically extracts facts, aggregates weekly summaries, and preserves history, enabling the AI to stay current with a user’s life without manual edits.
This article details a comprehensive root cause analysis system for Alibaba Cloud big data platforms, covering a five-layer architecture, three-dimensional analysis (cluster health, task execution, data pipelines), knowledge graph-driven automation, and AI-enhanced future directions to shift from reactive firefighting to proactive defense.
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.
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.
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.
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.
This article explains the definitions, differences, and integration trends of Knowledge Bases and Knowledge Graphs within Retrieval‑Augmented Generation, helping developers decide which technology best fits their AI system requirements.
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.
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.
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.
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.
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.
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.
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.
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.
The article analyzes how leading Chinese tech firms are moving AI agents from simple code‑completion tools to comprehensive digital teammates that assist across the entire software development lifecycle, highlighting real‑world implementations, multi‑agent collaboration, challenges, and future trends.
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.
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.
This article outlines a three‑part framework for AI‑driven testing—building a knowledge‑graph‑based cognitive brain, deploying autonomous Android GUI AI agents, and integrating AI into DevOps pipelines—to transform software testing from fragile scripting to intelligent, self‑optimizing processes.
The article examines the evolution from intelligent, assistant‑style testing to fully autonomous, LLM‑driven test agents, outlining four core capabilities, real‑world implementations across unit, API, and UI layers, and the technical pillars that enable self‑learning, self‑healing, and multi‑modal testing.
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.
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.
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.
This article examines Tencent's large language model deployments across content generation, intelligent customer service, and role‑playing, detailing the underlying SFT, Retrieval‑Augmented Generation, GraphRAG, and Agent technologies that enable smarter, more reliable AI solutions.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The article recounts a 2016 hand‑crafted software testing panorama, then shows how the Claude 4 Sonnet Think model can automatically produce several versions of a software testing knowledge graph, analyzes its entity hierarchy and relationships, critiques gaps, and outlines future plans to store the graph in a database for enhanced testing education.
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.
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.
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.
This resource compiles a curated list of cutting‑edge studies and frameworks that combine multimodal GraphRAG, knowledge‑graph techniques, and large‑model AI across sectors such as finance, healthcare, and manufacturing, guiding readers through emerging paradigms and future directions.
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.
This article demonstrates how to construct a knowledge graph from a 58,099‑character document using GraphRAG, measures the indexing time on an M2 PRO machine, and compares Global, Local, and Drift query modes, highlighting three scenarios where Local mode is unsuitable.
This article presents a curated catalog of cutting‑edge AI resources, covering multimodal GraphRAG, knowledge‑graph and large‑model integration, financial industry AI products, Chinese‑medicine decision support, AI‑driven knowledge‑graph evolution, private‑domain Q&A pipelines, and emerging trends and standards, with a QR code for the full ebook.
This article presents a comprehensive overview of cutting‑edge research on integrating large language models with knowledge graphs, covering multimodal GraphRAG, financial AI solutions, traditional Chinese medicine decision support, and industry‑specific knowledge services, guiding readers through emerging paradigms and practical implementations.
This article walks through the complete GraphRAG workflow—from environment setup and indexing to command‑line and Python API queries—demonstrating how to build a knowledge graph, tune prompts, and retrieve high‑quality answers using Microsoft’s GraphRAG implementation.
This article examines how Retrieval‑Augmented Generation (RAG) has progressed from simple keyword‑based retrieval to advanced semantic methods, modular architectures, graph‑enhanced reasoning, and autonomous agentic systems, highlighting each approach's workflow, benefits, limitations, and the shift toward dynamic AI decision‑making.
This article walks through GraphRAG’s retrieval phase, showing how knowledge‑graph entities, relationships, and community reports are assembled into a query context, comparing local and global modes with traditional RAG, and illustrating the process with a concrete “Age of Big Data” example.
This article walks through the complete GraphRAG knowledge‑graph building pipeline—text splitting, entity extraction, relation mining, community clustering, and report generation—using a concrete example from the book “The Age of Big Data,” and explains why each step improves retrieval and answer quality.
This article explains GraphRAG’s architecture, compares it with traditional RAG, and presents experimental results showing that GraphRAG’s knowledge‑graph‑driven retrieval markedly improves answer accuracy, especially on low‑match, multi‑paragraph queries.
