Enterprise Semantic Technology

©2014, Cognizant
Enterprise Semantic Technology
Industrializing Your Organization's
Semantic Technology Platform
SPEAKER:
Thomas Kelly, Practice Director
Semantic Technology Center of Excellence
Enterprise Information Management
Cognizant Technology Solutions, Inc.

1 | ©2014, Cognizant
Cognizant Technology Solutions
20,000+ Projects in
40 countries
.………………………...Founded in 1994
(CTSH, Nasdaq)
………………………..
Headquarters
Teaneck, NJ USA
……………….…….
…………………….
25+ Regional
sales offices
………………….…………..….…..
………………………
75+ Global Delivery
Centers
……………….…...
Revenue
$8.84b in 2013 (up 20.4% YOY)
Q1 2014 – $2.42b
.
178,600+
employees (Mar 2014)
…………….…...
. . . .…………………..
Revenue Mix
NA: 77%, Europe:19%, RoW: 4%
1,223
active customers

Our Portfolio Across Industries
HEALTHCARE & LIFE SCIENCES
27 of the top 30 Global Pharmaceutical Companies
8 of the top 10 U.S. Healthcare Plans
9 of the top 10 Biotech Companies
2 of the top 5 Medical Device Companies
INSURANCE
7 of the top 10 Global Insurers
33 of the top 50 US Insurers
BANKING & FINANCIAL SERVICES
6 of the top 10 North American banks
8 of the top 10 European banks
MANUFACTURING,
LOGISTICS, ENERGY & UTILITIES
7 of the top 10 Automotive OEM
4 of the top 15 Industrial Manufacturers
4 of the top 15 Chemical Manufacturers
4 of the top 14 Logistics Providers
RETAIL, TRAVEL & HOSPITALITY
9 of the top 30 Global Retailers
2 of the top 4 Global Distribution System Companies
3 leading U.S. Airlines
3 of the world’s leading Restaurant Chains
INFORMATION, MEDIA &
ENTERTAINMENT
4 of the top 10 Information Service
Companies Worldwide
4 of the top 10 Global Media Companies
6 of the major U.S. Movie Studios
TECHNOLOGY
4 of the top 5 Online Companies
7 of the top 10 ISVs
2 of the top 5 Semiconductor
Manufacturers
COMMUNICATIONS
7 of the top 10 Communications
Service Providers & Equipment
Vendors

Many Organizations are at an Inflection Point
BusinessImpact
Time
Project-based
Semantic Technology
Engagement and Execution
Enterprise

Definition
industrialize
To manufacture on an industrial scale or using industrial methods
yourdictionary.com
Our point of view:
To engage people, practices and methods, and technologies that
provide a repeatable, predictable, consistent, time-efficient, and
cost-effective result

Semantic Industrialization Pyramid
Technology
• Data Stores
• Cross-Technology Integration
• Query and Analytics
• Access and Security
Practices and Methods
• Data Governance
• Knowledge Representation
• Data Acquisition / Onboarding
• Data Quality / Curation
• Data Publication (Sharing)
People
• Champions and
Stakeholders
• Communities of Interest
• Data Suppliers and
Consumers
• Semantic Technology Team

Agenda
• Semantic strategy and roadmap
• Semantic technology competency center
• Align project and data governance objectives
• Semantic technology platform and practices
• Use and extend industry ontologies
• Leverage internal and external data assets
• Define reference architecture models to guide
project teams
• Open access to data while securing those data
assets from unauthorized users
• Rapid model-based data integration

Semantic Strategy and Roadmap
Business
Discovery
Environment
Evaluation
Consensus
Building
Communicate
Roadmap
Approach
• Interview stakeholders in relevant business units
• Analyze the organization’s opportunities and challenges
• Define alignment between business needs and semantic solutions
• Determine resourcing requirements (time, funding, staffing, infrastructure)
• Test and fine-tune recommendations with champions and stakeholders
• Describe the strategy , its outcomes, and how it will be achieved
Outcomes
• Champion and stakeholder support for success
• Plans for funding, resourcing, and delivery
• Senior management will be able to see tangible benefits at the end of
every milestone mentioned in the roadmap
• Clear description of how semantic technology will contribute to the
organization’s success
Planning

