---
title: "Slides: GraphRAG: The Marriage of Knowledge Graphs and RAG: Emil Eifrem"
category: "slides"
video_id: "knDDGYHnnSI"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: GraphRAG: The Marriage of Knowledge Graphs and RAG: Emil Eifrem

## Source Video
[GraphRAG: The Marriage of Knowledge Graphs and RAG: Emil Eifrem](https://www.youtube.com/watch?v=knDDGYHnnSI)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/knDDGYHnnSI/slide-001.jpg]]

OCR text:

> INNOVATION SPONSOR
> aws
> PLATINUM SPONSORS
> MongoDB.
> Google Cloud
> neo4j

![[assets/slides/knDDGYHnnSI/slide-002.jpg]]

OCR text:

> neo4j
> Graph Database & Analytics

![[assets/slides/knDDGYHnnSI/slide-003.jpg]]

OCR text:

> The Evolution of... Web Search
> Serious Sports Fans On)y_$1 000.000 in Cash and Pozes!
> For serious sports fans only! Play Fantasy Football!
> Sa
> si "it's amazing where
> Go Get It will get you.
> Find: [ Go Get tt j
> Enhance your search.
> New Scarch » TopNews « Sites by Subject «Top 5% Sites - City Guide - Pictures & Sounds
> PeopleFind - Point Review - Road Maps - Software - About Lycos - Club Lycos » Help
> Add Yout Site to Lycos
> Copynght € 1996 Lycos™, Inc. All Rights Reserved.
> : Lycos is a trademark of Camegic Mellon University.

![[assets/slides/knDDGYHnnSI/slide-004.jpg]]

OCR text:

> The Evolution of... Web Search
> . Full text Era: 1994 - 2000
> |
> nae 4s Netscape
> | TT cela) re lve s 4 AOL
> 7 e we” Rie
> - ma llelcokxe aS STOO UMMC LS

![[assets/slides/knDDGYHnnSI/slide-005.jpg]]

OCR text:

> AI Engineer
> World's Fair

![[assets/slides/knDDGYHnnSI/slide-006.jpg]]

OCR text:

> Google Launches World’s Largest Search Engine & Subscnde
> : Google Now Enables internet Users to Search More Than 1 Billion URLs, Providing Quick and Easy Access to 560 Million Full-Text
> Indexed Web Pages and 500 Million Partlaity indexed URLs
> MOUNTAIN VIEW, Calif. - June 26, 2000 - Google Inc., one of the fastest growing search engines on the web, today announced it has
> released the largest search engine on the Intemet. Google's new index, comprising more than 1 dilbon URLs. offers users the wed's most
> comprehensive collection of websites, which can be easily searched with Google's fast and highly relevant search lechnology. Available now
> at www.goog'e com, Google's portal and destination site customers can also bcense this new index for integration with their own websites.
> we Google is based on a variety of innovative
> as technologies, including sophisticated text matching
> and its advanced, patent-pending technology called
> PageRankim, which ensures that the most important ;
> a results always come up first. ;
> PT os : .
> / WT at ° aws
> ; a Microso
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![[assets/slides/knDDGYHnnSI/slide-007.jpg]]

OCR text:

> TheEvolutionof..WebSearch
> PageRankEra:2000-2012
> AIE
> PageRank
> Google
> Full Text
> YAHOO!
> Netscape
> Search
> Ask
> LYCOS
> AAOL
> erved2024
> Microsoft
> smol
> aws

![[assets/slides/knDDGYHnnSI/slide-008.jpg]]

OCR text:

> AI Engineer
> World's Fair

![[assets/slides/knDDGYHnnSI/slide-009.jpg]]

OCR text:

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![[assets/slides/knDDGYHnnSI/slide-010.jpg]]

OCR text:

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![[assets/slides/knDDGYHnnSI/slide-011.jpg]]

OCR text:

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![[assets/slides/knDDGYHnnSI/slide-012.jpg]]