This article explores how a testing team can abstract core testing capabilities, construct a unified testing ecosystem, and integrate AI at strategic points—from data aggregation and automated analysis to knowledge‑base driven requirement enhancement and fully automated test case generation—ultimately turning quality assurance into a collaborative human‑AI partnership.
This article reviews six open‑source MCP servers—Bright Data, Graphiti, GitIngest, Terminal, Code Executor, and MindsDB—showing how each extends large language models with web scraping, long‑term memory, code navigation, command‑line control, sandboxed Python execution, and multi‑source data integration, complete with practical code examples.
This article explores DeepSeek's open‑source large‑model breakthroughs, PingCAP's AI‑enhanced database roadmap, TiDB.AI's retrieval‑augmented generation framework, the unified TiDB data engine, and practical Q&A insights on knowledge‑graph construction, vector search, and AI‑driven SQL generation.
The article argues that to unlock the disruptive potential of large language models in software engineering, they should be integrated from the requirements stage through the whole software development lifecycle, providing full semantic context, boosting overall efficiency, and creating an evolving project knowledge graph.
The article explains how large AI models are evolving from simple chatbots into reasoning-powered agents integrated with massive domain knowledge, creating powerful “industrial brains” that can understand and solve complex real‑world problems, reshaping the future of IT and the internet industry.
This article explains how the rapid development of large language models and knowledge graph technologies creates new opportunities for enterprise knowledge management, outlines the challenges of massive unstructured data, describes the architecture and core data flow of a corporate knowledge brain, and showcases key technologies and real‑world applications.
The article explains how AutoDev’s Context Worker pre‑generates semantic code context to improve RAG performance, outlines the limitations of vector‑based retrieval, describes the tool’s multi‑language AST analysis, knowledge‑graph construction, and provides command‑line usage examples for integrating the generated context into AI‑driven development workflows.
The article examines Glean’s evolution from an enterprise‑search startup to a comprehensive Work AI Platform, detailing its market growth, competitive positioning, technical architecture—including data connectors, knowledge graphs, custom models, and agent reasoning—and the strategic challenges it must overcome to sustain its lead.
This article presents a comprehensive overview of the security challenges in HarmonyOS native ArkTS applications and describes the design and implementation of a specialized static analysis framework—including source extraction, data‑flow and inter‑function analysis, knowledge‑graph construction, and risk inference engine—while also outlining integration into development pipelines and future research directions.
This article explores how knowledge graphs can be tightly integrated with large language models through prompt engineering, fine‑tuning, retrieval‑augmented generation, reasoning collaboration, and knowledge agents, outlining technical pathways, practical implementations, and future research directions across AI domains.
This article demonstrates how to construct an AI‑powered medical knowledge base for diabetes treatment by preprocessing literature, performing semantic chunking, generating BioBERT embeddings, storing them in a FAISS vector database, and using a RAG framework together with a knowledge graph to retrieve and generate accurate answers.
This article details Alibaba's security team's comprehensive approach to detecting ArkTS code vulnerabilities in HarmonyOS apps, covering architectural design, static analysis techniques, knowledge‑graph modeling, CI/CD integration, and future AI‑driven enhancements.
Researchers from Ant Group and Zhejiang University propose K-ON, a multi-token parallel prediction method that enables large language models to perceive knowledge graph entities through entity-level contrastive learning, achieving superior performance, lower cost, and higher efficiency on KG completion benchmarks.
The article explains Microsoft’s open‑source PIKE‑RAG system, detailing its four progressive stages—from knowledge‑base construction to creative multi‑agent reasoning—while describing the underlying modules, chunking strategies, multi‑granularity retrieval, and code snippets that enable specialized domain understanding and inference.
This issue reviews China's first open‑source short‑film model SkyReels‑V1, DeepSeek's Native Sparse Attention breakthrough, xAI's massive Grok‑3 deployment on 200k H100 GPUs, and a knowledge‑graph‑guided RAG framework, highlighting their performance gains, architectural innovations, and industry impact.
This article explains how NebulaGraph’s large‑scale graph database can be used to construct real‑time risk‑control knowledge graphs, describes practical applications such as community detection and path analysis, and explores how large language models enhance graph queries through Text‑to‑GQL, agents, exploration chains, and semi‑structured knowledge extraction.
Faced with billions of legal documents and the shortcomings of keyword search, Chinese legal professionals are turning to the AI‑powered YuanDian WenDa engine, which leverages Baidu's Wenxin model, a structured legal database, and prompt‑engineering to deliver trustworthy, citation‑rich answers and rapid research reports.
