The article explains how ontologies serve as a formal conceptual register for AI, covering description logic, the TBox/ABox distinction, ontology alignment, real‑world applications like Google Knowledge Graph and Siri, and practical advice for newcomers.
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.
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.
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.
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.
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.
This article explains how OWL extends RDF to model domain ontologies, outlines its core concepts, sub‑languages, key reasoning features, tooling, typical applications such as knowledge graphs and AI, and discusses both its advantages and practical challenges.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
