Building an Enterprise Knowledge Graph @Uber: Lessons from Reality

May 16, 20192 likes1,719 views

This document summarizes Uber's experience building an enterprise knowledge graph. It notes that Uber has over 200,000 managed datasets and billions of trips served, making it an ideal testbed for a knowledge graph. However, it also outlines several lessons learned, including that real-world data is messy, an RDF-based approach is difficult, and property graphs alone are insufficient. The document advocates standardizing on shared vocabularies, fitting tools and data models to existing infrastructure, and collaborating across teams.

Building an Enterprise
Knowledge
Graph @Uber:
Lessons from Reality
Joshua Shinavier, PhD
Knowledge Graph Conference
May 8th
, 2019
...

The future
Half empty
Half full
The present
Knowledge @Uber

● Uber is an ideal proving ground for an enterprise knowledge graph (EKG)
● 200k managed data sets
● Billions and billions of trips served
○ Low thousands of new entities per second
○ Totally doable!
● Even more sensor data
○ Use cases for graph stream processing
● Genuine need for knowledge and real-time inference
Knowledge @Uber

Building an Enterprise Knowledge Graph @Uber: Lessons from Reality

● Real data is messy
● We are not all ontologists

● Real data is messy
● We are not all ontologists
● Good enough does not scale

● Real data is messy
● We are not all ontologists
● Good enough does not scale
● Beware of the hype cycle

● Real data is messy
● We are not all ontologists
● Good enough does not scale
● Beware of the hype cycle
● RDF is a hard sell

● Real data is messy
● We are not all ontologists
● Good enough does not scale
● Beware of the hype cycle
● RDF is a hard sell
● Property Graphs are not enough

● Use and promote standards
● Invest in shared vocabulary

● Use and promote standards
● Invest in shared vocabulary
● Fit the tooling to the infrastructure

● Use and promote standards
● Invest in shared vocabulary
● Fit the tooling to the infrastructure
● Fit the data model to the data

● Use and promote standards
● Invest in shared vocabulary
● Fit the tooling to the infrastructure
● Fit the data model to the data
● Budget for “other stuﬀ”

Risk & Safety Knowledge Graph
This slide intentionally left blank to save entropy.
UBER KNOWLEDGE GRAPH

● Controlled vocabularies for all of Uber
○ Basic type aliases
○ Structured types for geospatial data, sensor data, money, etc. etc.
○ Entities and relationships (User, Vehicle, Trip, etc.)
○ Metadata vocabularies
● Elevates domain-speciﬁc RPC and storage schemas to ontologies
● Tooling carries schemas between data representation languages
○ Protobuf, Thrift, Avro, RDF, PG, etc.
Data Standardization

● Hundreds of thousands of structured datasets at Uber
● Data protections and user trust
○ GDPR and other regulations, Uber’s own data policies
○ What kind of user data? Where is it?
○ Heroic numbers of manual annotations
■ Limited expressivity, limited guarantees
■ Inference is required
● Two birds: in annotating datasets, standardize and compose schemas
○ Now we have a true global knowledge graph
○ Investigating eﬃcient reasoning and “No ETL” solutions
Metadata graph

● Common data model for RPC, storage, and KR at Uber
● In progress: alignment with the Property Graph Schema Working Group
● In progress: “Universal structure” of TinkerPop4
Algebraic Property Graphs

● Real data is messy
● We are not all ontologists
● Good enough does not scale
● Beware of the hype cycle
● RDF is a hard sell
● The Property Graph is not enough
● Use and promote standards
● Invest in shared vocabulary
● Fit the tooling to the infrastructure
● Fit the data model to the data
● Budget for “other stuﬀ”
● Collaborate early and often

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1. Building an Enterprise Knowledge Graph @Uber: Lessons from Reality Joshua Shinavier, PhD Knowledge Graph Conference May 8th , 2019 ...

2. The future Half empty Half full The present Knowledge @Uber

3. ● Uber is an ideal proving ground for an enterprise knowledge graph (EKG) ● 200k managed data sets ● Billions and billions of trips served ○ Low thousands of new entities per second ○ Totally doable! ● Even more sensor data ○ Use cases for graph stream processing ● Genuine need for knowledge and real-time inference Knowledge @Uber

4. EKG hierarchy of needs

6. ● Real data is messy

7. ● Real data is messy ● We are not all ontologists

8. ● Real data is messy ● We are not all ontologists ● Good enough does not scale

9. ● Real data is messy ● We are not all ontologists ● Good enough does not scale ● Beware of the hype cycle

10. ● Real data is messy ● We are not all ontologists ● Good enough does not scale ● Beware of the hype cycle ● RDF is a hard sell

11. ● Real data is messy ● We are not all ontologists ● Good enough does not scale ● Beware of the hype cycle ● RDF is a hard sell ● Property Graphs are not enough

12. ● Use and promote standards

13. ● Use and promote standards ● Invest in shared vocabulary

14. ● Use and promote standards ● Invest in shared vocabulary ● Fit the tooling to the infrastructure

15. ● Use and promote standards ● Invest in shared vocabulary ● Fit the tooling to the infrastructure ● Fit the data model to the data

16. ● Use and promote standards ● Invest in shared vocabulary ● Fit the tooling to the infrastructure ● Fit the data model to the data ● Budget for “other stuﬀ”

17. ● Use and promote standards ● Invest in shared vocabulary ● Fit the tooling to the infrastructure ● Fit the data model to the data ● Budget for “other stuﬀ” ● Collaborate early and often

18. Risk & Safety Knowledge Graph This slide intentionally left blank to save entropy. UBER KNOWLEDGE GRAPH

19. ● Controlled vocabularies for all of Uber ○ Basic type aliases ○ Structured types for geospatial data, sensor data, money, etc. etc. ○ Entities and relationships (User, Vehicle, Trip, etc.) ○ Metadata vocabularies ● Elevates domain-speciﬁc RPC and storage schemas to ontologies ● Tooling carries schemas between data representation languages ○ Protobuf, Thrift, Avro, RDF, PG, etc. Data Standardization

20. ● Hundreds of thousands of structured datasets at Uber ● Data protections and user trust ○ GDPR and other regulations, Uber’s own data policies ○ What kind of user data? Where is it? ○ Heroic numbers of manual annotations ■ Limited expressivity, limited guarantees ■ Inference is required ● Two birds: in annotating datasets, standardize and compose schemas ○ Now we have a true global knowledge graph ○ Investigating eﬃcient reasoning and “No ETL” solutions Metadata graph

21. ● Common data model for RPC, storage, and KR at Uber ● In progress: alignment with the Property Graph Schema Working Group ● In progress: “Universal structure” of TinkerPop4 Algebraic Property Graphs

22. ● Real data is messy ● We are not all ontologists ● Good enough does not scale ● Beware of the hype cycle ● RDF is a hard sell ● The Property Graph is not enough ● Use and promote standards ● Invest in shared vocabulary ● Fit the tooling to the infrastructure ● Fit the data model to the data ● Budget for “other stuﬀ” ● Collaborate early and often

23. [email protected] Thanks

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