The document details the expertise of key speakers from Discovery Machine and Concept Searching, highlighting their experience in artificial intelligence and knowledge transfer solutions. It outlines their products, including automated classification and metadata generation tools that enhance information governance and decision-making processes. The companies aim to solve the challenges of knowledge management by combining advanced technologies for effective and efficient enterprise-wide knowledge transfer.

1. Getting Knowledge Transfer Right
Enterprise Wide
Ken Lemons
VP Federal Programs
Concept Searching
kenl@conceptsearching.com
Twitter @conceptsearch
Todd Griffith
CTO and Co-Founder
Discovery Machine
tgriffith@discoverymachine.com
John Challis
Founder and CTO/CEO
Concept Searching
johnc@conceptsearching.com
Twitter @conceptsearch
Anna Griffith
CEO and Co-Founder
Discovery Machine
agriffith@discoverymachine.com

2. Expert Speakers
Todd Griffith – CTO and Co-Founder at Discovery Machine
has worked in the area of intelligent systems research for more than a decade and is widely
published on subjects that include cognitive science, human-computer interaction and
intelligent systems. His current research involves building knowledge acquisition tools that
allow subject matter experts to encode their own problem-solving strategies.
Ken Lemons – VP of Federal Programs at Concept Searching
has over 25 years’ experience in the IT industry, with a track record in consulting, solutions
delivery, sales and project management in the federal sector. He has managed Microsoft
consulting practices for several US government integrators, latterly as VP of Business
Development for Air Force and DoD programs. Ken has provided US DoD agencies with
solutions to address a range of challenges, leveraging a combination of Microsoft and third
party technology solutions.
John Challis – Founder and CTO/CEO at Concept Searching
is an experienced entrepreneur, and has had success with several ventures involving the
management of unstructured data. In 2002 he founded Concept Searching, and under his
technical expertise introduced statistical search and classification products that utilize
compound term processing to identify concepts within unstructured information.
Anna Griffith – CEO and Co-Founder at Discovery Machine
has more than 10 years’ experience in the commercialization of AI based software solutions.
She developed Discovery Machine’s methodology for knowledge capture which leverages
cognitive science and AI research to help individual experts in their articulation required for
both knowledge capture and automation. This methodology has proven successful in a wide
domain for both government and commercial sectors.

3. Agenda
• Introductions
• Discovery Machine
• About Us
• What We Do
• How We Differentiate Ourselves
• Demonstration
• Use Case Examples
• Concept Searching
• Approach and Technologies
• Knowledge Flow – Knowledge Transfer
• Demonstration
• Next Steps

4. • Company founded in 2002
• Product launched in 2003
• Focus on management of structured and unstructured information
• Technology Platform
• Delivered as a web service
• Automatic concept identification, content tagging, auto-classification,
taxonomy management
• Only statistical vendor that can extract conceptual metadata
• 2009, 2010, 2011, 2012, 2013 ‘100 Companies that Matter in KM’
(KMWorld Magazine) and Trend Setting product of 2009, 2010, 2011, 2012,
2013
• Authority to Operate enterprise wide US Air Force and enterprise wide
NETCON US Army
• Locations: US, UK, and South Africa
• Client base: Fortune 500/1000 organizations
• Managed Partner under Microsoft global ISV Program - ‘go to partner’
for Microsoft for auto-classification and taxonomy management
• Smart Content Framework™ for Information Governance
• Product Suite: conceptSearch, conceptTaxonomyManager, conceptClassifier,
conceptClassifier for SharePoint, conceptTaxonomyWorkflow, conceptContentTypeUpdater for SharePoint
Concept Searching – The Industry Leader in
Managed Metadata Solutions