Key Enablement Themes of your Strategy
Semantic Technology is the Enabling Foundation for Business Agility

Key Roles that will Influence the Success of Your Strategy
Communities of Interest
• Future stakeholder –
“disinterested (unbiased)
party”
• Often new to semantic
technology
• Approach: Engage and
educate
Stakeholders
• Success is influenced by the
semantic strategy
• May be new to semantic
technology
• May influence (or control)
resourcing the strategy
• Often “risk conscious”
• Must be “sold” on the value
of the semantic strategy
• Approach: Educate,
demonstrate success,
manage risk, overcome hard-
to-solve challenges, achieve
high-value ROI
Champions
• Stakeholder
• View semantic technology as
enabler of their future
success
• Demonstrate their support
• Help you to communicate and
“sell” the semantic strategy
• Approach: Align with
successes, position as
innovators, recognize their
support

Roadmap Scenario
1
30/60/90 Day Plan
• Services
• Skills Development
• Processes/Methods
• Technology
2
Build Team / Technology
• Staffing
• Training
• Technology Procurement
• Establish Success Metrics
3
Execute Project(s)
• Define Data Requirements
• Build Application Ontology
• Map Source Data to Ontology
• Construct Semantic Queries
4
Create Shared Ontology
• Identify Domain Concepts
• Define Common Vocabulary
• Describe Data Relationships
• Build Shared Ontology
5
Create Linked Data
• Define Use Cases
• Populate RDF Database(s)
• Map / Load Data Links
• Execute Validation Queries
6
Enterprise Data Integration
• Prioritize Domains
• Create Domain Ontologies
• Map Data Assets to Ontologies
• Enrich Data with Data Links

Roadmap Scenario
1 30/60/90 Day Plan
• Organize Community of Interest
• Select Project(s) to Execute
• Services
• Project Ontology Services
• Team / Skills Development
• Job Classification Development
• Ontology Modeling
• Controlled Vocabularies
• SPARQL, R2RML
• Processes/Methods
• Validating Ontologies
• Managing Industry Ontologies
• Semantic Query Performance
• Technology
• Ontology Editor
• Data Profiling
• Relational-to-RDF Mapping
• Automated Ontology Generation
• SPARQL-based Visualization
• RDF Database
2 Build Team / Technology
• People
• Educate Stakeholders, Champions,
Community of Interest
• Semantic Team
• Project Team(s)
• Skills Development
• Mapping Relational Data to an
Ontology Model
• SPARQL Data Query and Management
• Inferencing
• Technology Procurement
• Servers, Software, Network
• Establish Success Metrics
• Coverage of Projects’ Business
Requirements
• Support of Projects’ Performance
Requirements
• Speed-to-Business Value
• Support for Model Updates

Roadmap Scenario
3 Execute Project(s)
• Define Data Requirements
• Current Data Rules
• Industry-defined Data Rules
• Future Data Requirements
• Build Application Ontology
• Define Vocabulary
• Define Standard Properties
• Define Classes, Properties, and
Relationships
• Map Source Data to Ontology Model
• Construct Semantic Queries
• Create Inferencing Rules
• Visualize Data
• Validate Functionality and
Performance
• Define Cache / Persistence
Candidates
• Define and Develop Persistence
Structures
• Deploy and Train User Community

Agenda
Semantic strategy and roadmap
• Semantic technology competency center
project teams

Semantic Technology Competency Center
Data
Governance
Domain
Expertise
Ontology
Modeling
Capture and
Integrate
Expertise
Align with the
Organization’s
Data Strategy,
Objectives, and
Standards
Describe, Organize,
and Connect Data and
Knowledge Assets
Defining the Knowledge Capture and Management Services

