The State of SICoP

1
The State of SICoP

• Brand Niemann (US EPA), Chair,
• Semantic Interoperability Community of Practice
  (SICoP)
• Best Practices Committee (BPC), CIO Council
• May 17, 2005 (Updated June 14, 2005)
• http//web-services.gov/ and
• http//colab.cim3.net/cgi-bin/wiki.pl?SICoP

2
Overview

• 1. Organization
• 2. White Paper Series
• 3. Workshops and Conferences
• 4. Pilots
• Appendix 1 FEA-RMO Tutorial
• Appendix 2 Dynamic Knowledge Repository

3
1. Organization

• The Semantic Interoperability Community of
  Practice (SICoP) Grew Out of the First Semantic
  Technologies for E-Government Conference, White
  House Conference Center, September 8, 2003.
• SICoP Was Chartered by the Knowledge Management
  Working Group of the CIO Councils Best Practices
  Committee in March 2004.
• SICoP was co-chaired by Rick Morris, U.S. Army,
  Office of CIO, and Brand Niemann, U.S. EPA,
  Office of CIO, until April 2005.
• Rick Morris has retired from government service
  and a new co-chair has been identified and
  invited and is requesting the support of their
  organization.

4
Rick (Rodler F.) Morris, U.S. Army, Office of the
CIO
By SICoP Co-Chair, Brand Niemann, U.S. EPA, and
SICoP Members
5
2. White Paper Series

• SICoP is Producing Three Best Practices
  Modules
• (1) Introducing Semantic Technologies and the
  Vision of the Semantic Web
• Completed - delivered February 28th
• (2) The Business Case for Semantic Technologies
• Mills Davis, SICoP Module 2 Team Lead
• Interim delivered April 8, 2005, and final is
  scheduled for the Fall 2005 Conference
• (3) Implementing the Semantic Web
• Mike Daconta, SICoP Module 3 Team Lead
• In process for interim report at the Fall 2005
  Conference

6
2. White Paper Series

• This set of white papers is the combined effort
  of KM.Gov and SICoP.
• The papers will make the case that these
  technologies are substantial progressions in
  information theory and not yet-another-silver-bull
  et technology promising to cure all IT ills.
• The papers are written for agency executives,
  CIOs, enterprise architects, IT professionals,
  program managers, and others within federal,
  state, and local agencies with responsibilities
  for data management, information management, and
  knowledge management.

7
Module 1 Introducing Semantic Technologies and
the Vision of the Semantic Web

• This white paper is intended to inform readers
  about the principles and capabilities of semantic
  technologies and the goals of the Semantic Web.
• It provides a primer for the field of semantics
  along with information on the emerging standards,
  schemas, and tools that are moving semantic
  concepts out of the labs and into real-world use.
• It also explains how describing data in richer
  terms, independent of particular systems or
  applications, can allow for greater machine
  processing and, ultimately, many new and powerful
  autonomic computing capabilities.

8
Module 1 Introducing Semantic Technologies and
the Vision of the Semantic Web

• This white paper focuses upon applications of
  semantic technologies believed to have the
  greatest near-term benefits for agencies and
  government partners alike.
• These include semantic web services, information
  interoperability, and intelligent search. It also
  discusses the state and current use of protocols,
  schemas, and tools that will pave the road toward
  the Semantic Web.
• Takeaways We want readers to gain a better
  understanding of semantic technologies, to
  appreciate the promises of the next generation of
  the World Wide Web, and to see how these new
  approaches to dealing with digital information
  can be used to solve difficult information-sharing
  problems.

9
Module 1 Introducing Semantic Technologies and
the Vision of the Semantic Web

• Acknowledgements
• Executive Editors and Co-Chairs
• Special Recognition to Outgoing Co-Chair Rick
  Morris
• Managing Editor, Editor, and Copy Editor
• Special Recognition to Editor Ken Fromm
• Primary Contributors and Contributors
• Reviewers
• Leadership of the Best Practices and Architecture
  and Infrastructure Committees
• Supporting Agencies and Organizations

10
Kenneth R. Fromm, Loomia, Inc.
By SICoP Co-Chairs, Rick Morris, U.S. Army,
Brand Niemann, U.S. EPA
11
2. White Paper Series

• Module 2 The Business Case for Semantic
  Technologies
• Preliminary Research
• http//www.project10x.com/downloads/topconnexion/B
  usinessValue_v2.pdf
• Interim Report at the SWANS Conference
• Global investment to develop semantic
  technologies by governments, venture capital, and
  industry will approach 15 billion this decade.
  Semantic solution, services and software markets
  will top 50B by 2010.
• More than 150 ITC companies have semantic
  technology RD in progress, including most major
  players. 65 offer products.
• Semantic technologies are crossing the chasm to
  mainstream use. Early adopter research documents
  2 to 10 times improvements in key measures of
  performance across the solution lifecycle.
• Presentation slides available at
  http//www.project10x.com/downloads/topconnexion/M
  D_BizValue2005_SWANS.pdf

