The document discusses the integration of knowledge graphs with generative AI to enhance data retrieval and decision-making processes. It highlights the benefits of using knowledge graphs, including improved search relevance and the ability to automate complex queries, while also addressing challenges like data accuracy and bias. It emphasizes the application of these technologies in various fields, such as finance, life sciences, and customer service.

1. Knowledge Graphs
and Generative AI
Data Science Solutions
Architect
Neo4j
1
Dr. Katie Roberts

2. © 2023 Neo4j, Inc. All ri hts reserved.
Agenda
Groundin LLMs with Knowled e
Graphs
● Knowled e Graphs
● Graph Data Science
● Semantic Search
Neo4j Grounded Chatbot Demo
2
Katie Roberts
Data Science Solution Architect

3. © 2023 Neo4j, Inc. All ri hts reserved. 3
LLMs Give Us an Amazing
Opportunity to:
Automate data
retrieval tasks
Improve customer
service experiences
Expedite readin ,
understandin , & summarizin
2
Generate
content & code
4
1
3

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Example…
Q: What was the impact o Hurricane Lee?
● A: Hurricane Lee was a lon -lastin tropical cyclone that
ormed in the Atlantic durin the 2017 hurricane season…..
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But There Are Challenges…
Knowledge
cut-off
1
Reasonable answers,
not always accurate
Can inherit bias throu h
trainin data
2 3
Lack of enterprise
domain knowledge
4
Inability to veri y or
attribute sources
5

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How can you take advantage of this
massive opportunity while
overcoming these challenges?

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Use a Knowledge Graph to
Ground LLMs
What was the
impact o
Hurricane
Lee?
Hurricane Lee
may impact
distribution
networks in
the ollowin
areas: MA, NY,
NH, ME…

8. © 2023 Neo4j, Inc. All ri hts reserved. 8
Grounding with Neo4j
Knowledge Graphs
Connect Enrich Consume
Context rich,
connected view o
your data that
enables easier
decision makin
Enhance your data
with raph data
science, text
embeddin s, and
additional derived
context
Ground responses
with in ormation and
context in the raph
Improve search
relevance combinin
vector search and
raph traversals

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Connected Data
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With Knowled e Graphs

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Knowledge Graphs
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Structured
Unstructured
Ontolo ies
Graph Al orithms and
Graph Queries
Semantics,
Derived Relationships and
Additional Context
Natural
Relationships

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What can you do with a knowledge graph?
Finance
How many fla ed accounts are
in the applicant’s network 4+
hops out?
How many lo in / account
variables in common?
Add these metrics to your
approval process
What completes the
connections rom enes to
diseases to tar ets?
What enes can be reached 4+
hops out rom a known dru
tar et?
What mechanisms in common
are there between two dru s?
Collaborative filterin : users
who bou ht X, also bou ht Y
What items make you more
likely to buy additional items in
subsequent transactions?
Traverse hierarchies-what items
are similar 4+ hops out?
Li e Sciences
Marketin &
Recommendations

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Enrich
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With Cypher Patterns, Graph Data Science &
Text Embeddin s

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Graph Enrichment
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Human-cra ted query, human-readable result
MATCH (p1:Person)-[:ENEMY]->(:Person)<-[:ENEMY]-(p2:PERSON)
MERGE (p1)-[:FRIEND]->(p2)
AI-learned ormula, machine-readable result
Predefined ormula, human-readable result
PageRank(Emil) = 13.25
PageRank(Amy) = 4.83
PageRank(Alicia) = 4.75
Node2Vec(Emil) =[5.4 5.1 2.4 4.5 3.1]
Node2Vec(Amy) =[2.8 1.8 7.2 0.9 3.0]
Node2Vec(Katie) =[1.4 5.2 4.4 3.9 3.2]
Queries
Al orithms
Embeddin s
Machine
Learnin
Workflows
Train ML models
based on results

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What Are Graph Algorithms?
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Insights From Graph Algorithms
Outliers, Influencers, Vulnerabilities,..
Recommendations, Homophily, Outliers,..
Recommendations, What-i Analysis, Disambi uation,..
Shortest Path, Optimal path, Route Optimization,...
Link prediction, Recommendations, Next-Best Action,..
Centrality
Pathfinding
Community
Detection
Similarity
Embeddings
Link Prediction
Dimensionality Reduction, Representation Learnin , ..