Semantic Technology Services
Project Services
• Model-based Data Movement
• NLP / Semantic Search
• Linked Data Integration
• Curation Automation
Ontology Management Services
• Controlled Vocabularies
• Business Rules and Inferencing
• Ontology Integration/Rationalization
• Provenance
• Integrating Semantic Modeling with
Data Governance Activities
Infrastructure Services
• Capture and Validate Internal
Knowledge
• Knowledge Representation
• Validation Methods
• Embedding Expertise in Information
Management
• Business Rules in Ontology Models
• Frequently Used or Standard Analytics
Strategic / Enterprise Services Project Services
Ontology Management Services Domain Expertise
• Semantic Strategy and Roadmap
• Prioritizing and Building Enterprise
and Business Unit Models
• Establishing Standards for use of
Industry Ontologies
• Enterprise Data Integration
• Data Asset Cataloging, Search, and
Authorization

Key Roles and Responsibilities
• Semantic Strategy & Roadmap
• Establishing Standards for use of
Industry Ontologies
• Integrating Semantic Modeling with
Data Governance Activities
• Prioritizing Knowledge Capture and
Analysis
• Capture and Validate Internal Knowledge
• Defining Business Unit Models
• Capturing Domain Expertise in Information Management
• Embedding Domain Expertise in
Information Management
• Business Rules in Ontology Models
(Data Quality and Security Rules)
• Frequently Used or Standard Analytics
• Model-based Data Movement
• Data Curation Automation
• Linked Data Integration
Data Governance
Ontologist
Business Analyst
Semantic Developer
• Semantic Reference Architecture(s)
• Data Integration Solution Architecture(s)
• Transaction Models
Semantic Architect

Ontology Development Services for Projects
Establish
Scope
Discovery
Ontology
Modeling
Model
Validation
Map to
Data
Assets
Visualize
• Define Subject
Areas
• Identify and
Recruit Domain
Experts
• Prepare
Interview /
Validation
Schedule
• Conduct
Discovery
Sessions with
Domain SMEs
• Define Facts and
Rules
• Document
Findings
• Track Requests
for Changes
• Identify Related
Ontologies for
Inheritance
• Construct New
Classes
• Add New
Attributes and
Relationships
• Construct Rules
Logic
In some cases, the Ontology Modeling and Model Validation steps may be conducted during the
Discovery session(s), delivering a validated model more rapidly than traditional approaches.
• Create Model-
Specific Test
Cases
• Define and Build
New Model
Validation Rules
• Perform
Automated
Model
Validation
Checks
• Define Mapping
Between Source
Data Asset and
Ontology Model
• Create Data
Element-Level
Mappings
• Create and
Execute Test
Cases
• Create Sample
Visualizations
through Model-
based Queries
• Review
Visualizations
with Domain
Experts and
Project
Stakeholders
• Track Requests
for Changes

Agenda
Semantic technology competency center
project teams

Evolutionary Modeling for Enterprise Data Governance
App A
Database
Application-
Specific
Ontology
1
App C
Database
App B
Database
App D
Database
Application-
Specific
Ontology
2Application-
Specific
Ontology
3
1 An application project maps an existing database to an ontology, providing
semantic access to selected data elements
2 Another application project maps data elements from multiple databases to an
ontology, providing semantic integration and access to the relational data
3 A third project maps the data elements to a data organization that meets their
project needs, without changing the structure of the underlying data
Individual projects will independently create ontologies. These ontologies will
focus on supporting a specific business process, but may re-engineer the same
concepts, leading to ontology proliferation.
Industry
Ontology