This article examines how programming large models combined with code knowledge graphs can overcome the limited context of traditional code‑completion tools, detailing key techniques, trigger strategies, context acquisition methods, model fine‑tuning practices, current challenges, and future research directions for intelligent, repository‑wide code suggestions.
At the 2024 Global Machine Learning Conference in Beijing, 360 AI Research Institute showcased cutting‑edge multimodal large‑model research, fine‑grained open‑world object detection, and document understanding technologies, highlighting open‑source releases, real‑world deployments, and competitive achievements in AI competitions.
This article explains how to build a GraphRAG system that integrates knowledge graphs and vector embeddings using PolarDB, Alibaba Cloud's Tongyi Qianwen LLM, and LangChain, demonstrating improved retrieval‑augmented generation accuracy through combined graph‑and‑vector search.
The article details Ant Group’s AI‑driven security parallel plane and intelligent threat detection system, its DKCF‑based architecture, key modules for data correlation, unknown threat discovery, alarm reduction, and knowledge‑graph integration, and its recognition in the 2024 AI Pioneer Case Collection.
This article reviews the China Academy of Information and Communications Technology's advancements in large‑model‑driven data intelligence, covering development trends, key deployment technologies such as prompt engineering, fine‑tuning and RAG, emerging application paradigms, challenges, and a series of newly drafted standards to guide industry adoption.
This article analyzes the Meta Comprehensive RAG (CRAG) benchmark, detailing its three tasks, evaluation metrics, and the champion DB3 team's end‑to‑end solution that combines data preprocessing, dual‑stage retrieval, prompt engineering, LoRA‑based fine‑tuning, and public data augmentation to achieve top scores across all tasks.
The article examines how large language models are reshaping JD Retail's e‑commerce search and recommendation pipelines, detailing industry evolution, technical challenges such as knowledge hallucination, intent understanding, personalization, cost, and safety, and presenting JD's end‑to‑end AIGC architecture, data preprocessing, alignment, evaluation, and next‑generation AI search solutions.
This article shares the exploration and practice of using Baidu's Wenxin large model to build a commercial intelligence assistant, highlighting its impact on business revenue and user experience, code generation, knowledge graph integration, database query optimization, and visual analytics for enhanced data analysis.
The presentation by Dr. Su Yu outlines challenges such as data sparsity and delayed learning effects in AI‑driven education, introduces three technical breakthroughs—domain‑specific LLM training, small‑knowledge learning via hierarchical knowledge graphs, and reinforcement‑based cognitive recommendation—and showcases product applications like the Frog Programming Platform, AI Programming Learning Machine, and digital‑human AI recorded courses.
This article describes how a commercial search platform iteratively upgrades its system by structuring business knowledge into a knowledge graph, applying multi‑stage entity extraction (CRF, Electra‑CRF, GLM‑3, OCR), and leveraging large language models to improve relevance, user experience, and revenue.
The KAG framework combines knowledge‑graph‑driven symbolic reasoning with large language model generation to improve accuracy, reduce hallucinations, and enable controllable, domain‑specific AI services such as government and medical Q&A, with open‑source support via OpenSPG and TuGraph‑DB.
The 2024 Inclusion·Bund Conference hosted a forum on "Large Model Creativity Boundaries and Application Imagination," featuring leading AI experts who discussed agents, multimodal technology, knowledge graphs, announced a new industry alliance, unveiled three major model products, and presented a trustworthy AI framework report for finance, healthcare, and government sectors.
This article presents a comprehensive overview of 360's document cloud knowledge management and Q&A scenarios, discussing business pain points, large‑model challenges, the advantages of the intelligent document solution, and how knowledge graphs enhance retrieval‑augmented generation and document standardization for AI‑driven enterprise applications.
This article presents a comprehensive exploration of knowledge‑graph‑based modeling, neural‑symbolic multi‑hop reasoning, and large‑model‑driven question answering applied to precise medication decision‑making in hypertension, detailing system architecture, experimental evaluations, real‑world deployments, and future research directions.
On June 25, 2024, OpenKG hosted a hybrid academic salon at Alibaba Cloud Valley, featuring expert talks on knowledge graphs, large language models, and their joint impact on AI, with presentations from leading researchers and industry professionals across multiple sessions.
