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OWL

21 articles · Page 1 of 1
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Oct 20, 2026 · Industry Insights

Why No Chinese Company Has Built a Real Ontology Product: The 10-System Gap

The article explains why Chinese ontology product companies fail: a 5-month, 10-person demo only takes the shortest path for each of ten required systems — data ingestion, mapping, modeling, standards, versioning, collaboration, quality, reasoning, operations, and delivery — while a production product needs all ten fully built and integrated, a challenge that demands a full-stack architect plus AI leverage.

Data GovernanceGraphRAGKnowledge Graph
0 likes · 10 min read
Why No Chinese Company Has Built a Real Ontology Product: The 10-System Gap
inShocking
inShocking
Oct 19, 2026 · Fundamentals

Modeling Engineering Performance Guarantee Insurance as an Ontology

This article explains construction performance guarantee insurance parties and processes, traces ontology's evolution from philosophy to semantic web standards, and demonstrates building an ontology model for the insurance business with classes, properties, axioms, instances, and versioning for amount changes.

OWLRDFSHACL
0 likes · 21 min read
Modeling Engineering Performance Guarantee Insurance as an Ontology
Geek Labs
Geek Labs
Oct 13, 2026 · Operations

omdrop-owl Brings AirDrop to Linux on MediaTek Wi-Fi While Keeping Wi-Fi Active

The omdrop-owl project enables bidirectional AirDrop file transfer between Linux (on MediaTek MT7925 Wi-Fi) and Apple devices by running the AWDL protocol in userspace, piggybacking on the existing Wi-Fi channel so the network connection stays active, with installation steps, hardware compatibility details, and known limitations documented.

AWDLAirDropFramework 13
0 likes · 14 min read
omdrop-owl Brings AirDrop to Linux on MediaTek Wi-Fi While Keeping Wi-Fi Active
Data Bricklaying Diary
Data Bricklaying Diary
Sep 24, 2026 · Artificial Intelligence

Why Your Knowledge Graph Fails to Explain Business: The Missing Ontology Layer

This article uses an order management example to explain why a knowledge graph alone cannot capture business meaning, defines ontology as explicit computer-processable descriptions of concepts and constraints, distinguishes ontology from knowledge graphs and graph databases, compares OWL and OPM modeling approaches, and provides six validation questions for ontology-driven data governance.

Business OntologyData GovernanceGraph Database
0 likes · 21 min read
Why Your Knowledge Graph Fails to Explain Business: The Missing Ontology Layer
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.

GovernanceKnowledge GraphsLarge Language Models
0 likes · 25 min read
Can LLMs Auto-Build Ontologies? AI Finds Candidates, Business Defines Reality
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 8, 2026 · Backend Development

How Domain Ontologies Act as Semantic Adapters for Cloud ERP Integration

This article proposes using domain ontologies as semantic adapters in cloud ERP architectures to resolve multi-tenant data heterogeneity, semantic drift, and system mismatch through a three-layer mechanism covering concept mapping, granularity conversion, and rule formalization, with a sidecar deployment pattern and applicability guidelines.

OWLRDFcloud ERP
0 likes · 10 min read
How Domain Ontologies Act as Semantic Adapters for Cloud ERP Integration
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 3, 2026 · Industry Insights

OntoL: Ontology Modeling & Semantic Layer for Enterprise Knowledge Governance

This article analyzes OntoL, an ontology modeling tool for enterprise knowledge governance, detailing its three-layer architecture, core capabilities like visual modeling and versioning, applicable scenarios, current technical limitations, and future roadmap including SHACL constraints and federated query.

Knowledge GovernanceKnowledge GraphOWL
0 likes · 9 min read
OntoL: Ontology Modeling & Semantic Layer for Enterprise Knowledge Governance
AI Large Model Application Practice
AI Large Model Application Practice
Aug 31, 2026 · Artificial Intelligence

What Is an Ontology? 9 Questions to Understand Ontologies & Their Role in AI Agents

This beginner-friendly guide explains ontologies through nine key questions, covering their definition, difference from databases and knowledge graphs, standards like RDF and OWL, a telecom domain example, integration with AI agents, and practical steps to build a minimal viable ontology for enterprise use.

AI AgentEnterprise ArchitectureKnowledge Graph
0 likes · 33 min read
What Is an Ontology? 9 Questions to Understand Ontologies & Their Role in AI Agents
Data Bricklaying Diary
Data Bricklaying Diary
Aug 24, 2026 · Fundamentals

Ontology Isn't Esoteric: You Already Process Its Raw Materials Daily

This article explains that ontology modeling uses familiar business objects, attributes, relationships, and constraints from daily data and process work, but adds cross-system unified semantics, explicit computable constraints, machine verification, and continuous operations to enable data, rules, systems, and AI agents to collaborate on a shared business world.

