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Semantic Modeling

37 articles · Page 1 of 1
Data Bricklaying Diary
Data Bricklaying Diary
Sep 17, 2026 · Artificial Intelligence

Why Enterprises Need Business Ontologies Despite LLMs' World Knowledge

This article explains why large language models' general world knowledge cannot replace enterprise business ontologies, which provide versioned, traceable semantic models for object identity, institutional definitions, state validity, and responsibility boundaries within a specific organization.

Enterprise Knowledge ManagementLarge Language ModelsSemantic Modeling
0 likes · 19 min read
Why Enterprises Need Business Ontologies Despite LLMs' World Knowledge
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 11, 2026 · Backend Development

Why 90% of ERP Implementations Fail: The Hidden Semantic Gap OntoL Ontology Fixes

This article explains why ERP systems with identical workflows produce vastly different outcomes, revealing that business semantics and rules—not processes—determine success, and demonstrates how OntoL's ontology modeling with TBox/ABox layers and inference engines standardizes constraints, versioning, and traceability across manufacturing scenarios.

BOMECNERP
0 likes · 25 min read
Why 90% of ERP Implementations Fail: The Hidden Semantic Gap OntoL Ontology Fixes
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Sep 2, 2026 · Artificial Intelligence

Why Python, Java and BI Tools Fail at Enterprise AI—and How OntoL’s Living Semantic Base Solves It

The article explains that Python/Java focus on execution, BI on measurement, while ontology provides a living semantic foundation that unifies meaning and reasoning across systems, enabling AI to understand context, infer hidden knowledge, and turn scattered business expertise into actionable assets.

Enterprise AIOntoLOpen World Assumption
0 likes · 7 min read
Why Python, Java and BI Tools Fail at Enterprise AI—and How OntoL’s Living Semantic Base Solves It
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 29, 2026 · Industry Insights

How to Lightly Deploy Ontology in Enterprises: Three Practical Scenarios

The article diagnoses three core data‑knowledge pain points in enterprise digital transformation, proposes five ontology‑driven principles and a three‑layer mapping architecture, and illustrates lightweight, iterative rollout through three concrete scenarios covering design‑as‑modeling, enterprise‑wide semantic governance, and executable ontology for AI agents.

AI IntegrationEnterprise DataKnowledge Governance
0 likes · 16 min read
How to Lightly Deploy Ontology in Enterprises: Three Practical Scenarios
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 24, 2026 · Industry Insights

How Ontology Platforms Bridge Data, Business, and AI as a Semantic Foundation

The article explains what an ontology platform is, why enterprises need it to close the semantic gap between data and business, evaluates core technical capabilities, reviews the market landscape, and offers a step‑by‑step selection guide for building a robust Data+AI semantic infrastructure.

Data+AILLM IntegrationSemantic Modeling
0 likes · 17 min read
How Ontology Platforms Bridge Data, Business, and AI as a Semantic Foundation
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 readinessOWLSHACL
0 likes · 20 min read
Ontology Isn't Esoteric: You Already Process Its Raw Materials Daily
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.

Semantic Modelingcompetency questionsknowledge representation
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 20, 2026 · Artificial Intelligence

How Ontology Drives AI Transformation: Lessons from Palantir’s Turnaround

The article explains why Ontology—structured business semantics, three‑layer models, and executable constraints—has become a mandatory foundation for enterprise AI, detailing its role in reducing LLM hallucinations, enabling safe AI agents, showcasing benchmark case studies, and providing a step‑by‑step roadmap for building, governing, and scaling Ontology in practice.

AIAI AgentEnterprise Knowledge Graph
0 likes · 31 min read
How Ontology Drives AI Transformation: Lessons from Palantir’s Turnaround
Architect
Architect
Aug 20, 2026 · Industry Insights

What Real Problem Does Ontology Solve in Enterprise Knowledge Bases?

The article examines why ontology is essential for enterprise knowledge bases, showing how it resolves ambiguities that RAG, knowledge graphs, and agents cannot handle alone, and outlines a four‑layer architecture that ensures stable IDs, relationship semantics, fact lifecycle, and safe action execution.

LLMRAGSemantic Modeling
0 likes · 18 min read
What Real Problem Does Ontology Solve in Enterprise Knowledge Bases?
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 20, 2026 · Artificial Intelligence

Full Ontology Implementation Process: From Scenario Selection to Engineering Deployment

The article outlines a four‑step methodology for deploying ontologies in enterprise settings—starting with selecting a clear business scenario, analyzing requirements through rule, risk, validation and decision dimensions, mapping factors to data sources and interfaces, and establishing continuous validation, monitoring, and versioned iteration.