Evolutionary Modeling for Enterprise Data Governance
Application-
Specific
Ontology
App A
Database
Application-
Specific
Ontology
App C
Database
App B
Database
App D
Database
Databases
Application-
Specific
Ontology
Applications
1 2
3
Inherits and
Extends
North America
R&D
Ontology
Departments
4
Enterprise
OntologyEnterprise Inherits and
Extends
7
Inherits and
Extends
International
Market
Ontology
8
Industry Industry
Ontology
Inherits and
Extends
9
North America
Commercial Ops
Ontology
Inherits and
Extends
5
North America
Market
Ontology
Geographical
Business Units Inherits and
Extends
6
Inherits and
Extends
Each remaining ontology
retains the concepts that
are unique to their domain
Common concepts
are promoted to
the highest level
ontology in which
they are shared

Governance-Driven Ontology Management Extends the
Organization’s Data Ecosystem
Ingest new data
sources (light
integration and
curation)Reuse Expertise
Identify and leverage
existing, relevant data
assets and expertise
Analyze
Extend
Create and extend data
relationships,
leveraging insights
from previous study
cycles
Refine
Capture insights from new data
analysis cycles, refining
relationships to support new
analytics
Govern
Elevate proven data,
relationships, and expertise
to organization-wise
definition
Monitor and measure
use and benefits
achieved; identify next
set of priorities
Realize
Benefits

Agenda
Align project and data governance objectives
project teams

Using and Extending Industry Ontologies
Evaluation Criteria
1. Fitness for a planned purpose
2. Industry adoption history and potential
3. Sponsoring standards body’s support
and direction
4. Timing of a superceding ontology
Using the Industry Ontology
1. Identify domains and concepts relevant
to the business
2. Execute proof-of-concept project to
evaluate ontology concepts in real world
use
3. Identify and evaluate abstraction
methods (if needed)
1. Rename concepts
2. Mask/hide non-relevant concepts
4. Define adoption plans
5. Educate user community
6. Map data in current systems to the
industry ontology
Extending the Industry Ontology
1. Identify gaps in the industry ontology
2. Document business rules for new /
revised concepts
3. Define naming convention for new /
revised concepts
4. Create ontology model that references
the industry ontology
5. Define / design new / revised concepts
6. Validate the new ontology model

Extend existing investments in relational technology while
delivering smart applications
Organizations have invested
billions in relational technology
Relational technology powers high-
performance transaction systems
Speed parity is good… but getting
the job done faster is better
Semantic technology bridges relational
databases with semantic features to
help organizations to transition what
they want, when they want it.

Use Case – Hospital Supply Mobile Sales App
Skyland Children’s Hospital
1123 Hillcrest Drive
Washington, DC
Primary Contact
Silas Monroe, M.D.
Account
Diagnostic
Aug 20, 2014 -- As the Affordable Care Act takes effect
and healthcare shifts from a fee-for-service based
model to a value-based one, leading hospitals and

Use Case – Hospital Supply Mobile Sales App
Hospital
Supply
Mobile
Sales App
SPARQL
Access
Point
Ontology
Model
R2RML
Mappings
CRM
System
Sales
Mgmt
Order /
Inventory
Newsfeed
to RDF
Mapping
Benefits
• Fast integration of federated
relational data and public data
• No data mart required just to
manage data integration

The Imperative for Universal Data Access
Increasing need for access to the right data at the
right place at the right time
Organizations’
environments and
processes change
frequently and
unpredictably
There is an unmet
need to connect and
engage cross-
organizational data
There is more data
in more places and
in more formats
than ever before
Business
Units R&DPartners
Commercial
and Public
Data Publishers
Customers
Distribution
Network

Semantic Enterprise Data Integration
R&D Manufacturing Finance
Sales &
Marketing
Administration
Enterprise
• Small Model to Demonstrate Value
at the Business Unit Level
• Evolutionary Modeling provides
Incremental, Continuous Improvement
• Domain Expertise is Added to the
Models to provide Descriptive,
Predictive, and Prescriptive Insights
• Data Governance Guides the
Definition of Shared Data
• Externally-Hosted Data can be
Mapped to Business Unit and
Enterprise Models for Easy Access
Business Unit Level Models can use Vocabulary and
Data Organization that Best Fit their Operations

