The presentation given at a Grakn meetup, held at Deutsche Telecom's hubraum in Berlin on 29 September 2018. The presentation details one example of how to construct an expert system at the database level.

1. T H E K N O W L E D G E G R A P H
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Deutsche Telecom PoC
By James Fletcher
Principal Researcher at GRAKN.AI
@graknlabs
@jmsfltchr

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“For a computer to pass a Turing Test,
it needs to possess: Natural Language
Processing, Knowledge
Representation, Automated
Reasoning and Machine Learning”
Peter Norvig (Research Director, Google) and
Stuart J. Russell (CS Professor, UC Berkeley),
“Artificial Intelligence: A Modern Approach”, 1994
Wait, why do we need a knowledge base/graph?

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The Architecture of Cognition
Comprehension and production of
language: communication
Natural Language Processing
Reasoning, problem solving, logical
deduction, and decision making
Automated Reasoning
Expression, Conceptualisation,
memory and understanding
Knowledge Representation
Judgment and evaluation:
To adapt to new
circumstances and to detect
and extrapolate
new patterns
Machine Learning
Information Retrieval, Natural
Language Understanding:
User data, Enterprise data,
Financial data, Web data, etc.
Knowledge Acquisition
COGNITION is "the mental action or
process of acquiring knowledge and
understanding through thought,
experience, and the senses."

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Knowledge Base/Graph
The Architecture of Cognition
Comprehension and production of
language: communication
Judgment and evaluation:
To adapt to new
circumstances and to detect
and extrapolate
new patterns
Information Retrieval, Natural
Language Understanding:
User data, Enterprise data,
Financial data, Web data, etc.
Storage of knowledge (i.e.
complex information), and
retrieval of explicitly stored data
and derive new conclusions.
Natural Language Processing
Machine LearningKnowledge Acquisition
COGNITION is "the mental action or
process of acquiring knowledge and
understanding through thought,
experience, and the senses."

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THE ARCHITECTURE OF A COGNITIVE SYSTEM
Natural Language Processing
Knowledge Base Machine LearningKnowledge Acquisition

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VALUE TO AI: BE THE UNIFIED REPRESENTATION OF KNOWLEDGE
Inference of low-level patterns and
automation of analytics algorithms
Machine translation for parsed
query interpretation
Expressive and extensible
knowledge model
INPUT SYSTEMS
e.g. Information Retrieval, Entity Extraction,
Natural Language Understanding
LEARNING SYSTEMS
e.g. Neural Networks, Bayesian Networks,
Kernel Machines, Genetics Programming
OUTPUT SYSTEMS
e.g. Natural Language Query,
Natural Language Generation

7. Deutsche Telekom - Project Scope
Aim
Demonstrate Grakn’s usefulness for customer support system
Method
Prove Grakn can solve a complex domain: router connection
troubleshooting
Why?
Router troubleshooting follows complex logic, and a full Excel
sheet of the procedure was available to us

8. System
Architecture
Server

9. Slack Expert System Demo

10. Excel Sheet Walkthrough

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Decision Tree
Speedport W 701V P=GR D=GR O=?
D=OFF C
P=OFF A
P=GR_
BLINK
_FAST
ST=GR K

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The Model

13. Schema Walkthrough
Plain English declarations – easy
to debug

14. Model Walkthrough
How to define router behaviour

15. Example Query – inferring the next action

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Infer next action
diagnosis
action

17. Slack Expert System Demo cont.

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Decision Tree
Speedport W 701V P=GR D=GR O=GR WL=GR

19. Going further
Deduce LED statuses from
the given information

20. Example Query – finding the possibilities

21. Workbase demo

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Infer next action
diagnosis
action

23. Workbase demo cont.

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How do we implement a real system?
• Native language
drivers

25. Schema
Designer