Business Knowledge ManagementData IntegrationRule Engine
0 likes · 8 min read
Full Ontology Implementation Process: From Scenario Selection to Engineering Deployment
Data Bricklaying Diary
Data Bricklaying Diary
Aug 14, 2026 · Artificial Intelligence

Action ≠ API: Designing Business Execution Contracts for Enterprise Agents

This article explains why ontology Actions are not mere API wrappers but business execution contracts that bind semantics, decisions, evidence, permissions, idempotency, compensation, and audit receipts, detailing four Action forms, five boundary categories, common misconceptions, and a six-step implementation approach for enterprise agents.

MCPSemantic Modelingaction contract
0 likes · 20 min read
Action ≠ API: Designing Business Execution Contracts for Enterprise Agents
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 TransformationOWL
0 likes · 14 min read
Why Industrial Ontology Stumbles: From Academic Perfection to Real-World Roots
Woodpecker Software Testing
Woodpecker Software Testing
Aug 11, 2026 · Artificial Intelligence

From AI Hype to Essential Testing: The 2026 AI‑Augmented Testing Landscape

By 2026, AI‑augmented testing has become a core quality gate in leading tech firms, with 87% of top software companies deploying at least two AI capabilities such as semantic‑aware test modeling, predictive defect detection, and trustworthy AI governance, as illustrated by real‑world cases from a bank, an e‑commerce platform, and an automotive OEM.

AI governanceAI testingCI/CD
0 likes · 7 min read
From AI Hype to Essential Testing: The 2026 AI‑Augmented Testing Landscape
Architect
Architect
Aug 8, 2026 · Databases

Ontology as the Semantic Control Plane for Agent Fact Systems

The article explains how an ontology—defining objects, relationships, constraints, and inferable boundaries within a domain—serves as a semantic control plane between the fact and action layers of an agent‑driven system, ensuring consistent interpretation, validation, and lifecycle management of business facts.

Agent SystemsGraph DatabasesRDF
0 likes · 18 min read
Ontology as the Semantic Control Plane for Agent Fact Systems
Data Bricklaying Diary
Data Bricklaying Diary
Aug 1, 2026 · Big Data

AI Data Engineering: The Data Supply System for the Agent Era

This article defines AI Data Engineering as a data supply system for large models and agents, extending traditional data engineering with semantic modeling, RAG, controlled data services, permission governance, and feedback loops to make data understandable, retrievable, callable, and auditable for reliable enterprise AI deployment.

AI Data EngineeringAgentsData Services
0 likes · 11 min read
AI Data Engineering: The Data Supply System for the Agent Era
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Jul 31, 2026 · Industry Insights

Decision‑Centric Scenario Ontology Design: A Six‑Step Methodology for Fast POC Delivery

The article presents a decision‑centric scenario ontology design method that contrasts domain and scenario ontologies, outlines a standardized six‑step process—from scene definition to effect validation—and demonstrates its rapid POC delivery and business impact through an insurance lead‑marketing case study.

Decision SupportEnterprise Digital TransformationPoC
0 likes · 15 min read
Decision‑Centric Scenario Ontology Design: A Six‑Step Methodology for Fast POC Delivery
Data Bricklaying Diary
Data Bricklaying Diary
Jul 16, 2026 · Big Data

Building Business Semantic Models for Ontology-Driven Data Governance

The article explains how to transform business models into machine-understandable business semantic models for ontology-driven data governance, covering eight key content types including concepts, relationships, states, processes, rules, metrics, evidence, and action contracts, plus transformation steps, granularity control, and deliverables such as semantic glossaries and relationship models.

AI AgentOntology-Driven Data GovernanceSemantic Assets
0 likes · 16 min read
Building Business Semantic Models for Ontology-Driven Data Governance
Data Bricklaying Diary
Data Bricklaying Diary
Jul 15, 2026 · Big Data

Ontology-Driven Data Governance: Business Modeling from Research to Symbolic Models

This article explains how ontology-driven data governance requires thorough business research and modeling before database design, detailing a six-element framework (objects, processes, states, rules, data sources, Agent opportunities) and visual modeling techniques like OPM and BPMN to create stable semantic foundations for data mapping and AI agents.

AI AgentsBPMNOPM
0 likes · 12 min read
Ontology-Driven Data Governance: Business Modeling from Research to Symbolic Models
Data Bricklaying Diary
Data Bricklaying Diary
Jul 14, 2026 · Big Data

Ontology-Driven Data Governance: Start with Business Scenarios, Not Table Fields

This article explains why ontology-driven data governance should begin by selecting high-value business scenarios rather than analyzing existing table fields, detailing five selection criteria, a pain-point mapping method, and a value-opportunity formula with concrete examples like equipment monitoring and legal case review.

