When we set out to build a knowledge graph at Zalando, most people did not know how to build one, or considered machine learning as the better solution. However, endorsement from upper management led to the current project, where we use ontologies to improve the customer search and browsing experience. There are many unique things about the way we built our ontology for Enterprise purposes. Our ontology is peer-reviewed, use case-driven, and we apply special techniques to keep the graph and our APIs and data in sync. Communicating the graph to different professionals also has its challenges. Backend engineers and machine learning experts have a hard time understanding knowledge graph quirks. Product people accept it only if it creates a clear improvement for customers. How do you reconcile them all?

1. BUILDING THE
FASHION
KNOWLEDGE
GRAPH
TALK AT
CONNECTED DATA LONDON
KATARIINA KARI
07-11-2018

2. 2
Zalando at a Glance
Enterprise Knowledge Graph
Definition of the Knowledge Graph
In the Beginning...
What we implemented and added value
BUILDING THE FASHION
KNOWLEDGE GRAPH

3. 3
ZALANDO AT A GLANCE
~ 4.5billion EUR
revenue 2017
> 200
million
visits
per
month
> 15,000
employees in
Europe
> 90
million
orders
> 24
million
active customers
> 300,000
product choices
~ 2,000
brands
17
countries
as at Aug 2018

4. OUR VISION:
CONNECTING PEOPLE AND FASHION

5. 5
KNOWLEDGE GRAPH
AS WE UNDERSTAND IT

6. 6
A NAMED DIRECTED GRAPH OF CONCEPTS WITH URL-LIKE IDENTIFIERS
https://knowledge.zalando.net/ontology/pumps
IRI
URL
internal
structural
associative

7. 7
UNDERSTANDING AND SPEAKING OUR CUSTOMER’S LANGUAGE
The right kind
of contents
The best
possible view
contents
SEARCH BROWSING

8. 8
FASHION CONCEPTS ARE THE CORE
Zalando
contents
application
ontology
external
vocabulary
schema.org
extension?
?

9. COMMUNICATING THE KNOWLEDGE GRAPH
TO DIFFERENT PROFESSIONAL

10. 10
COMMUNICATING THE KNOWLEDGE GRAPH
PRODUCT MANAGERS
Does it improve our
customer experience?
Does it make money?
BACKEND ENGINEERS
Open World
Assumption?
Why Graph Databases?
MACHINE LEARNING
EXPERTS
Only see the graph as a data
source like any other data and
complain there is too little of
the data.

11. 11
IN THE BEGINNING...

12. “Search can be improved with many Machine Learning algorithms.
Most successful search engines also use Knowledge Graphs to
improve the search. We should explore this possibility.”

13. “Is a static Category Tree the best way
to represent fashion contents?”

14. 14
IN THE BEGINNING...
Little Semantic Web
Knowledge Inside the
Company
“Ontologies were used in
one project I worked on
in another company.
They did not really
work.”
“Machine Learning
works better.”
“So it is manual work?
Will it scale?”
Upper Management
Endorsement
Team of Backend Developers
was put together and some
research engineers
Knowledge
sharing on
Ontologies
RDF,
SPARQL

15. 15
GETTING INTO THE TOPIC OF SEMANTIC WEB
Do you the benefit and added value of
knowledge graphs?
Team skills
Research
Backend
Engineering
Backend &
Frontend
Engineering
Product

16. 16
GETTING INTO THE TOPIC OF SEMANTIC WEB
Modelling
RDF
GraphDB
OntoClean
“Proper
modelling.
How should it
be done?”
SPARQL
RDF
Syntax,likeTurtle
GraphDB
Modelling
OntoClean
HARD TO LEARN
“Enough
high-quality
data”
“Knowledge modelling. Data
has to be correct at all times,
but at the same time simple
and easy to follow”
“Performance and use
of graph databases”
“balance between a clean
graph and use cases”
EASY TO LEARN

17. 17
COOLEST TOPICS OR FEATURES IN SEMANTIC WEB
“Graph
databases in
general”
“Sparql Query
language”
“interlinkedness: how one
part of the system can make
use of another, almost
unrelated, part”
“The power of SPARQL
for querying the graph.”
“SPARQL and what it can
do with normalised data”
“The possibility to connect
different kinds of information
from multiple sources into
one entity”
“There are many features and
use cases still undiscovered,
which I believe, a graph data
structure helps to fulfil.”
“Ability to create human
understanding and
'intelligence' out of relations.”

18. 18
WHAT WE HAVE
IMPLEMENTED
& BIGGEST ADDED VALUE

19. 19
IMPLEMENTATIONS
Zalando
contents

20. 20
WHERE WE SEE THE MOST VALUE OF THE GRAPH
Measured significant
improvement of search
powered by the graph
Fashion concepts do not
only fetch products, they
fetch editorials and other
kinds of content

21. 21
A KNOWLEDGE GRAPH
FOR AN ENTERPRISE

22. 22
GRAPH CONTENTS IS PEER-REVIEWED
application
ontology
fashion
concepts
maintained in a GitHub repository
pull requests
4-eye principle
MODELLING PRINCIPLES
OntoClean adapted
Consistency in content
connections are analysed for
subsuming fashion concepts
Use Case Driven Modelling

23. 23
NO ZALANDO CONTENTS IN THE GRAPH
Zalando
contents
> 300K products cannot
be stored in the graph
MICROSERVICES TO THE RESCUE
We store rules with which products
can be retrieved from other
systems via API calls.
Another service uses those rules to
index our fashion concept
identifiers onto Zalando’s products.
So that other services can
consume it.

24. 24
GRAPH DATABASE – WHAT TO USE?
DATOMIC
Implement our own
triple store
BLAZEGRAPH
Open-source graph
database
AMAZON NEPTUNE
AWS, compliant, supported

25. 25
HOW WE DECREASED LATENCY
COMPLEX
MODELLING
What is a fashion
concept is implied via
modelling
SPARQL
Queries are long and
complex = latency
INFERENCE
We implement our own
inference rules

26. 26
GDPR

27. The Knowledge Graph at Zalando is….
satisfying use cases
peer-reviewed
and adapted to a micro-service architecture

28. The Knowledge Graph
adds convenience for our customers
and drives a dynamic shop experience.

29. THANK YOU
QUESTIONS?

30. KATARIINA KARI
katariina.kari@zalando.fi
+358 40 513 5700
07-11-2018
RESEARCH ENGINEER

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