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.
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
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
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
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
40.
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
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]