OCR text:

> What IS GraphRAG?
> ct avacy lanl elon
> Clea ]e)nl ne: (Ci cee aun era mn ere a TeTEREOEE SS
> a Microsoft Ea? awWws

![[assets/slides/knDDGYHnnSI/slide-013.jpg]]

OCR text:

> User
> Your Application

![[assets/slides/knDDGYHnnSI/slide-014.jpg]]

OCR text:

> GraphRAG Retrieval Patterns
> 1. Doa vector search to find an initial set of nodes

![[assets/slides/knDDGYHnnSI/slide-015.jpg]]

OCR text:

> GraphRAG Retrieval Patterns
> Oates ot0 ac Ras bette ee eee SNe ee ee eae te cree ee ae
> |. Do avector search to find an initial set of nodes
> 2. Traverse the graph around those nodes to add context
> 3. (Optional: Rank the results using the graph and pass the
> top-k documents to the LLM)
> | ; )
> Ma Iracesxe) aS OVO UMEEGLU

![[assets/slides/knDDGYHnnSI/slide-016.jpg]]

OCR text:

> AIE
> The Benefits of
> GraphRAG
> Microsoft
> smol ai
> aws

![[assets/slides/knDDGYHnnSI/slide-017.jpg]]

OCR text:

> ‘O) Higher ane Tin)
> X the accuracy of LLM responses
> by 54.2%, an average of 3x.
> Accuracy
> The clearest driver of a aan Sequeda
> GraphRAG adoption amongst s
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![[assets/slides/knDDGYHnnSI/slide-018.jpg]]

OCR text:

> ital
> Accuracy
> GraphRAG: Unlocking LLM
> Ree eld discovery on narrative private data
> GraphRAG adoption amongst Renres dire ‘Si
> users is higher accuracy.
> “By combining LLM-generated
> vate ert knowledge graphs and graph
> Oe w . ,
> SE EA machine learning, GraphRAG
> Beet Fea enables us to answer important
> Pett Ph kept 5
> a er classes of questions that we cannot
> ek .
> une attempt with baseline RAG alone."
> : - BE Microsoft

![[assets/slides/knDDGYHnnSI/slide-019.jpg]]

OCR text:

> Devel t |
> The second reason we hear
> people choose GraphRAG over
> vector-only RAG is easier
> development... once they've
> pushed through the initial
> learning curve.
> 
> " Werdly ease of develapment Jer lack
> thereof! :s aiso ene of the stumbins: blocks!
> How can that be?
> 
> One word Knowledge Graph Construction’
> Ok that’s three. Susneld that tnouenAtuerd
> ae

![[assets/slides/knDDGYHnnSI/slide-020.jpg]]

OCR text:

> EasierDevelopment:Why?
> Natural Language Description:"Apples and oranges are both fruits"
> DATASEMANTICS
> AIE
> EXPLICIT(SYMBOLIC)
> Representation
> erved2023
> Microsoft
> smol
> aws

![[assets/slides/knDDGYHnnSI/slide-021.jpg]]

OCR text:

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![[assets/slides/knDDGYHnnSI/slide-022.jpg]]

OCR text:

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> Customer “i actualy already Pxed a couple of bugs thanks te this!”

![[assets/slides/knDDGYHnnSI/slide-023.jpg]]

OCR text:

> Knowledge Graph Construction
> Typically PDFs or other phe ee Structured data with
> text documents Structured data with short text values
> long-form text
> Bee -_ as
> | a
> an a.
> . . if fai)
> a Microsoft @aU? awWws

![[assets/slides/knDDGYHnnSI/slide-024.jpg]]

OCR text:

> Two Types of Knowledge Graphs
> A graph representation of for
> example the words, paragraphs.
> chunks, documents and the
> relationships between them
> 1 . Pa
> ; = mE lrelxekxe) aS O00 O UMMC LS

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.