Semantic AI Making Great Data and Making Data Great

CONFIDENTIAL.© 2019 SMARTLOGIC SEMAPHORE INC.
Semantic AI – Making
Great Data and Making
Data Great
Smartlogic Webinar
1
Thursday, May 23rd, 2019
11 am – 12 pm Eastern

Speakers
22
Jeremy Bentley
CEO & Founder
Smartlogic
Scott Henninger
Senior Solution Architect
Smartlogic

A few housekeeping items
33
• This webinar is in broadcast mode
all participants are muted.
• Please put your questions in the
GoToWebinar panel and we’ll
answer as many as we can in the Q
& A session.
• This broadcast is being recorded –
replay information will be sent to all
registrants following the broadcast.

Agenda
44
• A Bit About Smartlogic
• Semantic AI - the what and where it fits
• The value of Semantic AI
• Semantic tools and technologies – benefits, value and
examples
• Q & A

CONFIDENTIAL.© 2019 SMARTLOGIC SEMAPHORE INC. 5
Semantics/Semantic AI

CONFIDENTIAL.© 2019 SMARTLOGIC SEMAPHORE INC. CONFIDENTIAL
Make Great Data & Make Data Great
6© 2018 SMARTLOGIC SEMAPHORE INC.
What if you could turn information assets into knowledge that answers questions that
prevent revenue leakage, minimize risk and accelerate growth?
Smartlogic’s Semantic AI platform – Semaphore – is applied to enterprise initiatives such as
improving customer experience, contract lifecycle management, records management, data
and text analytics, process automation, regulatory compliance and information security.
Extracting & Harmonizing text into qualified contextual data is how we “Make Great Data”
Harmonizing & Enriching data into qualified contextual data is how we “Make Data Great”
Semaphore provides the semantic layer in your digital ecosystem to generate semantic
(meta)data, automatically extract & classify context and meaning from unstructured and
structured information assets and manage knowledge models.
Our focus on information intensive solutions spans cross-industry, addressing specific
business needs within Global 2000 organizations.

What problems do we solve?
Smartlogic focuses on enabling solutions to data quality problems
within Global 2000 information intensive organizations
There is great value in my
customer interactions, but
I can’t access the data
which is in text form
My data lake and our
content stores are really a
cesspool
I need to see our
enterprise information
end to end, not just within
silo.
I’m ignoring 80% of my
information in my BI
analytics programs
Different systems use
different vocabularies, I
need an enterprise catalog
Our contracts reality does
not line up with our CRM
system data.
Sensitive information is
not secure
Manual (customer service)
workflows are expensive
and prone to error
Our content includes
personal and sensitive
information. We need a
way to find it (GDPR).

AUDIENCE
INFORMATION SERVICES
SYSTEM APPLICATIONS
Traditional Enterprise Architecture
Content
Creators
Managers &
Editors
Decision Makers
Business
Partners
Customers
Business Intelligence Business Apps & Workflow Systems Portals
Business Systems Enterprise Search
Databases | Data Warehouses CMS | Information Feeds | ERDMS | DAM
Structured Unstructured

AUDIENCE
SYSTEM APPLICATIONS
Semantic Enterprise Architecture
Content
Creators
Managers &
Editors
Decision Makers
Business
Partners
Customers
Business Intelligence Business Apps & Workflow Systems Portal
SEMANTIC AI
Meanings, concepts & relationships
i.e. Addressable Enterprise Context

The AI we are talking about
Smartlogic

AUDIENCE
SEMANTIC AI
SYSTEM APPLICATIONS
How Semantic AI Fits in the Enterprise
Content
Creators
Managers &
Editors
Decision Makers
Business
Partners
Customers
Business Intelligence Business Apps & Workflow Systems Portal
Semaphore - Modular Semantic AI Platform
Fact Extraction| NLP &/or Rules-based Classification | Semantic Integration & Visualization| Knowledge Modeling

Data vs Text
12
Social Media threads
Chatbot conversations
Call centre logs
Catalogues
Product Sheets
Commercial Contracts
Research reports
Export processing
Engineering notes
Design criteria
Customer on-boarding documents
News & bulletins
Corporate Actions & Minutes
Records
Schema names
Special Instructions
Metadata values
………..
Tweets, IM….
Emails
News
PDFs
Documents
Presentations
Spreadsheets
Database fields
……
Internal and External, Structured & Unstructured

Example of Harmonization, enrichment & extraction
13
Patient Age: 73
Sex: Female
Ethnicity: Caucasian
Treatment: Eszopiclone - DB00402
(Insomnia)
Treatment: Zolpiden - DB00425
(Migraine)
Adverse Event: Syncope
Date of Event: 2014-08-10
Semantic
Integration
Service
Semaphore Knowledge Model
Management, Classification &
Language Service, Facts
Framework
Raw text Qualified Data
Initial information was
received from the reporter on
Aug 14th, 2014. This is a
report of a serious adverse
event from a reporter
regarding a 73 year-old white
female subject experienced a 5
minute blackout on Aug-10
while taking Fulnite for the
treatment of Insomnia and a
Nasen for the treatment of
migraines.