5. About Discovery Machine Inc. (DMI)
• Company Founded in 1999
• Headquartered: Williamsport, Pennsylvania, USA
• Awards:
• Department of Defense SBIR Success Story with the Defense Advanced Research Projects
Agency (DARPA)
• Ben Franklin Technology Partners’ 2010 Most Technologically Innovative Product Award
Winner
• Finalist for the 2013 Pennsylvania's Small Business Governor's Award
• US Patents:
• US Patent 7,257,455 B1 - System & Method for Collecting & Representing Knowledge
• US Patent 7,757,220 B2 - Computer Interchange of Knowledge Hierarchies
• US Patent 7,958,073 B2 - Software Methods & Methods for Task Method Hierarchies
• US Patent 8,019,716 B2 - Reflective Processing of Computer Hierarchies
• Products:
• Knowledge Service Modeler
• Behavior Modeling Console for VBS2
• Maritime Console
• Behavior Creation Toolkit
• Virtual Training Coaches

6. About Discovery Machine Inc. (DMI) Cont.
• Capture and Deploy
• Adaptable Expertise
• Enable better decision making
• Take action

7. Unstructured
Text
Enter
Text
Search
DATA
Classic Lessons
Learned System
Information
Ontology
Enter
Data
Search
Classify Manage
Ontology Based
Lessons Learned
SME
Formal/
Deployable
Models
Solutions
Enter
Best
Practices
Search
Knowledge
Coordinator
Adaptation via
Critics
Deployable
Best Practices

8. Word
Excel
Xml
html
1. Umbrella
2. Conceptualize - Tell stories & sketch process
3. Formalize - Map information to process
4. Operationalize - Create knowledge system
5. Test and Deploy - Deploy to the enterprise
1. Expert Domain Scoping - Creating
umbrella for project DMI Methodology

9. Combined Solution Differentiators
• Capture enterprise knowledge and deploy subject matter expertise
• Effective content identification and classification
• Create tangible knowledge assets
• Automatic identification
• Visual representation complex business and technical processes
that promote best practices
• Real-time decision making through dynamically updated, executable
strategies and methods
• Improved training and maintenance of organizational know-how

10. Toaster Coach Demonstration

11. Knowledge
Database
Uses
Knowledge Service Modeler
Virtual Coach
DMI Model
Uses
Retrieved Information
Queries Database to make informed decisions
&
Combined Solution

12. Manufacturing

13. Aerospace

14. Energy

15. Health Care

16. Contact Information
Website:
www.discoverymachine.com
Email:
sales@discoverymachine.com
Follow us on LinkedIn:
http://www.linkedin.com/company/discovery-machine-inc.
Like us on Facebook:
https://www.facebook.com/discoverymachine
YouTube:
http://www.youtube.com/aidiscoverymachine

17. Manual Tagging is a
Behavior Modification Problem
conceptClassifier automates the tagging process to remove the
behavior modification problem of manual tagging for Governance,
Findability and Migration
• conceptClassifier increases productivity,
improves ‘Findability’, automates
‘Governance’, migrates content
• Organizes intellectual assets and
provides a factor of improvement for
end users to find company information
with an automated tagging approach
for any search platform
• Improves ability to target content
through EMM/TS with 2013 Search and
SharePoint 2010/2013
• Improves SharePoint Portal Adoption
• Aligns content with governance polices
and federally mandated requirements
• Intelligently migrates content into and
out of SharePoint
• Mitigates Risk
• Reduces federal governance, corporate
information policy, and personally
identifiable information exposures while
improving eDiscovery audit capabilities
and ensuring alignment with content
retention policies
• Lowers Cost of Administration
• Leverages native integration to
Microsoft stack, manages migration of
GUIDS
• Lowers cost of ownership to build out
and administer EMM/Term Store
• Intelligent content migration
• Intuitive user interface for easy
product adoption