Leveraging External Data Assets
(2) Replicated, Internally Federated
External
Databases
Firewall
(3) Internally Merged
External
Databases
Firewall
Characteristics
(1)
Externally
Federated
(2)
Replicated,
Internally
Federated
(3) Internally
Merged
Data Location
Some or all
datasets reside
outside the
firewall
All datasets
reside inside the
firewall
All data resides in
a merged, shared
database
Data Integration
Internal and
External
Internal Internal
Data Latency
Data is updated
on system of
record’s
schedule
Data is
replicated/
refreshed on
internal schedule,
but still
dependent on
systems of
record’s schedule
Data is
replicated/
refreshed on
internal schedule,
but still
dependent on
systems of
record’s schedule
Query / Analysis
Performance
Performance
dependent on
external
systems of
record’s
infrastructure
Performance
dependent on
internal
databases’
infrastructure
Performance
dependent on
internal merged
database‘s
infrastructure
External
Databases
Firewall
(1) Externally Federated
• Internal management of external data
can address performance concerns for
infrequently updated small- to mid-
size external databases
• Physical integration (option 3) may
achieve a specific performance or
management benefit

Agenda
Use and extend industry ontologies
Leverage internal and external data assets
project teams

Define Reference Architecture Models to Guide Project
Teams
Technology Products
Define product features that
enable the data ecosystem
• Technology Product Features
• Supported databases and data
structures
• Semi-structured and unstructured
data
• Supported access methods
(SPARQL, API, web services)
• Modeling tools
• Data caching options
• Fit with Current/Planned Architecture
• Benchmark of Representative
Transactions
Reference Architecture
Transaction Models
Define fit-for-purpose integration of
technology products to meet transaction
requirements
• Transaction Requirements for Execution
Frequency, Throughput, Response Time
• Source Data Profile
• Formats, Latency
• Frequency and Volume of Updates
• Critical Processing Time Windows
• Source System Impact
• Performance Profiles for Representative
Transactions
• Data and Query Results Caching Techniques
• High Availability / Failover Options
Bring Best-of-Breed Technology Products to Data Management and Delivery

Industrialize Your RDF Data Store
What are your requirements?
• Planned Data Volumes
• ACID Properties
• Runs on Multi-Server Platform
with Load Balancing
• Runs on a Cloud Platform
• Parallel Processing
• Connectors to Relational,
Document, and other Data
Management Technologies
• Backup and Recovery
• High Availability
• Automatic Failover
• User-, Role-, Class-, and Rule-
Based Security
• Product Support (8x5, 24x7)
• Track Record of Quality Product
Releases
• Match products against your requirements
(platforms, resilience, product functionality)
• Configure your infrastructure (servers,
storage, network) to the performance
requirements of your workload
• Plan for vendor support (operating hours,
response time, locations, language support)
• Performance test RDF data store products
• Test your suite of transactions, rather than just
comparing standard benchmarks
• Test on your planned platforms and connected
technologies
• Talk to other customers about their
experience with the products

Industrialize Your Data Quality Management
Evolution of Requirements for Data Quality Rules
Traditional
One-Size-fits-All
Implementation
The Organization’s
Data Quality Rules
Unit A
Rules
Unit B
Rules
Shared
Rules
Second
Repository
Unmet
Demand

Industrialize Your Data Quality Management
Evolution of Requirements for Data Quality Rules Processing
New
Data
Onboard
New Data
Data
Quality
Tests
Evaluation
Results
Effective when data is
mostly static, and data
quality is consistent
Internal
Data Store
New
Data
Dynamic Data
Quality Management
Query-level
Data Quality
Tests
Ontology model contains data quality
rules that are executed at query time.
Business unit models may contain rules
that are specific to their requirements.
New
Data
Query
Results
And / or
Unfiltered
Internal
Data Store
New
Data
Regular
Data Loads
Data
Quality
Tests
Addresses variable
data quality with a
consistent set of rules
Filtered