The Value of Semantic AI
14
Perceptive Insight Engines
Tightened Contract Lifecycle Management
Timely Contract Risk Assessment
Enriched Robotic Process Automation
Performant Metadata Management
Scrupulous Compliance Processing
Agile Predictive Analytics
Augmented Data Loss Prevention
Lossless Records Management
Exceptional Knowledge Management
Trustworthy Cognitive Computing
Semantic AI
Platform
Text-dependent systems Qualified Data
Search Engines
Contract Management
Risk Reporting
Process Automation
Metadata Management
Compliance Processing
Predictive Analytics
Data Security
Records Management
Knowledge Management
Cognitive Computing
Customer Intimacy
Revenue Loss Prevention
End-to-end RPA
Silo agnostic BI
Risk optimization
Customer Onboarding
Safety Analytics
Heavy Asset Servicing
ITAR Compliance
Supplier Optimization
On-line Sales Promotion
Knowledge Recycling
Valuable Business Outcomes
Competitive Advantage
10:10 projects

→ Humans create and manage knowledge models that capture business
rules, meanings & relationships, ML augmented.
→ Context is referenced from knowledge models and encoded by the
Semantic AI engine in the form of {meta}data.
→ Adopting an enterprise Semantic AI platform means that context is
addressable by all enterprise systems
→ This synthesized contextual data is harmonized, enriched and of quality
→ Semantic AI delivers 10:10 projects compared to the old way of working
Summary

Semantic Tools and Technologies

Model-Driven Semantic AI
17
→ Defining semantic information from data
o structured and unstructured data
→ Terminology check: Model
o For purposes here, a model is an ontology or a taxonomy
o Defined with a formal structure
o a glossary, set of terms or controlled vocabulary on its own is not a model
• applying a formal structure – classes, sets, relationships – to the vocabulary makes it a model
→ Modeling with W3C Semantic Technology standards (aka recommendations)
o SKOS, SKOS-XL, RDFS, OWL, SHACL
o Are other modeling formalizions possible
• Yes, but not a topic or today

Harnessing Models for Enrichment and Extraction
of Semantic Metadata
18
Build and manage semantic models
Simplify the ingestion, development and customization
Enrich, extract and harmonize
• Enrich information assets with complete, consistent and precise metadata
• Extract critical facts, entities and relationships for further processing
• Harmonize different information sources for unified access
Apply semantics to your business problem
• Enable knowledge discovery
• Support investigative analytics
• Automate manual processes for higher precision

Core Modeling Technologies – W3C Semantic Technologies
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→ Simple Knowledge Organization System (SKOS)
o controlled vocabularies – hierarchies, taxonomies, glossaries, thesauri
o concept-based: each “term” is a concept
• preferred label, synonyms (alternative), relationships (hierarchical, associative)
o label extension for SKOS (SKOS-XL)
• every label is an object - capture metadata about the label
→ RDF Schema (RDFS), Web Ontology Language (OWL)
o relationship types, class-subclass relationships and instances (sets)
→ RDF Shapes Constraint Language (SHACL)
o data validation and cleansing, detect inconsistencies, etc.

Core Modeling Technologies (2)
20
→ Underlying data model is the Resource Description Framework (RDF)
o can therefore use SKOS, SKOS-XL, RDFS and OWL in same model
→ Model example:
o Synonyms (SKOS)
• <Internet> has preferred label “Internet”
• <Internet> has alternative label “World Wide Web”
o Hierarchical relationships (SKOS)
• <Internet> has broader concept <Networking>
• <Networking> has broader concept <Information and communication technology>
o Associative relationships (SKOS)
• <Internet> has related concept <Websites>
o Class membership (RDFS/OWL)
• <Internet> is a member of <Information Technology>

Build and Manage Semantic Models
21
Concept
Hierarchy
Concept property values
Ontology (class
membership) Associative relationships

Build and Manage Semantic Models
22
SHACL Constraint Violation
(warning, info, violation)

→ NLP-based rules built automatically from domain model
o Built for for business users
o Leverage all model information including relationships
o Disambiguates content
o Can apply additional relevant tags
→ Scoring based
o More advanced than Boolean rules
o Use all evidence available
→ Accurate and Controllable
o Over 50 rule templates tunable to specific use cases
o Templates generate rules for each concept
o Easy to see how classification decision was made
→ No need to build training sets
→ Text mining to enrich domain model
→ NLP in 20+ different languages including CJK
→ Domain model can be leveraged for other use cases
Metadata Creation - Extract from Data
23

Classification Example
24
Text being analyzed
The Bottom Line: the metadata we need to understand this document doesn’t
appear in the text. Semaphore uses ontologies and evidence in the text to create
the metadata needed for analysis.
Metadata created by
Semaphore
Transparent Rules
showing how
Semaphore
determines
metadata

