Abstract
The Web is overflowing with unstructured content, ranging from scientific papers, enterprise documents to social media posts. Unlocking the knowledge hidden in these sources is critical for next-generation Web intelligence, enabling semantic search, advanced reasoning, and deep collaboration with Large Language Models (LLMs). This tutorial presents a comprehensive overview of methods for transforming unstructured Web content into structured Knowledge Graphs (KGs), addressing key challenges in information extraction across multiple dimensions, including entities and relations, events, spatio-temporal indices, visual layouts, and metadata. We then discuss methods for constructing and curating high-quality, multi-perspective KGs at scale. Participants will gain a systematic understanding of state-of-the-art methods, including recent advances in document analysis, document-to-KG approaches, and hybrid systems combining LLMs with structured knowledge, such as LLM-driven knowledge graph construction from unstructured documents, RAG over enterprise knowledge bases, KG-augmented LLMs for grounded reasoning, and neuro-symbolic reasoning pipelines. We will cover the paradigm shift of knowledge graph construction from supervised deep learning models to LLM-assisted knowledge engineering while highlighting open challenges such as scalability, factual consistency, and evaluation. We will demonstrate KG's transformative potential through practical applications in web search, question answering, casual reasoning and scientific discovery.