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ontology engineering

9 articles · Page 1 of 1
dbaplus Community
dbaplus Community
Sep 23, 2026 · Artificial Intelligence

How Ontology Engineering Gives AI Agents a Business Cognitive Layer

This article details a four-layer ontology engineering system that transforms heterogeneous business data into a real-time ontology, enabling read-only AI agents to perform multi-hop reasoning over stable object identities, class relationships, and natural-language rules — demonstrated through a traffic accident investigation case study.

AI AgentKnowledge Graphbusiness semantics
0 likes · 25 min read
How Ontology Engineering Gives AI Agents a Business Cognitive Layer
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Sep 18, 2026 · Fundamentals

From Ontology to CBE: The Four-Layer Ladder for Flexible Ontology Application

The article distinguishes objective and perspective ontologies, identifies two gaps between static knowledge and dynamic systems, and proposes a four-layer engineering paradigm—Unique Ontology, Perspective Ontology, Component-Based Design, and BIP—to build executable ontology twins for planning and optimization.

BIPComponent-Based EngineeringDigital Twin
0 likes · 27 min read
From Ontology to CBE: The Four-Layer Ladder for Flexible Ontology Application
Data Bricklaying Diary
Data Bricklaying Diary
Sep 14, 2026 · Artificial Intelligence

Can LLMs Auto-Build Ontologies? AI Finds Candidates, Business Defines Reality

This article explains how large language models accelerate ontology engineering through candidate discovery, formalization, and validation, but cannot replace human responsibility for defining business objects, rules, and risk boundaries, proposing a seven-step human-AI collaboration process with three-zone isolation to prevent AI from polluting production baselines.

Knowledge GraphsLarge Language ModelsOWL
0 likes · 25 min read
Can LLMs Auto-Build Ontologies? AI Finds Candidates, Business Defines Reality
DataFunSummit
DataFunSummit
Sep 11, 2026 · Artificial Intelligence

Graph Engineering Restructures Agent Systems: From Harness to Ontology

This article reviews a 2026 paper on Graph Engineering for LLM agents, detailing the shift from individual agent intelligence to system intelligence via explicit task DAGs, runtime state management with checkpoints and replay, multi-agent coordination through capability modeling, and ontology engineering for shared semantics.

Agent CoordinationDAG SchedulingGraph Engineering
0 likes · 20 min read
Graph Engineering Restructures Agent Systems: From Harness to Ontology
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 9, 2026 · Artificial Intelligence

Why LLMs Fail at Critical Decisions: Ontologies Provide the Missing World Model

The author details how LLMs failed in underwater battlefield simulations despite trying prompts, agents, RAG, and knowledge graphs, and explains why ontologies — formal, reasoning-capable world models — are essential for trustworthy AI decisions, illustrating with a concrete case and a six-step ontology engineering process.

AI decision-makingKnowledge RepresentationLLM Limitations
0 likes · 12 min read
Why LLMs Fail at Critical Decisions: Ontologies Provide the Missing World Model
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 7, 2026 · R&D Management

ERP Implementation from an Ontology Perspective: Hidden Pitfalls No One Warns You About

This article analyzes ERP implementation failures through an ontology engineering lens, revealing how conceptual misalignment, logical inconsistencies, and physical migration risks derail projects, and proposes four principles: ontology-first design, 20% customization limit, master data governance, and phased domain evolution.

ERPImplementationOntology
0 likes · 14 min read
ERP Implementation from an Ontology Perspective: Hidden Pitfalls No One Warns You About
Data Bricklaying Diary
Data Bricklaying Diary
Aug 23, 2026 · Fundamentals

From Business Research to Ontology Modeling: 7-Step Framework for Verifiable Business Models

This article details a seven-step methodology to transform business research findings into verifiable business models ready for ontology engineering, using an order fulfillment risk case study to illustrate object identification, rule definition, evidence mapping, and validation through competency questions and boundary samples.

Data GovernanceVerificationbusiness modeling
0 likes · 28 min read
From Business Research to Ontology Modeling: 7-Step Framework for Verifiable Business Models
Data Bricklaying Diary
Data Bricklaying Diary
Aug 21, 2026 · Fundamentals

Why Ontology Projects Must Define Competency Questions First: Verifiable Acceptance from Day One

This article explains why ontology projects should define competency questions before modeling, detailing how these verifiable business questions constrain scope, expose data gaps, guide modeling trade-offs, and enable repeatable acceptance testing through traceability to data, rules, evidence, and test cases.

Knowledge RepresentationSemantic Modelingcompetency questions
0 likes · 22 min read
Why Ontology Projects Must Define Competency Questions First: Verifiable Acceptance from Day One
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 7, 2026 · Artificial Intelligence

Why RAG Misses, Agents Hallucinate, Code Stalls—Ontology as the Missing Semantic Layer

The article argues that the root cause of common AI deployment problems—poor RAG relevance, agent hallucinations, and brittle graph‑query code—is the lack of a unified semantic layer, and demonstrates how ontology engineering can supply a reasoning‑driven, adaptable contract that aligns concepts, constrains actions, and decouples business rules from implementation.

AI architectureAgentKnowledge Graph
0 likes · 9 min read
Why RAG Misses, Agents Hallucinate, Code Stalls—Ontology as the Missing Semantic Layer