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Knowledge engineering

26 articles · Page 1 of 1
DataFunSummit
DataFunSummit
Aug 9, 2026 · Artificial Intelligence

Ontology-Driven Knowledge Engineering for Enterprise AI Office Agents

The article analyzes the knowledge bottlenecks that hinder enterprise AI agents, proposes a three‑layer ontology‑driven architecture, details a six‑step ontology construction workflow, showcases concrete office‑automation scenarios (document review, meeting minutes, document structuring), and outlines evaluation metrics and a fast‑track rollout plan.

Enterprise AIKnowledge engineeringagentic AI
0 likes · 28 min read
Ontology-Driven Knowledge Engineering for Enterprise AI Office Agents
DataFunSummit
DataFunSummit
Jul 22, 2026 · Artificial Intelligence

Why Knowledge Bases Alone Can’t Empower AI Agents: The Need for Actionable Experience

Large language models may know a great deal, yet they still stumble on concrete tasks because knowledge must be transformed into actionable, context‑aware skills; this article analyses how skill representation, model‑specific cognition, and continuous practice reshape knowledge engineering for self‑evolving AI agents.

AI agentsExperience LearningKnowledge engineering
0 likes · 16 min read
Why Knowledge Bases Alone Can’t Empower AI Agents: The Need for Actionable Experience
DataFunTalk
DataFunTalk
Jul 20, 2026 · Artificial Intelligence

Why Knowledge Bases Alone Won’t Make AI Agents Effective: The Need for Actionable Experience

Large language models may know a lot, but they still fail at concrete tasks because knowledge must be transformed into actionable, experience‑based skills; the article analyzes how self‑evolving AI agents require skill libraries, context‑aware representations, and continuous practice‑driven knowledge production rather than static documentation.

AI AgentActionable KnowledgeAutonomous Systems
0 likes · 16 min read
Why Knowledge Bases Alone Won’t Make AI Agents Effective: The Need for Actionable Experience
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Jul 6, 2026 · Artificial Intelligence

From Feasibility to Self‑Evolution: Four‑Stage Intelligent UI Test Case Generation

The article analyzes Kuaishou’s four‑stage evolution—from a feasibility‑only LLM prompt to a self‑evolving multi‑agent system—showing how AI‑driven test case generation, knowledge injection, and automated review dramatically improve coverage, adoption, and maintenance efficiency in UI testing.

AI testingAutomationKnowledge engineering
0 likes · 22 min read
From Feasibility to Self‑Evolution: Four‑Stage Intelligent UI Test Case Generation
DataFunSummit
DataFunSummit
Jul 1, 2026 · Artificial Intelligence

Ontologies: The Semantic Operating System for Large‑Model AI

While the industry has spent the last two years chasing ever larger language models, enterprises actually lack a unified, computable and evolvable semantic structure, and ontologies—re‑imagined as a semantic operating system—provide the necessary backbone for reliable, business‑aware AI deployment.

Enterprise AIKnowledge engineeringLarge Language Models
0 likes · 16 min read
Ontologies: The Semantic Operating System for Large‑Model AI
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Jun 26, 2026 · Artificial Intelligence

Component-Based Engineering (CBE) Agents: From Single-Task AI to Systematic Project Delivery

Current AI agents excel at isolated tool calls but cannot reliably deliver complex projects; this article analyzes their limitations, introduces a Component-Based Engineering (CBE) paradigm with a fixed meta‑model for formal problem modeling and automated solving, enabling systematic, verifiable, outcome‑based AI agent deployments.

AI agentsComponent-Based EngineeringConstraint Solving
0 likes · 20 min read
Component-Based Engineering (CBE) Agents: From Single-Task AI to Systematic Project Delivery
DataFunTalk
DataFunTalk
Jun 19, 2026 · Artificial Intelligence

From Code Generation to Self‑Healing Ops: How AI Agents Drive Enterprise Efficiency

A technical livestream with experts from DeepSecurity, Ping An Life and China Mobile Jiutian reveals that the real bottleneck for AI agents in enterprises is not model capability but engineering, organizational processes and risk control, and outlines concrete strategies—code graphing, layered constraints, verification loops and metric‑driven adoption—to turn probabilistic AI output into reliable, organization‑wide productivity.

AI AgentAI Native WorkflowEnterprise AI
0 likes · 16 min read
From Code Generation to Self‑Healing Ops: How AI Agents Drive Enterprise Efficiency
DataFunSummit
DataFunSummit
Jun 13, 2026 · Artificial Intelligence

Beyond General LLMs: Efficient Adaptation and Data Value Mining for Finance

The article details a systematic practice—starting from the “iceberg” challenges of finance, through data and knowledge engineering, reverse knowledge extraction with REER, multi‑dimensional synthetic data generation, prompt engineering (APO), cost‑aware fine‑tuning, inference acceleration, and emotion‑value evaluation—culminating in actionable guidelines for deploying large models in banking scenarios.