AI AgentData QualityOntology-Driven Data Governance
0 likes · 11 min read
Ontology-Driven Data Governance: Start with Business Scenarios, Not Table Fields
Data Bricklaying Diary
Data Bricklaying Diary
Jul 13, 2026 · Big Data

Building Ontology-Driven Data Governance: Objects, Processes, Roles & Operating Mechanisms

This article presents a comprehensive framework for ontology-driven data governance that extends beyond semantic modeling into a continuous operating system covering three governance object categories, a six-step scenario-based process, eight cross-functional roles, seven operating mechanisms, and integration with existing data governance capabilities to support AI-ready semantic assets and Agent actionability.

AI Data EngineeringAgent SystemsOntology-Driven Data Governance
0 likes · 16 min read
Building Ontology-Driven Data Governance: Objects, Processes, Roles & Operating Mechanisms
Data Bricklaying Diary
Data Bricklaying Diary
Jul 9, 2026 · Artificial Intelligence

Industrial AI Can't Just Predict: Ontology Platforms Enable Semantic Safety Loops

This article argues that industrial AI must go beyond predictive models by integrating ontology platforms that embed equipment, process, and safety semantics, using an activated carbon box temperature warning case to show how semantic context turns raw alerts into actionable, governed safety decisions.

OPMSemantic Modelingactivated carbon box
0 likes · 17 min read
Industrial AI Can't Just Predict: Ontology Platforms Enable Semantic Safety Loops
Data Bricklaying Diary
Data Bricklaying Diary
Jul 5, 2026 · Industry Insights

Piloting a Judicial Semantic Platform: 6 Steps from Semantic Model to Controlled AI Agent

This article outlines a six-step methodology for piloting a judicial semantic platform: freeze a runnable semantic model using OPM, integrate minimum necessary data, run case object views, build a business-oriented front-end, integrate a controlled AI Agent with audit logging, and validate via a five-dimensional acceptance loop before scaling.

AI AgentOPMSemantic Modeling
0 likes · 16 min read
Piloting a Judicial Semantic Platform: 6 Steps from Semantic Model to Controlled AI Agent
ThinkingAgent
ThinkingAgent
Jun 14, 2026 · Artificial Intelligence

Ontology: The Overlooked Knowledge Infrastructure Driving AI Understanding

The article explains how ontology—a 2,500‑year‑old philosophical concept—provides the structured knowledge backbone that large language models lack, detailing its definition, differences from databases and knowledge graphs, its role in reducing hallucinations, defining knowledge boundaries, enabling reasoning, and four practical AI application scenarios.

AI AgentsLarge Language ModelsRAG
0 likes · 17 min read
Ontology: The Overlooked Knowledge Infrastructure Driving AI Understanding
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Jun 10, 2026 · Databases

Ontology Intelligence & Decision Modeling: From OntoGraph DB to OntoOS (WorldOS)

The article analyzes why traditional graph databases fall short for ontology‑driven intelligent applications, compares graph versus ontology databases, introduces OntoGraph as a state‑layer ontology DB, explains Property Runtime's computed‑property engine and lineage tracking, and shows how OntoFlow and OntoOS together enable end‑to‑end decision modeling and sandbox simulation.

Semantic Modelingdecision enginegraph database
0 likes · 13 min read
Ontology Intelligence & Decision Modeling: From OntoGraph DB to OntoOS (WorldOS)
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
May 25, 2026 · Artificial Intelligence

From Filing Records to Building Dictionaries: The Paradigm Shift in Data Governance for the AI Era

The article explains how traditional data governance, which merely cleans and organizes files, fails to meet AI’s need for semantic understanding, and argues that adopting ontology‑based governance—building a “cognitive dictionary” of entities, relationships, and rules—enables machines to truly comprehend and reason over enterprise data.

AISemantic Modelingdata governance
0 likes · 13 min read
From Filing Records to Building Dictionaries: The Paradigm Shift in Data Governance for the AI Era
DataFunSummit
DataFunSummit
Apr 27, 2026 · Artificial Intelligence

How Tencent Games Leverages AI to Turn Data Governance into a Service

Tencent Games’ data governance team details an AI‑driven, end‑to‑end semantic framework that shifts traditional rule‑based data management to a service‑oriented model, cutting storage waste by 30 %, halving development time, and boosting asset recommendation accuracy to 95 % across its global gaming platform.

AIGaming IndustryResource Optimization
0 likes · 19 min read
How Tencent Games Leverages AI to Turn Data Governance into a Service
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Apr 21, 2026 · Artificial Intelligence

Why Ontology Engineering Is the Secret Sauce Behind Scalable AI Agents

The article analyzes how Palantir's ontology engineering unifies semantic and operational layers to provide unified business views, executable actions, governance, and evolution capabilities that empower AI agents with reliable context, closed‑loop control, scenario simulation, and easier deployment across enterprise environments.