Find | Discover | Personalize
25
Overviews and
resources
Filters organised by
facet
Accurate results
Explore relationships
Context and/or
more detail

Harmonize Data from Disparate
26
Leveraging Internal and External Terminology Resources and Models (Ontologies, Taxonomies, Controlled Vocabularies, etc.):
All organizations have these to some degree.
Augment the process of generating and building models: “assisted model generation”.
Industry Standard
Model Sourcing
Reference Data
Sourcing
Access Linked
Open Data
Internal Model
Sourcing

Information Flow
27
SME
Content
Manager
Searcher
Classification
Strategy
Administrator
Text Mining
Publisher
Semaphore Classification
Server
Semantic
Enhancement
Server
Search Application
Framework
Search
Content
Repository
Sample Content
Linked Data & Linked
Open Vocabularies
Existing Models
Additional
Vocabulary
Link
Import
Rules
Content
Metadata
Metadata
Semaphore
Ontology Editor

Assisted Ontology Generation
28
Use classification engine to build, refine the ontology
Drag-and-drop into taxonomy
Text Miner – uses
classification
engine to identify
entities

More About Creating Data from Documents for Analytics
Semaphore

Fact Extraction
Using semantic techniques to transform unstructured or
textual content into sets of concrete data elements that
can be used in other business applications.
This allows organizations to take advantage of the 80% of information
that is currently beyond the reach of business intelligence applications.

Rules
Model-Driven Fact Extraction
“I had a heart attack near
17 Feb 2008”
“Patient suffered congestive
heart failure around
Feb 17, 2008”
“John experienced a lateral
myocardial infarction on
2008-02-17.”
How the data is needed…Where the data is… Semaphore
Natural
Language
Processing
Entity
Extraction
Ontologies

Accommodate variations efficiently
Market Value
Anchors detect key phrases for valuation.
• Configure nearness
• Use model (synonyms, relationships)
• e.g. “common stock” vs “common equity”

34
Extracting important section or paragraph from document, as well as individual facts…
Two dimensional fact
(name and role) extracted
as data
Daniel R. Coker, 62, was appointed President and Chief Executive Officer in March, 2003. He was appointed to the
Company’s Board of Directors in February, 2007, having also served on the Company’s Board of Directors from 2003 to 2004.
Mr.
Coker joined Gentherm in 1996 as Vice President of Sales and Marketing. Prior to joining Gentherm, Mr. Coker worked with
Arvin,
Inc. from 1986 through 1995 as Vice President and General Manager of North American Operations. Mr. Coker received his
bachelor’s degree from Tennessee Technological University.
Barry G. Steele, 44, was appointed Vice President Finance and Chief Financial Officer in 2004 and Treasurer in 2005.
Prior to joining Gentherm, Mr. Steele worked since 1997 in a number of senior financial management positions, including
Chief Financial Officer for Advanced Accessory Systems, LLC, a global supplier of specialty accessories to the
automotive industry. Prior to 1997, Mr. Steele worked for PriceWaterhouse LLP. Mr. Steele received a bachelor’s degree
from Hillsdale College and is a Certified Public Accountant.
Semantic models managed in
Semaphore not only inform
extractions but enable
advanced analysis
Entire paragraph extracted for review and comparison or reuse

Group data into Facts
Name
Title
Fact
Fact
Fact
Fact
Fact
Fact
Fact
Fact

Sequences and Sub-Facts
Example: extracting First Layer Premium from a reinsurance document

Benefits of Semantic AI
 Process documents faster and with greater accuracy than human operators,
with less need for manual intervention; reducing costs and the impact of staff
churn.
 Extract actionable information from unstructured content, previously beyond
the reach of conventional processing tools.
 Extend solutions to cover new document types, or even new applications for
the technology introduced, without engaging programmers to implement new
capabilities.
 Results are fully auditable and traceable.
 Mitigates risk and reduce the compliance burden.

Recognized Leader & Niche
38
A result of over a decade of innovation and commitment to quality
The Gartner Peer Insights Logo is a trademark and service mark of
Gartner, Inc., and/or its affiliates, and is used herein with
permission. All rights reserved.
Leader in Gartner’s 2018 Magic Quadrant for
Metadata Management Solutions
The growing need for organizations to inventory and leverage
data assets continues to drive strategic initiatives on metadata
management and the growth of its solutions:
Gartner Peer Insights: Metadata Management Solutions
(Reference ID: G00340701)
Niche in Gartner’s 2018 Magic Quadrant for
Insight Engines
Insight engines augment search technology with artificial
intelligence to deliver insights in context and using various
modalities derived from the full range of enterprise content and
data:
Gartner Peer Insights: Insight Engines
(Reference ID: G00328869)
Check Out What Our Customers Are Saying!

Q&A
Thanks for joining the webinar
Q A&

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Semantic AI Making Great Data and Making Data Great

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