Inference AccelerationKnowledge engineeringLarge Language Models
0 likes · 14 min read
Beyond General LLMs: Efficient Adaptation and Data Value Mining for Finance
DataFunSummit
DataFunSummit
May 19, 2026 · Databases

Building an AI‑Ready, Multi‑Modal Database Foundation for the Digital Intelligence Era

The talk outlines the AI era’s three major challenges for large language models and data (stability, 3V, resource limits), presents YashanDB’s original theory breakthroughs—including resource‑constrained computing, shared‑cluster performance and adaptive transaction scheduling—details a multi‑modal fusion architecture, introduces the KSA trustworthy knowledge‑engineering framework, and showcases real‑world deployments in smart‑city and energy domains.

AI-Ready Data PlatformEnergy IndustryKnowledge engineering
0 likes · 16 min read
Building an AI‑Ready, Multi‑Modal Database Foundation for the Digital Intelligence Era
dbaplus Community
dbaplus Community
May 14, 2026 · Big Data

Building a ‘One‑Sentence Bank’: Big Data and AI Fusion for Small Banks

The article outlines the evolution of big data in banking, compares management models for heterogeneous data, describes the shift from data engineering to knowledge engineering, introduces LLMOps for high‑quality knowledge bases, and details how integrating AI and data can enable a “one‑sentence bank” that answers queries and executes tasks.

Artificial IntelligenceBankingBig Data
0 likes · 22 min read
Building a ‘One‑Sentence Bank’: Big Data and AI Fusion for Small Banks
DataFunSummit
DataFunSummit
Apr 22, 2026 · Artificial Intelligence

From Flawed RAG to Production‑Ready: Deep Dive into Scaling Retrieval‑Augmented Generation

This expert roundtable dissects why RAG often fails in production—low recall, hallucinations, cost overruns—and walks through concrete diagnostics, hybrid search designs, knowledge‑engineering tricks, GraphRAG and Agentic RAG advances, plus practical deployment, security, and cost‑optimization guidelines.

AI deploymentAgentic RAGHybrid Search
0 likes · 20 min read
From Flawed RAG to Production‑Ready: Deep Dive into Scaling Retrieval‑Augmented Generation
phodal
phodal
Apr 8, 2026 · R&D Management

How to Turn a Decade of Writing into a Reusable AI Skill

The author explains how, after ten years of writing, they analyzed their own articles, extracted evolving stylistic patterns, and engineered a modular, reusable writing skill—/phodal-writer/—that can be repeatedly loaded by AI to produce consistently structured, paced, and judgment‑rich content.

AI WritingKnowledge engineeringPrompt Engineering
0 likes · 14 min read
How to Turn a Decade of Writing into a Reusable AI Skill
AI Architecture Hub
AI Architecture Hub
Apr 8, 2026 · Artificial Intelligence

Turn LLMs into Knowledge Engineers: Build a Self‑Growing Obsidian Wiki

This article explains how Andrej Karpathy's LLM‑plus‑Obsidian workflow transforms large language models into continuous knowledge engineers, detailing a three‑layer architecture, core operations, practical setup steps, and open‑source tools that enable a self‑maintaining, compounding personal wiki.

Knowledge engineeringLLMObsidian
0 likes · 16 min read
Turn LLMs into Knowledge Engineers: Build a Self‑Growing Obsidian Wiki
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Mar 30, 2026 · Industry Insights

Will 99% of Software Engineers Become Redundant? A Cold Look at AI’s Impact on the Tech Workforce

The article examines how AI’s exponential acceleration is reshaping software development, debunking the hype that 99% of engineers will be obsolete, highlighting real data on junior job loss, the rise of knowledge engineering, technical debt risks, and the need for engineers to evolve into AI‑enabled system designers.

AIAutomationIndustry Trends
0 likes · 15 min read
Will 99% of Software Engineers Become Redundant? A Cold Look at AI’s Impact on the Tech Workforce
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Mar 17, 2026 · Artificial Intelligence

Andrew Ng’s Agent Skills: From Basics to Must‑Know Essentials

This article breaks down Andrew Ng and Anthropic’s Agent Skills course, explaining how organized skill folders give general AI agents domain expertise, repeatable workflows, and new capabilities, while using portable, composable design and progressive disclosure to make agents reliable, scalable, and industrial‑grade.

AI AgentAgent SkillsArtificial Intelligence
0 likes · 9 min read
Andrew Ng’s Agent Skills: From Basics to Must‑Know Essentials
Efficient Ops
Efficient Ops
Jan 26, 2026 · Artificial Intelligence

Why AI Skills Will Redefine Agents Beyond MCP

This article explains how AI Skills serve as structured knowledge bases that complement, rather than replace, Model Context Protocols, enhance Retrieval‑Augmented Generation, and drive three major trends—standardized agent stacks, low‑code knowledge engineering, and the emergence of personal AI agents.