AI AgentsEnterprise AIPalantir
0 likes · 25 min read
Why Ontology Engineering Is the Secret Sauce Behind Scalable AI Agents
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.

OWLRDFSemantic Modeling
0 likes · 18 min read
How to Build Your First Business Ontology for AI Agents – A Step‑by‑Step Guide
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Dec 1, 2025 · Artificial Intelligence

Why Enterprises Must Rethink Ontology to Bridge the Last Mile of LLM Deployment

The article explains how a well‑designed enterprise ontology—an explicit business‑level semantic and constraint model—turns large language models from risky, hallucination‑prone tools into safe, auditable AI agents that can act across systems, enforce policies, and become a lasting digital asset.

AI governanceEnterprise AILLM Integration
0 likes · 15 min read
Why Enterprises Must Rethink Ontology to Bridge the Last Mile of LLM Deployment
DataFunSummit
DataFunSummit
Feb 5, 2024 · Artificial Intelligence

Ant Group's Knowledge Graph: Overview, Construction, Applications, and Integration with Large Models

Ant Group shares its comprehensive knowledge graph initiatives, detailing the fundamentals, construction pipeline, fusion techniques, cognitive representations, diverse business applications, and the emerging synergy between knowledge graphs and large language models, illustrating how graph-based AI enhances accuracy, interpretability, and downstream services.

Artificial IntelligenceData IntegrationGraph Fusion
0 likes · 14 min read
Ant Group's Knowledge Graph: Overview, Construction, Applications, and Integration with Large Models
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 9, 2023 · Artificial Intelligence

Designing Scalable Knowledge Graph Schemas: From Structure to Semantic Modeling

This guide presents a comprehensive methodology for building knowledge graph schemas that decouple structural representation from semantic meaning, covering schema design, attribute semantic standardization, concept modeling, multi‑relational and hypergraph techniques, and practical steps for implementation across complex business domains.

AISemantic Modelingdata modeling
0 likes · 54 min read
Designing Scalable Knowledge Graph Schemas: From Structure to Semantic Modeling
DataFunSummit
DataFunSummit
Feb 3, 2023 · Artificial Intelligence

Interactive BERT for Relevance in Health E‑commerce Search

This article presents an in‑depth exploration of an interactive BERT‑based relevance model for health e‑commerce search, detailing the business context, query and product feature extraction, domain‑specific sample generation, model architecture enhancements, offline and online performance gains, and practical deployment through knowledge distillation.

AIBERTSemantic Modeling
0 likes · 14 min read
Interactive BERT for Relevance in Health E‑commerce Search
DataFunTalk
DataFunTalk
Jan 11, 2023 · Artificial Intelligence

Exploring Interactive BERT for Relevance in Health E‑commerce Search

This article presents a comprehensive overview of Alibaba Health's interactive BERT approach for improving relevance in health e‑commerce search, covering business background, model design, domain‑specific data construction, knowledge‑distilled twin‑tower deployment, experimental results, and a detailed Q&A session.

AIBERTSemantic Modeling
0 likes · 14 min read
Exploring Interactive BERT for Relevance in Health E‑commerce Search
DataFunTalk
DataFunTalk
Jul 7, 2021 · Big Data

Solving Data Island Challenges and Enabling Advanced OLAP Analysis on Heterogeneous Big Data Platforms – Kyligence Solution Overview

This article explains the growing analytical demands in the big‑data era, the limitations of traditional OLAP, and how Kyligence’s distributed OLAP engine addresses data‑island issues, multi‑dimensional and many‑to‑many analysis, unified security, and performance optimization with MDX on Spark, delivering a seamless Excel‑like experience.

AnalyticsData IntegrationDistributed Computing
0 likes · 9 min read
Solving Data Island Challenges and Enabling Advanced OLAP Analysis on Heterogeneous Big Data Platforms – Kyligence Solution Overview
JD Cloud Developers
JD Cloud Developers
Dec 28, 2020 · Artificial Intelligence

Top Tech Highlights: C++20 Release, AI Cloud Surge, MuZero Breakthrough & More

Last week’s developer newsletter spotlights the official C++20 standard launch, a surprising over‑100% growth in China’s AI cloud market, Nature’s discovery of universal facial expressions, DeepMind’s rule‑free MuZero game mastery, JD Cloud’s 99.995% SLA upgrade, mimik‑IBM edge‑computing partnership, a Fudan microservice fault‑localization thesis, and an AAAI paper on semantic relation modeling.

Artificial IntelligenceC++20Semantic Modeling
0 likes · 9 min read
Top Tech Highlights: C++20 Release, AI Cloud Surge, MuZero Breakthrough & More