1. Knowledge Representation and Reasoning into Machine and Deep Learning
David Newman SVP, Innovation R&D
Innovation Group, Wells Fargo Bank
linkedin.com/in/davidsnewman1
September 27, 2017

2. What is Knowledge Representation and Reasoning (KRR)?
Presentation Topics
The Challenge of Data
What is Knowledge Representation and Reasoning (KRR)?
What are Some Use Cases?
What are some ways that KRR via Ontologies can be Applied to Improve Machine and Deep Learning?

3. Why are we so Impacted by Data Challenges?
Collecting, Cleaning, and Organizing Data Consumes at Least 80% of Data Scientists Time.
Why are we so Impacted by Data Challenges?
*Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task,
Survey Says, Forbes, March 2016

4. What are Some of these Key Data Challenges?
Data challenges include:
reconciling and harmonizing disparate data across line of business silos
linking and aggregating data
validating, curating, and classifying data for operational processing and creating good feature sets for machine learning
We need an effective data management capability that can ensure that data:
is harmonized and integrated across all data sources
is curated and aligned to common meaning
is utilized and understood not only by humans, but by machines as well

5. Conventional Data Management Capabilities are Not Sufficiently Fulfilling our Data Needs!
Bad Data
Bad Data

6. Applying Knowledge Representation and Reasoning using Semantic Technology is a Way to Meet our Complex Data Needs!

7. Ontologies Express the Meaning of Concepts
Concept of “Corporate Control”
Subject
Corporation
Predicate
controls
Object
Corporation
type
is kind of
type
controls
majority
voting shares
London Bank
is inverse of
plays role
plays role
inference
inference
Parent
Company
is majority
controlled by
Subsidiary
“Things” not Strings

8. For Finance the EDM Council is Developing a Free and Open Source Common Financial Data Standard Using Ontologies

9. FIBO Ontologies Provide a Scaffolding for Concept Reuse and Extension… Supporting Efficiency, Effectiveness and Governance
Industry Standard
Ontology
Enterprise Standard
Ontology
Enterprise Knowledge
Graph

10. Ontologies can Play a Critical Role to Better Enable AI
Artificial Intelligence
Knowledge Representation and Reasoning
Machine Learning
Deep Learning
Natural Language Processing

11. Progressing Up the AI Stack
Ontologies are a Bridge Between Human Induced Knowledge and Machine Induced Knowledge
Machine Induced
Knowledge
Cognitive Computing
Machine and Deep Learning
Foundational knowledge for cognitive computing to learn and build stronger associative connections
Progressing Up the AI Stack
Enterprise Ontology
Semantically curated and inferred data will give greater lift to machine learning
Prior knowledge from ontologies used for supervised ML training sets for data quality, correctness and better NLP
Conceptual scaffolding for enterprise ontology
Validate, curate, link and harmonize legacy data
Industry Ontology
Human Induced
Knowledge

12. Ontologies can be Used for Better Feature Engineering
Disparate
Data
Sources
Data Linkage
and Harmonization
Data Validation
and Consistency
Data Classification
and Inference
Create Feature Set
From Multiple
Disparate Data
Sources and
Formats
Identify instances
of incorrect or
inconsistent data
Smart Dimensionality
Reduction

13. Ontologies Enable Data Harmonization and Alignment Needed for Consolidation of Disparate Data Based on Common Meaning
Global Bank owns > 50% voting shares of London Bank
Semantic Mapping
Semantic Adapters
FIBO Operational
Ontologies
FIBO
RDBMS
Big Data

14. Ontologies Better Position us to Manage Linked Data and Reduce Data Redundancy
“Things Not Strings”

15. Using Ontologies … Automated Logical Consistency Checks can be Performed to Identify Violations of Semantic Rules in Features
Definition of Aunt in Ontology
Supervised
Machine Learning
Data in Message Payload
Logical
Inconsistency!
Reasoning

16. Human Facing Definition Machine Facing Definition
Ontologies can be Used to Reason over Data for Classification and Dimensionality Reduction
An interest rate swap in which fixed interest payments on the notional are exchanged for floating interest payments.
Human Facing Definition
Fixed Float IR Swap
InterestRateSwap and
hasLeg some FixedRateLeg
and hasLeg some FloatingRateLeg
Machine Facing Definition
Fixed Float IR Swap
Business Entity
Business Entity
Interest Rate Swap
London
Bank
Atlas Bank
type
Inferred
Fixed Float IR Swap
identifies
identifies
type
Inferred
LEI5001
Swap1001
LEI7777
LEI
has
party
has Leg
Swap
has Leg
has
party
LEI
Floating Rate Leg
Inferred
Swap
Leg 1
Swap
Leg 2
Inferred
Fixed Rate Leg
type
type
has principle
has rate
has rate
has principle
LIBOR 5%
has currency
has currency
USD
USD

17. Knowledge Graph is the Convergence of Ontologies with Machine Learning Insights
Probabilistic Associations and Classifications
all counterparties for interest rate swap trades that are likely (> 99.5%) to default given a x% rise in LIBOR
Operational Semantic Graph Data
Knowledge Graph

18. Machine Learning can Generate Probabilistic Associations that can be Expressed by the Knowledge Graph as New Relations
Address
Explanation
Person A
Other
Name
Cosine
Other Relation
Customer A
Similarity
Infer predicate relationships
(same as, colludes, knows, et.al.)
Household
Customer B
Address
Enterprise Entity Resolution
Dedupe/Link Customers,
People, Companies, Addresses
From Strings to Things
KYC, Fraud, Credit Risk …
Person B
Name
Other Relation

19. Ontologies can be Used to Transform Unstructured Content into a Structured Form for Machine Learning and in silico Memory
intended use
plans to
expand
“I am planning to buy a truck to expand my construction business”
capture content
James
Jackson
semantically
parse and classify text
Natural
Language
Understanding
store customer
communications
Intent: purchase truck
Context: business expansion
Action: recommend Auto Loan
Action: offer to increase credit line
Knowledge
Graph
understanding
machine
learning
recommendation

20. Ontology Concept Iteration for Text Generation and Extraction
Ontologies can also Help Generate More Precise Neural Word Embeddings to Better Optimize Natural Language Understanding
Concept
Definition
Concept
Web Link
Ontology Concept(s)
Relation
Relation
Definition
Concept
Annotation
Web Link
Ontology Concept Iteration for Text Generation and Extraction
Understanding contracts
that describe financial instruments
Merge human knowledge
with machine knowledge
Train with less but more
precise data
Generate Neural Word Embeddings
for each Concept in Ontology
Neural Word Embeddings
Neural Network

21. Important Advances in Ontology and Machine Learning Research
Research from the University of Mannheim, Germany [Paulheim, Stuckenschmidt] show how ontologies can help ML perform validation of data at least 50x faster than a state of the art semantic reasoner by training a binary classifier.
Research from Rensselaer Polytechnic Institute [Makni, Hendler] shows how we can use ontologies to train deep learning neural networks to perform reasoning to materialize semantic inferences efficiently at scale.
Research from the University of Oregon [Wang, Dou, Lowd] shows how we can use ontologies to improve ML insights by building a lattice of deep neural networks that reflects the structure of taxonomies. They call this Semantic Deep Learning.

22. Thank You!
linkedin.com/in/davidsnewman1