AI EcosystemAI agentsKnowledge engineering
0 likes · 8 min read
Why AI Skills Will Redefine Agents Beyond MCP
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Jan 16, 2026 · Artificial Intelligence

How to Evaluate Ontology Quality: Metrics, Methods, and Tools

This article surveys ontology quality evaluation by outlining key metrics such as consistency, completeness, and coverage, and reviewing five major assessment approaches—including corpus‑based, gold‑standard, metric‑driven, rule‑based, and application‑driven methods—while highlighting representative tools, open‑source implementations, and future research challenges.

Knowledge engineeringLarge Language ModelsSemantic Web
0 likes · 20 min read
How to Evaluate Ontology Quality: Metrics, Methods, and Tools
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 24, 2025 · Artificial Intelligence

Beyond RAG: Three Emerging Knowledge‑Engineering Strategies (ICL, Online Learning, SLM)

The article outlines three post‑RAG knowledge‑engineering approaches—In‑Context Learning with dynamic few‑shot selection, Online Learning encompassing Meta‑Learning and Lifelong Learning to quickly adapt to new tasks, and the Small Language Model path that combines fine‑tuned task‑specific experts with LLM‑SLM collaboration for efficient, privacy‑preserving inference.

Knowledge engineeringLLMLifelong Learning
0 likes · 4 min read
Beyond RAG: Three Emerging Knowledge‑Engineering Strategies (ICL, Online Learning, SLM)
DataFunTalk
DataFunTalk
Feb 11, 2025 · Artificial Intelligence

Roundtable on Enhancing Large Model Effectiveness: RAG, Tool Use, and Knowledge Engineering

Experts from Dipu, Ant Financial, iKang, and Zhihu discuss practical strategies for improving large model performance, covering RAG, tool‑using, offline knowledge engineering, multimodal training, evaluation metrics, and future trends, while sharing case studies from manufacturing, healthcare, retail, and C‑end applications.

Knowledge engineeringLarge Language ModelsRAG
0 likes · 9 min read
Roundtable on Enhancing Large Model Effectiveness: RAG, Tool Use, and Knowledge Engineering
AntTech
AntTech
Aug 12, 2024 · Artificial Intelligence

DKCF Trustworthy Framework for Large Model Applications and AI Security Practices

The article outlines the DKCF (Data‑Knowledge‑Collaboration‑Feedback) trustworthy framework presented at the 2024 Shanghai Cybersecurity Expo, detailing challenges of large AI models, four key trust factors, and Ant Group's practical security implementations for professional AI deployments.

AI safetyDKCFKnowledge engineering
0 likes · 10 min read
DKCF Trustworthy Framework for Large Model Applications and AI Security Practices
DataFunTalk
DataFunTalk
May 5, 2022 · Artificial Intelligence

NLP Evolution: Symbolic Deep Parsing vs Neural Pre‑trained Models, Low‑Code Trends, and Semi‑Automated Applications

The article reviews the history and current state of NLP, compares symbolic deep‑parsing and neural pre‑trained approaches, discusses the knowledge‑bottleneck and low‑code trend, and illustrates semi‑automated, low‑code NLP deployment in the financial domain while pondering future integration of symbolic and neural methods.

Knowledge engineeringLow-codeNLP
0 likes · 23 min read
NLP Evolution: Symbolic Deep Parsing vs Neural Pre‑trained Models, Low‑Code Trends, and Semi‑Automated Applications
DataFunTalk
DataFunTalk
Jan 20, 2020 · Artificial Intelligence

The Second Half of Knowledge Graphs: Opportunities and Challenges

This comprehensive report analyzes the evolution of knowledge graphs, reviews achievements of the first half, and examines the challenges and opportunities of the emerging second half, highlighting shifts from large‑scale simple applications to complex, expert‑driven scenarios, and outlining strategies for representation, acquisition, and application in the era of big data and AI.

AIKnowledge GraphKnowledge engineering
0 likes · 30 min read
The Second Half of Knowledge Graphs: Opportunities and Challenges
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 4, 2018 · Artificial Intelligence

Turning Fashion Into AI‑Ready Data: Building Practical Image Datasets

This article explains how Alibaba's Image & Beauty team designs and iterates a practical fashion image dataset by aligning data purpose, integrating professional knowledge, handling sample scarcity and structured noise, and defining fine‑grained evaluation metrics to enable AI models that truly understand clothing.

Knowledge engineeringcomputer visiondata annotation
0 likes · 34 min read
Turning Fashion Into AI‑Ready Data: Building Practical Image Datasets
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 23, 2017 · Artificial Intelligence

How Large-Scale Knowledge Graphs Are Shaping AI and Natural Language Understanding

The December 20 Knowledge Graph symposium in Hangzhou, organized by Alibaba and the Chinese Society of Computational Linguistics, gathered leading Chinese scholars who discussed the pivotal role of massive knowledge graphs in AI, natural language processing, knowledge engineering, reasoning, and data‑driven intelligence.

Artificial IntelligenceKnowledge GraphKnowledge engineering
0 likes · 12 min read
How Large-Scale Knowledge Graphs Are Shaping AI and Natural Language Understanding