18. A Manual Metadata Approach Will Fail 95%+ Of The Time
Issue Organizational Impact
Inconsistent Less than 50% of content is correctly indexed, meta-tagged or
efficiently searchable rendering it unusable to the organization. (IDC)
Risky 59% of middle managers miss valuable information every day
because they can’t find it or never see it (Accenture)
Cumbersome - expensive Average cost of manually tagging one item runs from $4 - $7 per
document and does not factor in the accuracy of the meta tags nor the
repercussions from mis-tagged content. (Hoovers)
Malicious compliance End users select first value in list.
(Perspectives on Metadata, Sarah Courier)
No perceived value for end user What’s in it for me? End user does not see value for organization nor
risks associated with litigation and non- conformance to policies. Less
than 14% of end users receive training. (AIIM)
What have you seen Metadata will continue to be a problem due to inconsistent human
behavior.
The answer to consistent metadata is an automated approach that can extract the meaning
from content eliminating manual metadata generation yet still providing the ability to manage
knowledge assets in alignment with the unique corporate knowledge infrastructure.
Manual Approach Leads to Failure

19. Intelligent Knowledge Flow – Expert Knowledge Transfer
• Discovery Machine and Concept Searching have combined
artificial intelligence with expert knowledge, metadata generation,
auto-classification, and taxonomy management to deliver expert
knowledge transfer
• conceptClassifier
• Automatically generates semantic metadata
• Auto-classifies content
• Provides the most relevant and accurate information for Discovery
Machine's Knowledge Service Modeler

20. Benefits
• Reduce costs, risks, and time associated with information governance,
records management, data privacy, and government mandates
• Improve information governance - find trusted and relevant information
on health and safety, asset maintenance, compliance guidelines
• Effective content identification and classification, regardless of content
location in the enterprise
• Eliminate the error and cost burdens of identification and management
of records
• Document knowledge in the context of business process to create
tangible knowledge assets
• Increase the quality of information for decision making
• Automatic migration of content driven by classification of metadata

21. • Metadata driven application and enforcement of policies - conceptClassifier has been
deployed since 2003 to automatically generate metadata and use that metadata to apply and enforce
policies. Most clients are using the platform to support their information governance strategy.
• Proven, mature functionality out of the box - The platform has been deployed in numerous sites
and applications across the enterprise, including SharePoint 2007, 2010, 2013, Office 365, Documentum,
Hummingbird, SQL Server, Oracle, File Shares, and web sites.
Smart Content Framework™
Getting It Right

22. • Concept Searching’s statistical concept identification underpins all technologies
• Multi-word suggestion is explicitly more valuable than single term suggestion algorithms
• conceptClassifier will generate conceptual metadata by
extracting multi-word terms that identify ‘triple heart bypass’
as a concept as opposed to single keywords
• conceptTaxonomyManager uses statistical concept
identification to provide real-time feedback during the process
of building, testing, refining, and deploying taxonomies
• Metadata can be used by any search engine index or any
application/process that uses metadata.
Concept Searching
provides Automatic
Concept Term Extraction
Triple
Baseball
Three
Heart
Organ
Center
Bypass
Highway
Avoid
Industry Unique Technology

23. Concept Searching
Demonstration

24. What’s the End Result?
• Concept Searching has incorporated automatic semantic metadata
generation, auto-classification, and taxonomy management into
Discovery Machine’s Knowledge Service Modeler
• Virtual coaches provide guidance and actionable knowledge through
the use of artificial intelligence, automatic generation of semantic
metadata, auto-classification, and taxonomy management capabilities
• Combined leading-edge technologies
• Unique solution
• Find full details here
For a comprehensive demo of the combined solution and discussion of
expected ROI, please contact Ken Lemons at Concept Searching
or Todd Griffith at Discovery Machine

25. Thank You
Ken Lemons
VP Federal Programs
Concept Searching
kenl@conceptsearching.com
Twitter @conceptsearch
Todd Griffith
CTO and Co-Founder
Discovery Machine
tgriffith@discoverymachine.com
John Challis
Founder and CTO/CEO
Concept Searching
johnc@conceptsearching.com
Twitter @conceptsearch
Anna Griffith
CEO and Co-Founder
Discovery Machine
agriffith@discoverymachine.com