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How can we build AI systems that do more than retrieve passages and generate plausible answers? In this tutorial, participants will build ARGO, a reproducible, open-source graph-based system for agentic semantic analysis across heterogeneous content. Starting from real documents and web pages, they will learn how to extract content and structure, identify entities and relations, retrieve relevant evidence, construct a semantic knowledge graph, and orchestrate reasoning through graph-based AI agents. ARGO connects core ideas from Semantic Web research with recent advances in information retrieval, Knowledge Graph engineering, GraphRAG, LLM-based tool calling, and MCP-enabled interoperability. Rather than treating retrieval, graph construction, reasoning, and validation as separate steps, the tutorial presents them as parts of a controllable agentic workflow. Agents decide when to search, when to inspect sources, when to traverse the graph, when to call tools, when to validate intermediate results, and when to generate a final answer. The result is not a simple chatbot, but a semantic AI system whose answers are grounded in source evidence, entities, relations, and explicit reasoning paths.
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