We walk through the steps of building an application with Grakn. We will begin with how to construct a model of the scenario, moving on to talk about how to use reasoning to reach concrete insights, and go further to use Analytics for a deeper understanding of the dataset. Finally, we will show the specific usefulness of Grakn for applications in planning, logistics and operations, as we create a journey planner to get us from A to B across the London Underground. Blog post: https://blog.grakn.ai/planning-logistics-and-operations-modelling-the-london-tube-network-in-grakn-7c03baff65c6 Github code: https://github.com/graknlabs/grakn_examples/tree/master/tube_network_example

1. Planning, Logistics and Operations:
Modelling the London
Tube Network in Grakn

2. Overall Objectives:
o A quick introduction to Grakn
o Get in deep with some real data
o Look for insights into the tube network that
aren’t obvious
o Build a journey planner to find a efficient
path through the network

3. What is a knowledge graph?
Knowledge schema
Flexible Entity-Relationship concept-
level schema to build knowledge
models
Model complex
domains
Automated Reasoning
Automated deductive reasoning of data
points during runtime(OLTP)
Deriveimplicitfacts&simplification
Distributed Analytics
Automated distributed algorithms
(BSP) as a language (OLAP)
Automatedlargescaleanalytics
Higher-Level Language
Strong abstraction over low-level
constructs and
complex relationships
Easier toworkwith
complexdata

4. “ForacomputertopassaTuringTest,itneedsto
possess:Natural LanguageProcessing,
KnowledgeRepresentation, Automated
Reasoning andMachineLearning”
Peter Norvig (Research Director, Google) and Stuart J. Russell (CS
Professor, UCBerkeley),“Artificial Intelligence: A Modern
Approach”, 1994
Wait,whydoweneeda knowledgebase/graph?

5. Other database solutions are just that – data storage, not knowledge storage
Data Data
Knowledge
?
From data to knowledge

6. Data Import
Schema Design
Build
Reasoning
Statistics
Analytics
Use
Tube Network Knowledgeset

8. The Raw Data
A quick sample of TFL data:

9. The Raw Data
Tube Stations
• Naptan-id
• Name
• Latitude
• Longitude
Tube Lines
• Name/id
Zones
• Name
Routes
• Naptan-id of origin
• Naptan-id of end destination
• Tube line operated by
Timetables
• Schedule of duration to stops along the route
(we derive tube-stop ordering from this)
• Tube line operated by

10. routes
940GZZLUNHG
940GZZLUHPK
940GZZLUEAN
940GZZLUNAN
940GZZLUWTA
940GZZLUEBY
2 mins
4 mins
7 mins
15 mins
17 mins
route 1
940GZZLUSBC
940GZZLUWCY
940GZZLUEAN
940GZZLUNAN
940GZZLUWTA
940GZZLUEBY
2 mins
5 mins
8 mins
12 mins
15 mins
route 2
940GZZLUSBC
940GZZLUWCY
940GZZLUEAN
940GZZLUNAN
940GZZLUWTA
940GZZLUEBY
940GZZLUNHG
940GZZLUHPK
More familiar
How?

12. Which way to go?
station station
route-section
route-section
service
serviceserviceserviceservice
route-section route-sectionroute-sectionroute-section
tunnel
beginning end
station station
route-
section
route-
section
route-
section
route-
section
route-
section
to
to
to
to
to
from
from
from
from
from
5n
5 x n
duration
has
station
route-
section
route-
section
route-
section
route-
section
route-
section
to
to
to
to
to
from
from
from
from
from
A nicer way
n

13. Schema
A <relationship> has a <role> in the form of a <subject>

14. Reasoning

15. Reasoning

17. Reasoning

18. Rules: Inferring Concepts
Here we impart knowledge: If the destination is further East than the
origin, then the route is Eastbound

19. Rules: Inferring Concepts

20. Statistics

21. How do we build a real
application? - Demo

22. Other database solutions are just that – data storage, not knowledge storage
Data Data
Knowledge
?
From Data to knowledge

23. From Data to knowledge
Facilitated by:
• Natural Representation of the interconnectedness of data
• Ability to make long highly-chained queries
• Capturing knowledge in the form of rules, applied at the database-level
• Easy access to the knowledge via natural queries
• Logical reasoning by deduction over the known information