0 views collected around this technical thread.
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 International Semantic Web Conference (ISWC) 2022 will be held in Hangzhou, China from October 23‑27, featuring a call for papers, detailed important dates, and a full list of organizing committee members across research, resource, in‑use, workshop, industry, and doctoral tracks.
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.
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.