Knowledge Graphs for a Connected World - AI, Deep & Machine Learning Meetup

Knowledge Graphs 
for a Connected World
March 24, 2016
Benjamin Nussbaum  
@bennussbaum
www.graphgrid.com | www.atomrain.com

Introduction
Benjamin Nussbaum
20 years of Technology Innovation. 
Software architecture | Database design | Server infrastructure 
President & CTO of AtomRain, 
one of the world’s leading NEO4J Solution Partners  
and makers of GraphGrid.
a platform by

Today’s Meetup Agenda
Knowledge Graphs for a Connected World
• What is driving the adoption of graphs
Graph Basics for AI Champions
• Where a graph fits within a web 3.0 strategy
• Why a graph is the first step to AI
• How a graph works
Graph Development for Innovation Teams
• Who does what
Graphs in Action
• Popular use cases
• Putting it all together
Q&A

A Web of Things 
Generating a Web of Data
Knowledge Graphs
are driving
strategies for
Web 3.0,
The Semantic Web

A Web of Things 
Generating a Web of Data
Dynamic Data
At Web Scale
The Entertainment Graph TM 
560 million nodes  
1.8 billion relationships 
3.0 billion properties
Continuous ingestion from dozens of
external such as Wikipedia, Netflix, Amazon
and iTunes for personalized recommendations
and social discovery of content.
The World’s Leading Graph Database

Brands and Ventures 
now have access to graph platform services
Solution Partner

Brands and Ventures 
now have access to graph development partners
Solution Partner

Knowledge 
Graph
Big Data 
Ingestion
Real-Time
Queries & Algorithms
Pre-Computed
Queries & Algorithm
Discovery 
+ Reasoning
Personalizing Apps Smart Places Interacting Machines
Your Graph is a Data Service to “Smart” Touchpoints
DATA PLATFORM API

Data  
Science
Artificial 
Intelligence
Relating 
with Interaction
Acting 
with Processing Layers
Serving 
with Graphs
Discerning 
with Patterns
Identify Link Prescribe Do ThinkPredict Sense Adapt
Apps  
access and update 
the graph
Real-Time 
data about customers 
things, and relationships.
Algorithms  
reason 
over the graph
Patterns 
for best, worst, and next 
steps or things.
Smart Things 
send machine results  
to the graph
History  
of a machine’s 
action and results.
AIs 
access customer
insight 
in the graph
Prediction 
of a customer’s 
next need or want.
A Graph Manages  
your Brand’s Evolving Knowledge
Knowledge Graph

A Graph Records a New Kind of Data
Semantic Web 
and  
Knowledge Graphs
Enterprises Systems 
and  
Business Transactions
For Business Operations
▪ Business Systems 
generate data.  
▪ Data about Business 
Orders, purchases, invoices, 
customer interactions… 
▪ Static System of Record  
Standard data; relationships are
not first class citizens. 
CRM 
System
Product
Catalog
Invoice 
System
▪ Connected Customers & Smart
Things 
generate data. 
▪ Data about Real-World  
Concepts, people, places, things, 
and their relationships. 
▪ Dynamic Graph of Relationships 
Discovers and learns through
patterns as relationships change 
For Connected Experiences

A “Node” 
in the graph
Hotel
Room
Person
A Graph Models 
Real-World People, Places, and Things
Solution Partner
A “Label” 
in the graph

A “Relationship” 
in the graph
PREFERS
Hotel
Room
Person
HAS_AVAILABLE
A Graph Models 
Contextual Relationships
Solution Partner

PREFERS
Hotel
Room
Person
“Properties” 
in the graph
lastStayed: 2-10-2015
name: Hilton Hotel
name: Jane Smith
number: 315
HAS_AVAILABLE
A Graph Stores and Updates Data 
about Each Thing and its Relationships
Solution Partner

PREFERS
Hotel
Room
Person
Queries 
traverse the graph to discover 
relevant resources
For Jane’s preferred hotel 
and travel destination,  
identify available rooms, 
present information to her app. HAS_AVAILABLE
Algorithms 
calculate to 
solve problems
- Spot Patterns. 
- Prescribe Best Solution. 
- Predict Results.
Queries and Algorithms 
Reason over the Graph
Solution Partner

Graph Queries 
Start with one “entity” and traverse the graph  
to discover linked people, places, or things
Query for a Graph
MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,  
count(report) AS Total
NEO4J Cypher Language
“Complex Join” in SQL
Solution Partner

Example:  
Calculates the shortest path—the least number of nodes, relationships—between two nodes
Traversal Algorithms 
Navigate the graph and calculate to spot patterns or solve problems
Solution Partner

Subject Matter Experts work with Graph Experts 
to create the conceptual model

Graph Software Engineers 
create the software solution to transform and load data into the graph model

Multidisciplinary Team 
ensures the quality of queries and algorithms
User Results
Ongoing Lab:
• Subject Matter Experts
(i.e Marketing)
• Data Engineer
• Algorithm Developer

Knowledge 
Graph
Big Data 
Ingestion
Real-Time
Queries & Algorithms
Pre-Computed
Queries & Algorithm
Discovery 
+ Reasoning
DATA PLATFORM
Platform Experts 
manage the scaling platform
Top Challenges 
1. Query Performance
2. Algorithm Performance 
3. Graph Operation 
at Scale
4. Server Infrastructure  
at Scale 
5. Ingestion Engines
6. Entity Resolution
API
An enterprise-grade, internet scale 
data management platform

Master Data Management
For customer interests, product lines, store locations, org charts…
For white papers, visit neo4j.com/use-cases/

Identify & Access Management
Validates who you are, what group you belong to, and what you’re permitted to
do.

Graph Based Search
Delivers a structured result: such as a song, music attributes, artist, album, and
playlists.

Real time Recommendations
Based on past purchases, recent browsing, or friends’ purchases.

Social Network
Family, friend and follower relationships 
reveal influencers, peer groups, and patterns of social behavior.

Fraud Detection
Uncovers fraud rings and patterns of unusual customer behavior.

Putting it all together: A Connected Fitness Venture
PERSONA 
GOALS AND PREFERENCES
• Skill Level
• Health Conditions
• Workout Goals
• Eating Goals
• Muscle Groups
• Body Areas
• Workout Types
• Supplement Needs
CONSUMER WANTS
1. What fitness programs are best to help me accomplish my workout goals?
2. Which nutritional supplements will help me achieve my eating and workout goals?
3. Who in the community can I work out with and which workout would be good to do
together?
Scoring Algorithm  
considers importances  
the user places  
on each item
For Complete Review with Sample Queries 
http://neo4j.com/graphgist/95f4f165-0172-4b3d-981b-edcbab2e0a4b

BRAND’s  
WEB OF EVERYTHING
• Supplement lines
• Fitness programs
• Social network

Product Cross-Selling 
aligned to users’  
personal goals—and results

Q&A
What do you think about Graphs?

Thank You!
Knowledge Graphs for a Connected World
March 24, 2016
Benjamin Nussbaum  
@bennussbaum
www.graphgrid.com | www.atomrain.com

Knowledge Graphs for a Connected World - AI, Deep & Machine Learning Meetup

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