BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Graphwise - ECPv6.17.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://gws-sso-test.graphwise.ai X-WR-CALDESC:Events for Graphwise REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/Vienna BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20241027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20261025T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Europe/Vienna:20250716T150000 DTEND;TZID=Europe/Vienna:20250716T160000 DTSTAMP:20260225T105804Z CREATED:20250626T110010Z LAST-MODIFIED:20260225T105804Z UID:239612-1752678000-1752681600@gws-sso-test.graphwise.ai SUMMARY:Graph RAG - Why Your RAG Needs a Graph DESCRIPTION:On-Demand WEBINAR \n \n \n \n \n \n Graph RAG – Why Your RAG Needs a Graph\n \n \n \n \n \n Unifying Structured and Unstructured Data in Graph to Give Context \n \n \n \n \n \n \n \n \n \n July 16\, 2025 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Susan Härtig \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n Fill out the form to watch this webinar\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Chunk Is Dead\, Long Live the Graph RAG! \nAs AI applications increasingly rely on large language models (LLMs) for search\, summarization\, and decision support\, one major limitation persists: traditional Retrieval-Augmented Generation (RAG) systems often treat knowledge as disconnected chunks of text\, missing the deeper context behind relationships. But what if we could connect the dots more intelligently? \nSmarter Retrieval for Smarter AI \nIn this on-demand webinar\, we introduce a graph-based approach to RAG that leverages the power of knowledge graphs to represent meaning more holistically. By structuring information as a web of semantic relationships\, this method enables LLMs to retrieve and generate content that is not only more accurate but also more contextually aware. \nWatch Márcia R. Ferreira and Astrid Krickl\, Data and Knowledge Engineers at Graphwise\, as they walk through the Graphwise Graph RAG framework—an innovative architecture that enhances semantic retrieval using graph-connected data. Through real-world use cases and the Graphwise platform\, they’ll demonstrate how this approach bridges gaps left by chunk-based methods and discuss practical implementation challenges often overlooked in AI pipelines. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Discover these key takeaways:\n \n \n \n \n \n \n \n \n \n Knowledge Graphs are Everywhere: Fortune 500 companies are using them\, are you? \n \n \n \n \n \n \n \n \n \n \n \n Graph vs Knowledge Graph: Learn what differentiates a Knowledge Graph from a regular Graph. \n \n \n \n \n \n \n \n \n \n \n \n Enterprise Structure: Find out the secret recipe for Knowledge Graphs and LLMs in an enterprise. \n \n \n \n \n \n \n \n \n \n \n \n Vector and Graph: What are the differences and benefits of vector-based RAG and a graph-based RAG approach? \n \n \n \n \n \n \n \n \n \n \n \n And much more!\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Whether you’re designing search systems\, building intelligent assistants\, or architecting AI solutions\, this webinar will give you a fresh perspective on integrating structured knowledge into modern workflows.\n \n \n \n \n \n \n \n \n \n \n \n \n \n WATCH NOW\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n Speaker\n \n \n \n \n \n \n \n \n \n \n \n \n Susan Härtig URL:https://gws-sso-test.graphwise.ai/event/webinar-graph-rag-why-your-rag-needs-a-graph/ LOCATION:Online CATEGORIES:On-demand Webinar ATTACH;FMTTYPE=image/png:https://gws-sso-test.graphwise.ai/wp-content/uploads/2025/07/WebFeatImg_WhyGraphNeedRag_16172025.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Vienna:20250716T150000 DTEND;TZID=Europe/Vienna:20250716T160000 DTSTAMP:20250626T110010Z CREATED:20250626T110010Z LAST-MODIFIED:20250626T110010Z UID:252307-1752678000-1752681600@gws-sso-test.graphwise.ai SUMMARY:Graph RAG - Why Your RAG Needs a Graph DESCRIPTION:On-Demand WEBINAR \n \n \n \n \n \n Graph RAG – Why Your RAG Needs a Graph\n \n \n \n \n \n Unifying Structured and Unstructured Data in Graph to Give Context \n \n \n \n \n \n \n \n \n \n July 16\, 2025 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Susan Härtig \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n Fill out the form to watch this webinar\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Chunk Is Dead\, Long Live the Graph RAG! \nAs AI applications increasingly rely on large language models (LLMs) for search\, summarization\, and decision support\, one major limitation persists: traditional Retrieval-Augmented Generation (RAG) systems often treat knowledge as disconnected chunks of text\, missing the deeper context behind relationships. But what if we could connect the dots more intelligently? \nSmarter Retrieval for Smarter AI \nIn this on-demand webinar\, we introduce a graph-based approach to RAG that leverages the power of knowledge graphs to represent meaning more holistically. By structuring information as a web of semantic relationships\, this method enables LLMs to retrieve and generate content that is not only more accurate but also more contextually aware. \nWatch Márcia R. Ferreira and Astrid Krickl\, Data and Knowledge Engineers at Graphwise\, as they walk through the Graphwise Graph RAG framework—an innovative architecture that enhances semantic retrieval using graph-connected data. Through real-world use cases and the Graphwise platform\, they’ll demonstrate how this approach bridges gaps left by chunk-based methods and discuss practical implementation challenges often overlooked in AI pipelines. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Discover these key takeaways:\n \n \n \n \n \n \n \n \n \n Knowledge Graphs are Everywhere: Fortune 500 companies are using them\, are you? \n \n \n \n \n \n \n \n \n \n \n \n Graph vs Knowledge Graph: Learn what differentiates a Knowledge Graph from a regular Graph. \n \n \n \n \n \n \n \n \n \n \n \n Enterprise Structure: Find out the secret recipe for Knowledge Graphs and LLMs in an enterprise. \n \n \n \n \n \n \n \n \n \n \n \n Vector and Graph: What are the differences and benefits of vector-based RAG and a graph-based RAG approach? \n \n \n \n \n \n \n \n \n \n \n \n And much more!\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Whether you’re designing search systems\, building intelligent assistants\, or architecting AI solutions\, this webinar will give you a fresh perspective on integrating structured knowledge into modern workflows.\n \n \n \n \n \n \n \n \n \n \n \n \n \n WATCH NOW\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n Speaker\n \n \n \n \n \n \n \n \n \n \n \n \n Susan Härtig URL:https://gws-sso-test.graphwise.ai/event/webinar-graph-rag-why-your-rag-needs-a-graph-2/ LOCATION:Online CATEGORIES:On-demand Webinar ATTACH;FMTTYPE=image/png:https://gws-sso-test.graphwise.ai/wp-content/uploads/2025/07/WebFeatImg_WhyGraphNeedRag_16172025.png END:VEVENT END:VCALENDAR