Opening Access to Data while Securing those Data Assets
from Unauthorized Users
Use Internet security features, including certificates, to
authenticate and authorize users
Leverage RDF data store security features
Build a semantic model that defines security rules and
access for groups, roles, and users. Direct queries through
the security model.
Build semantic models for specific user groups, defining
only the objects and properties that the user groups are
authorized to access

Agenda
Use and extend industry ontologies
Leverage internal and external data assets
Define reference architecture models to guide
project teams
Open access to data while securing those data

1. Define Preliminary Objectives
1. Discuss Functional and Timing Objectives, and
Priorities
2. Clarify Immediate, Short-Term, and Long-Term
Business Value (SMART *)
a. Cost Reduction/Avoidance
b. Meet Critical Customer Need
3. Is This the Right Solution?
4. Set Expectations
a. Evolutionary Process
b. Initial Results Quickly
c. Frequent, Active Participation
d. Feedback Critical to Making Refinements
5. Brainstorm Deliverables that Produce Business
Benefits; Define a Few Sample Queries
6. Ask for Commitment to Benefits Realization
7. Start the Clock!
* SMART -- Specific, Measurable, Attainable, Realistic, and Traceable

2. Profile the Data
Light Profiling focusing on
Understanding Key Data Elements
Identify Initial Data Filtering
Candidates
Capture Insights about Key Data
Relationships

3. Generate the Initial Ontology for the New Data (if
necessary)
Reverse-engineer Ontology from
New Data
Load New Data into the RDF Store
(or Create Link to the Data)
Create Business-relevant Synonyms
for High-Importance Attributes
Refinements will be made in
Future Iterations

4. Generate the Initial Ontology for the Existing Data
(if Necessary)
Existing
Data
New
Data
Ontology Models
Map Selected Entities and Critical
Attributes for Existing Data Source(s)
to the Source-specific Ontology
Add Reference to the Source-specific
Ontology to the New Data Ontology
Model
Refinements will be made in
Future Iterations
New Data Ontology manages
integration with Existing Data until the
ontology is sufficiently mature to be
promoted into an enterprise ontology

5. Integrate Entities over Common URIs
Different URIs, Separately
Maintained
Focus on Key Entities
Equivalence Functions Logically
Integrate the Federated Data
Reduces Query Complexity and
Can Improve Query Performance

6. Create URI Links
Links Reduce Query Complexity
and Can Improve Query
Performance
The Data has Common Values that
can be used in Join Operations, but
doesn’t have Links
Focus on Key Queries, Identify
Complex or Time-Sensitive Joins
Add Linking URI Attribute to
Dependent Entity
Amend Selected Queries to
Leverage the New Link
cust:ZipCode
geo:ZipCode
JOIN
Customer Geography
cust:ZipCodeURI
LINK
Customer Geography

Summary
Enterprise Semantic Technology
• New Solutions – Solve previously hard to solve challenges
• Accelerate Benefits – Deliver business value sooner
• Better Engagement – Champions and stakeholders support
execution, while communities of interest prepare to engage
• Reduce Risk – Provide more-predictable execution, and
increasing likelihood of successful delivery

Questions?

Speaker
Thomas (Tom) Kelly
Practice Director, Enterprise Information Management,
Cognizant
Thomas Kelly is a Director in Cognizant’s Enterprise Information
Management (EIM) Practice and heads its Semantic Technology
Center of Excellence, a technology specialty of Cognizant Business
Consulting (CBC). He has 20-plus years of technology consulting
experience in leading data warehousing, business intelligence and
big data projects, focused primarily on the life sciences and
healthcare industries. Tom can be reached at
Thomas.Kelly@cognizant.com and at @tjkelly3va.
For more information, check out our website at www.smartEIM.com

Enterprise Semantic Technology

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