AI readinessEnterprise ArchitectureKnowledge Graphs
0 likes · 20 min read
Ontology Isn't Esoteric: You Already Process Its Raw Materials Daily
Digital Deification
Digital Deification
Aug 19, 2026 · Artificial Intelligence

Enterprise AI's Bottom Line: Semantic Firewall Keeps LLM Hallucinations Out of Production

The article argues that enterprises must not let large language models directly execute production actions; instead, an ontology-based semantic firewall should validate every LLM output against defined business concepts, relationships, rules, and terminology before allowing execution, preventing hallucinations from causing real-world accidents.

Enterprise AIHallucination BlockingLLM safety
0 likes · 11 min read
Enterprise AI's Bottom Line: Semantic Firewall Keeps LLM Hallucinations Out of Production
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 16, 2026 · Artificial Intelligence

Master TBox vs ABox: Distinguish Rules from Facts in Knowledge Graphs

The article explains that TBox (Terminological Box) defines abstract class and property axioms without concrete instances, while ABox (Assertional Box) records specific individual facts, showing how their interplay enables OWL reasoning, illustrated with database schema analogies, logical examples, and a practical engineering checklist.

ABoxKnowledge GraphOWL
0 likes · 6 min read
Master TBox vs ABox: Distinguish Rules from Facts in Knowledge Graphs
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 13, 2026 · Industry Insights

Why Industrial Ontology Stumbles: From Academic Perfection to Real-World Roots

The article examines how ontology, once hailed as the key to bridging raw data and complex business logic in digital transformation, often fails in industrial settings because academic standards clash with dynamic realities, prompting a shift toward lightweight, iterative semantic models focused on objects, connections, and actions.

AI IntegrationDigital transformationIndustrial AI
0 likes · 14 min read
Why Industrial Ontology Stumbles: From Academic Perfection to Real-World Roots
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 13, 2026 · Artificial Intelligence

OWL Ontology: From Academic Ivory‑Tower Toy to Engineering Burden

The article analyzes why OWL, designed for logical completeness and reasoning in the Semantic Web, becomes a performance and complexity burden in industrial knowledge‑graph projects, detailing which features are academically valuable and which turn into engineering traps, and offering practical usage guidelines.

Description LogicKnowledge GraphOWL
0 likes · 13 min read
OWL Ontology: From Academic Ivory‑Tower Toy to Engineering Burden
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 8, 2026 · Artificial Intelligence

From Controlled Vocabularies to Ontologies: Tracing the Evolution of Knowledge Representation

The article examines the historical progression from simple controlled vocabularies through taxonomies and thesauri to formal ontologies and knowledge graphs, highlighting how each stage adds semantic depth, formal constraints, and machine‑readable logic for richer knowledge modeling and inference.

Controlled VocabularyKnowledge GraphOWL
0 likes · 14 min read
From Controlled Vocabularies to Ontologies: Tracing the Evolution of Knowledge Representation
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 6, 2026 · Databases

Ontology vs Graph Inference Engine: How the Wrong Choice Can Render Your Knowledge Graph Useless

Choosing between OWL‑based ontologies and graph‑database inference engines fundamentally affects knowledge‑graph design: ontologies provide formal logical consistency and open‑world reasoning, while graph inference offers fast, flexible queries, with each suited to different constraints, scalability, and maintenance scenarios.

Graph DatabaseKnowledge GraphNeo4j
0 likes · 9 min read
Ontology vs Graph Inference Engine: How the Wrong Choice Can Render Your Knowledge Graph Useless
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Jun 7, 2026 · Artificial Intelligence

OWL vs OPL: Which Ontology Modeling Approach Fits Complex Systems?

The article compares OWL’s classification‑centric, internally‑focused ontology modeling with OPL’s relationship‑centric, system‑oriented approach, examining their philosophical bases, handling of new concepts and feature changes, maintenance costs, and suitability for static knowledge bases versus dynamic, evolving complex systems.

OPLOWLknowledge representation
0 likes · 9 min read
OWL vs OPL: Which Ontology Modeling Approach Fits Complex Systems?
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 GraphOWLRDF
0 likes · 18 min read
How to Build Your First Business Ontology for AI Agents – A Step‑by‑Step Guide
xkx's Tech General Store
xkx's Tech General Store
Sep 4, 2025 · Artificial Intelligence

First Hands‑On with Camel AI‑OWL: Exploring a Multi‑Agent Framework

The article walks through installing the open‑source Camel AI‑OWL agent, explains its multi‑agent architecture, runs two practical test cases—retrieving GitHub statistics and generating a physics animation—and highlights strengths, UI shortcomings, tool‑registration issues, and the project's recent inactivity.

AI agentsCamel AIOWL
0 likes · 10 min read
First Hands‑On with Camel AI‑OWL: Exploring a Multi‑Agent Framework