Keynote: The Art of the Possible with Graph Technology Dr. Jim Webber, Chief Scientist, Neo4j Are you drowning in data but lacking in insight? 80% of business leaders say data is critical in decision-making, yet 41% cite a lack of understanding of data because it is too complex or not accessible enough. You’ll learn how companies are using graph technology to leverage the relationships in their connected data to reveal new ways of solving their most pressing business problems and creating new business value for their enterprises. You’ll see real-world Government use cases that include fraud detection, AI/ML, supply chain management, network/IT operations and more.

1. The Art of the Possible
Dr. Jim Webber
Chief Scientist, Neo4j

4. PRECEEDED PRECEEDED PRECEEDED

5. Fight!
Fight!
Fight!
Fight!
Fight!
Fight!

6. ENEMY
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ENEMY
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1066}
ALLY

7. ENEMY
{since: 1776}
REPORTS_TO
FOUGHT_IN
FOUGHT_IN
British Victory

9. British Victory

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14. Legacy approaches
are not optimized to
handle the shift
to connected data

15. Imagine you have to
“normalize” this data
into a relational
database with SQL

16. Previous data technology can’t
tackle today’s challenges

17. The world is changing

18. The world is changing
As things get more connected, data does too

19. The world is changing
Knowledge, not just data, is a competitive advantage

20. Gen AI Fraud Supply Chains Medicine Space Travel

21. Gen AI Fraud Supply Chains Medicine Space Travel
The insights are latent
in your data’s connections

22. Index-Free Adjacency: Fast, Flexible, Scalable
Relational Database
Person Person-Friend Friend
Native Graph Database

23. Graph databases
natively store and
query relationships
Data model purpose-built for connected data
Balance of schema flexibility and structure
Powerful and productive query language

24. Connectedness and Size of Data Set
Response
Time
Relational and
other NoSQL
databases
Native Graph Database
1000x Advantage
5+ hops
3+ degrees
Thousands of connections
0 to 2 hops
0 to 3 degrees
Few connections
Performance Advantage of Graph as # of Joins Increase
From Minutes to Milliseconds

25. Uncovering relationships
in connected data is key to
deeper understanding of
Context
Behavior
Preferences

26. Uncovering relationships
in connected data is driving
game-changing innovation

27. Enabling extraordinary outcomes
Powering applications that are impossible
with other technologies

28. Graph Transactions, Storage &
Querying
Graph Analytics, ML,
& Data Science
Intelligent Operational Systems Better Predictions for Analytics
Neo4j Differentiation
Neo4j Database built for Operational and Analytical Workloads

29. Graph Applications
(System of Record Applications)
Application Stack
Real-time Messaging & Processing
(Clickstream, IOT, CDC etc)
Developer
Intelligent apps
Data Analyst
Query and analyze
Biz Analyst
Visual Analytics
Data Scientist
Algorithms and features
Data engineer
Get clean, useful data
ML Engineer
ML Ops
Data Platform
(Data Platforms-Snowflake, BigQuery,
Databricks)
AI/ML Ops
BI Platforms
Graph Analytics & Analytical Apps
(Real-time applications, visualizations
and algorithms)
Powered by
Market Trends
Modern Data Platform

30. ChatGPT is just ONE Year Old!

31. KGs LLMs
+
BETTER TOGETHER

32. +
Combine explicit and implicit relationships for
reliable and contextual answers
LLMs for
language generation
Retrieval Augmented
Generation
Knowledge Graph
Construction

33. Neo4j Inc. All rights reserved 2023
LLMs for language generation

34. Generate
personalized
Natural Language
experiences
Prompt
(Description of desired text
with personalized data)
Response
(Generated
personalized Text)
LLM API
User Experience
(e.g. Email, custom Ad)
Enterprise App
(gets context from KG)
Knowledge
graph
LLMs for language generation

35. Retrieval Augmented Generation

36. User
Embedding
models
LLMs
Operational
database
Prompt
(Description
of desired
text with
personalized
data)
GenAI
app
Grounding with
Retrieval
Augmented
Generation (RAG)
Retrieval Augmented Generation

37. Retrieval Augmented Generation

38. Natural Language
Search on
unstructured data
using implicit
relationship
Prompt +
Relevant
Information
LLM
Embeddings API LLM
Chat API
User
Vector Search
Prompt Response
Relevant Results
/ Documents
Embedding
2
3
1
Vector Index
Retrieval Augmented Generation

39. Grounding LLM Responses with Implicit and
Explicit Search Through Knowledge Graph
User Question Vector
Vector Index
LLM
(Embedding Model)
Application
LLM
Smart Search
Explicit and Implicit
Results
Similarity
Search
Implicit Results
Explicit results in a Neo4j
knowledge graph
Natural Language
Results
Retrieval Augmented Generation

41. LLM Accuracy
Vector-Only RAG
Use Case Maturity (time)
Vector + Graph RAG
Business value
unlocked with
graphs
Use
Case
Effectiveness

42. This is just the beginning
The fusion of AI and graphs will be amazing

43. This is just the beginning
Researchers are leading the way

44. LLMs hallucinate in
interesting and
potentially troubling
ways
[Yejin Choi et al, U Washington]

45. LLMs hallucinate by in
interesting and
potentially troubling
ways
[Yejin Choi et al, U Washington]
Knowledge model 435x smaller and 16% better than raw LLM

46. New techniques
promise a revolution
in creating better and
smaller LLMs
[Yejin Choi et al, U Washington]

47. The future is connected

48. The future is connected
Rethink what’s possible

49. 250K data pros
Join our
Global Community
Enroll in
GraphAcademy
20K completed courses
Attend
NODES 2023
24 hours, 10K+ attendees
OCT 26th

50. Thank you
Dr. Jim Webber
jim.webber@neo4j.com