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Text Embedding Vectors for
Semantic Search
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Given a question, find the most relevant documents based on a similarity metric
(such as Cosine Similarity) between vector o the question and vectors o
contents.
Movin rom keyword search to similarity (semantic) search.
Q: what is a text
embeddin ?
abstractId similarity
456 0.923445
22 0.892114
… ...
Top K by similarity

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Graph Data Science Journey
Find the patterns you’re
looking for in connected data
Identify associations,
anomalies, and trends using
unsupervised machine learning
Learn features in your graph
that you don’t even know
are important yet
Knowled e Graphs
Graph Al orithms
Graph Native ML
→
→

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Consume
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With LLMs and Semantic Search

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Semantic Search Journey
Vector Similarity
Search
Graph Traversals &
Pattern Matchin
Knowled e Graph
In erence & ML
Find relevant documents and
content or user queries
Find people, places, and thin s
associated to content. Identi y
patterns in connected data.
Further improve search
relevance and insi hts by
enhancin your Knowled e
Graph.
Use raph al orithms and ML
to discover new relationships,
entities, and roups.
Vector Database Graph Database
Neo4j
Graph Data Science

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Knowledge Retrieval with Neo4j

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RAG (Retrieval Augmented Generation)
Pattern with Neo4j
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Neo4j
LLM API
User
Cypher Prompt + Relevant
In ormation
Prompt Response
Relevant Results
Retrieve relevant results rom Neo4j
usin LLM to enerate embeddin s
and/or Cypher
2
3
1
1
2
3
Combine relevant results with
prompt
Instruct LLM to only use the relevant
results to enerate response
LLM API
Embeddin s and/or
Cypher Generation
Improved ACCURACY and RELEVANCE o
responses
E. . What is the impact o Hurricane
Calvin?
Hurricane Calvin caused minor floodin in
Hawaii….
vs…
50 policyholders may be at risk o property
dama e due to Hurricane Calvin.

22. © 2023 Neo4j, Inc. All ri hts reserved. 22
Q: Does claim X
have any
association with
previous
fraudulent
parties?
Q: Provide a list
of claims with
damage
amounts
>$100,000
Q: What
policyholders
may be impacted
by Hurricane
Calvin?
Knowledge Retrieval with Neo4j

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Demo Intro
23
LLM Chatbot

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Typical Business Resilience Data
Analyze business impact o
● so tware & OS vulnerabilities,
● hardware & so tware up rades,
● buildin / eo raphic disasters
● chan es to business data ormats
…across mission critical applications and
business locations
24
hierarchies, flows,
relationships…

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Actual Data Model
25
CVE Data
Business Data
Elements
Vendors,So tware
Business Tasks
Application Instances
Data Trans ers
People/Roles
Locations
IT Assets

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LangChain
Demo
Application
● Translates En lish to
Cypher
● Consumption usin LLM
model with ew shot
promptin
● Data au mentation rom
Neo4j response
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Demo

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Neo4j Database
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API
Knowled e Graph
Graph Data Science
Graph DB
Knowled e Apps
In ormation Extraction
& In estion
Structured
Unstructured
Ontolo ies
GCP Vertex AI
Data
Sources API Layer
Customer Service
Ticket Tria in
Recommendations
News Content &
Discovery
Enterprise Knowled e
Search
Patient Prioritization
Clinical Decision
Support Systems
Pharmacovi ilance
Health Assistants
FAQ Bots
LLMs
Amazon
Sa eMaker
AzureML
Bloom
Aura Enterprise Edition LLMs
API
Step 1: Capture Knowled e Step2: Enrich Step 3: Semantic & Contextual Search

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Neo4j Enriches All Phases of an
AI Ecosystem
Knowled e Graphs Graph Feature
En ineerin and Graph
ML
Graph Analytics,
Investi ations and
Counter actuals
Inte rations and
Knowled e Graphs or
Heuristic AI
Connect Analyze Capitalize
Interpret
Neo4j Neo4j GDS Neo4j Bloom Neo4j Connectors

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Thank
You!
katie.roberts@neo4j
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