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Semantic Web

22 articles · Page 1 of 1
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.

OWLSemantic Webbusiness rules
0 likes · 25 min read
Can LLMs Auto-Build Ontologies? AI Finds Candidates, Business Defines Reality
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 AgentOWLOntology
0 likes · 33 min read
What Is an Ontology? 9 Questions to Understand Ontologies & Their Role in AI Agents
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.

ABoxOWLOntology
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 · 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 LogicOWLOntology
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 VocabularyOWLOntology
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.

Neo4jOWLOntology
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
Aug 4, 2026 · Artificial Intelligence

Why Teams Are Shifting From Open‑Source Ontology Tools to Custom Solutions

The article analyzes why open‑source ontology tools like Protégé and WebProtégé, once standard for semantic modeling, fall short in large‑scale, collaborative, and compliance‑heavy enterprise knowledge‑graph projects, prompting many organizations to build their own ontology platforms.

OntologyProtégéSemantic Web
0 likes · 11 min read
Why Teams Are Shifting From Open‑Source Ontology Tools to Custom Solutions
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Jun 7, 2026 · Industry Insights

How Palantir Transforms Knowledge Representation into an Enterprise Operating System

The article analyzes Palantir's shift from traditional OWL knowledge representation to a dynamic, secure, and AI‑enabled enterprise operating system, detailing philosophical, architectural, capability, security, AI, and business layers, and highlighting concrete upgrades and real‑world examples.

AIDigital TwinEnterprise OS
0 likes · 12 min read
How Palantir Transforms Knowledge Representation into an Enterprise Operating System
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.

OPLOWLOntology
0 likes · 9 min read
OWL vs OPL: Which Ontology Modeling Approach Fits Complex Systems?
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.

Evaluation MethodsKnowledge EngineeringSemantic Web
0 likes · 20 min read
How to Evaluate Ontology Quality: Metrics, Methods, and Tools
Laravel Tech Community
Laravel Tech Community
Feb 21, 2023 · Blockchain

Key Features and Advantages of Web 3.0

This article explains the main characteristics of Web 3.0, including its decentralized architecture, blockchain foundations, semantic web, AI integration, DeFi, ubiquitous access, and 3‑D graphics, highlighting how these innovations differentiate it from earlier web generations and improve user privacy and experience.

Artificial IntelligenceDeFiSemantic Web
0 likes · 8 min read
Key Features and Advantages of Web 3.0
DataFunTalk
DataFunTalk
May 12, 2022 · Artificial Intelligence

Construction and Application of Power Industry Knowledge Graphs

This article introduces the AI Institute of China Electric Power Research Institute, outlines the background, core concepts, and development of power sector knowledge engineering, details knowledge representation, ontology construction, and graph building methods, and discusses practical applications and future challenges of power domain knowledge graphs.

Artificial IntelligenceSemantic Webdomain ontology
0 likes · 29 min read
Construction and Application of Power Industry Knowledge Graphs
DataFunSummit
DataFunSummit
Dec 8, 2021 · Artificial Intelligence

Knowledge Graph Forum at DataFunCon – Speakers, Topics, and Registration Details

The DataFunCon Knowledge Graph Forum on December 18 gathers leading experts from academia and industry to discuss large‑scale knowledge graph construction, storage, applications, and challenges, offering attendees insights into cutting‑edge AI techniques, graph databases, and practical deployment strategies across multiple domains.

Artificial IntelligenceGraph DatabasesKnowledge Extraction
0 likes · 11 min read
Knowledge Graph Forum at DataFunCon – Speakers, Topics, and Registration Details
DataFunTalk
DataFunTalk
May 21, 2021 · Artificial Intelligence

Knowledge Graph Course Syllabus Overview

This document outlines a comprehensive knowledge graph curriculum covering fundamentals, representation methods, storage solutions, extraction techniques, reasoning, fusion, question answering, graph algorithms, and emerging research directions across nine detailed chapters.

AI EducationGraph DatabasesSemantic Web
0 likes · 4 min read
Knowledge Graph Course Syllabus Overview
DataFunTalk
DataFunTalk
Aug 8, 2020 · Artificial Intelligence

Knowledge Graph Construction and Applications in Alibaba B2B E‑commerce

This article explains how Alibaba B2B leverages knowledge‑graph technology—from its historical roots in knowledge engineering and expert systems to modern semantic‑web models, extraction pipelines, reasoning methods, storage solutions, and representation learning—to improve search, recommendation, and scene‑based procurement incentives in e‑commerce platforms.

AlibabaSemantic Webentity extraction
0 likes · 31 min read
Knowledge Graph Construction and Applications in Alibaba B2B E‑commerce
TAL Education Technology
TAL Education Technology
Jul 23, 2020 · Artificial Intelligence

Comprehensive Overview of Knowledge Graphs: Construction, Storage, and Applications in Recommendation Systems

This article provides a detailed introduction to knowledge graphs, covering their definition, why they are needed, the four basic triple types, construction pipelines (including data sources, crowdsourced vs automated methods, and schema versus data layers), storage and query techniques using graph and relational databases, and their practical applications such as enhancing precision, diversity, and explainability in recommendation systems through models like DKN, RippleNet, and graph neural networks.

AISemantic Webentity linking
0 likes · 15 min read
Comprehensive Overview of Knowledge Graphs: Construction, Storage, and Applications in Recommendation Systems
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 EngineeringSemantic Web
0 likes · 30 min read
The Second Half of Knowledge Graphs: Opportunities and Challenges
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 28, 2018 · Artificial Intelligence

How to Build a Knowledge Graph from Scratch: Bottom‑Up Techniques Explained

This article explains the fundamentals of knowledge graphs, compares top‑down and bottom‑up construction methods, describes data types, storage options, logical and technical architectures, and walks through the iterative steps of information extraction, knowledge fusion, processing, updating, and real‑world applications.

Knowledge FusionOntologySemantic Web
0 likes · 18 min read
How to Build a Knowledge Graph from Scratch: Bottom‑Up Techniques Explained
Ctrip Technology
Ctrip Technology
Jul 29, 2016 · Artificial Intelligence

Reasoning Techniques in Knowledge Graphs and Their Application to a High‑School Exam Robot

The talk reviews the history and concepts of knowledge graphs, explains logical and statistical reasoning methods—including rule‑based and representation‑learning approaches—and demonstrates how these techniques can be applied to build an intelligent robot that assists students in solving high‑school exam problems.

Semantic Webexam robotknowledge graph
0 likes · 5 min read
Reasoning Techniques in Knowledge Graphs and Their Application to a High‑School Exam Robot