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Building, and communicating, a knowledge graph in Zalando

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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?

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Building, and communicating, a knowledge graph in Zalando

  1. 1. BUILDING THE FASHION KNOWLEDGE GRAPH TALK AT CONNECTED DATA LONDON KATARIINA KARI 07-11-2018
  2. 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. 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. 4. OUR VISION: CONNECTING PEOPLE AND FASHION
  5. 5. 5 KNOWLEDGE GRAPH AS WE UNDERSTAND IT
  6. 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. 7 UNDERSTANDING AND SPEAKING OUR CUSTOMER’S LANGUAGE The right kind of contents The best possible view contents SEARCH BROWSING
  8. 8. 8 FASHION CONCEPTS ARE THE CORE Zalando contents application ontology external vocabulary schema.org extension? ?
  9. 9. COMMUNICATING THE KNOWLEDGE GRAPH TO DIFFERENT PROFESSIONAL
  10. 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. 11 IN THE BEGINNING...
  12. 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. 13. “Is a static Category Tree the best way to represent fashion contents?”
  14. 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. 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. 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. 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. 18 WHAT WE HAVE IMPLEMENTED & BIGGEST ADDED VALUE
  19. 19. 19 IMPLEMENTATIONS Zalando contents
  20. 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. 21 A KNOWLEDGE GRAPH FOR AN ENTERPRISE
  22. 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. 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. 24 GRAPH DATABASE – WHAT TO USE? DATOMIC Implement our own triple store BLAZEGRAPH Open-source graph database AMAZON NEPTUNE AWS, compliant, supported
  25. 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. 26 GDPR
  27. 27. The Knowledge Graph at Zalando is…. satisfying use cases peer-reviewed and adapted to a micro-service architecture
  28. 28. The Knowledge Graph adds convenience for our customers and drives a dynamic shop experience.
  29. 29. THANK YOU QUESTIONS?
  30. 30. KATARIINA KARI katariina.kari@zalando.fi +358 40 513 5700 07-11-2018 RESEARCH ENGINEER
  31. 31. This presentation and its contents are strictly confidential. It may not, in whole or in part, be reproduced, redistributed, published or passed on to any other person by the recipient. The information in this presentation has not been independently verified. No representation or warranty, express or implied, is made as to the accuracy or completeness of the presentation and the information contained herein and no reliance should be placed on such information. No responsibility is accepted for any liability for any loss howsoever arising, directly or indirectly, from this presentation or its contents. DISCLAIMER 31

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