12
2. White Paper Series
13
Example-1 Information in Context
14
Example-1 Information in Context
15
Example-1 Information in Context
16
Example-1 Information in Context
17
Example-1 Information in Context
18
Example-1 Information in Context

• Best Practice Example for the SWANS Conference
• Digital Harbor
• Composite Application Solution EII, SOA, and
  Portals
• 6 years and over 50M in investment RD and over
  100M in partner RD
• Supports 22 industry standards including OWL
• Delivered 24 business templates over six domains
• Government customers include Navy, Air Force,
  NSA, DISA, DIA, NRO, NGA, CIFA, and DHS
• The most exciting thing Ive seen since Mosaic.
  Vinton Cerf, Father of the Internet.
• Collaboration Between TopQuadrant and Digital
  Harbor on a Composite Application Pilot for the
  Federal Enterprise Architecture Solution Space
  (see next slides)

19
Example-1 Information in Context
20
Example-1 Information in Context
See Semantic Technology Pilots Semantic
Mapping/Harmonization of the PRM, PART, and OMB
300 in the Best Practices Repository at
http//web-services.gov
21
2. White Paper Series

• Module 2 The Business Case for Semantic
  Technologies Final Report Outline
• 1.0 Executive Summary
• 2.0 About the Report
• 3.0 Business Value of Semantic Technologies
• 4.0 Making the Business Case for Semantic
  Interoperability
• 5.0 References
• 6.0 Endnotes
• 7.0 Appendices Interviews and Case Studies

22
3. Workshops and Conferences

• Second Semantic Technologies for E-Government
  Conference, September 8-9, 2004
• XML 2004 Conference, November 14-17, 2004
  (Keynote Session Tracks)
• Semantic Interoperability Study Group for the
  Architecture Infrastructure Committee
  Leadership, October-December 2004
• Monthly Collaboration Expedition Workshops on
  Ontologies with the Architecture Infrastructure
  Committee (AIC) at NSF, December 2004, and
  February 2005
• Semantic Web Applications for National Security
  Conference Jointly with the DARPA/DAML Program,
  April 7-8, 2005
• The Third Semantic Technologies for E-Government
  Conference, Fall 2005, Washington, DC
• E-Government Interoperability in the European
  Union, November 7, 2005 (tentative)
• XML 2005 Conference From Syntax to Semantics,
  November 14-18, Atlanta, Georgia (Keynote
  Session Tracks)

23
3. Workshops and Conferences

• Sir Tim Berners-Lee at the SWANS Conference,
  April 7 on the Government Role
• Making public data available in standard Semantic
  Web formats.
• Requiring funded data to be available in Semantic
  Web formats
• Encouraging flagship applications.
• Supporting Web Science research for advanced
  tools.

24
3. Workshops and Conferences
Sir Tim Berners-Lee at the SWANS Conference,
April 7 on the constant tension
Keep a wise balance. The semantic web allows a
mixture of the two approaches, and smooth
transitions between them.
25
3. Workshops and Conferences

• Substance of the Semantic Web Deborah
  McGuinness, Stanford and Mike Dean, BBN, SWANS
  Conference, April 7, 2005
• Selected Technical Benefits
• 1. Integrating Multiple Data Sources
• 2. Semantic Drill Down / Focused Perusal
• 3. Statements about Statements
• 4. Inference
• 5. Translation
• 6. Smart (Focused) Search
• 7. Smarter Search Configuration
• 8. Proof and Trust

26
3. Workshops and Conferences

• Open Standards for Government Information
  Sharing Timing the Transformations Needed for
  Sustained Progress By Combining the Expertise of
  Multiple Communities, June 28th
• How to Build Readiness Advancing Discernment and
  Value Through Implementation Profiles and Pilots
• IRS Topic Maps with the IRS Tax Products CD-ROM
• Terragram for Taxonomies at the World Bank
• SVG Maps for Knowledge Management and Taxonomies
• Ontologies for SSAs Policy Net
• Geospatial Ontologies for the Geospatial Profile
• Taxonomies and Metadata for the Intelligence
  Community

27
3. Workshops and Conferences

• E-Government Interoperability in the European
  Union Workshop (Draft)
• European Interoperability Framework for
  pan-European eGovernment Services
• Dr. Barbara Held
• On Software Freedom and Interoperability
• Simon Phillips, Chief Technology Evangelist, Sun
  Microsystems
• Achieving Business Agility with Service
  Enablement
• Eric Newcomer, CTO, IONA Technologies
• Break and Networking
• Joining Up Government Through Incremental
  Integration A Criminal Justice Case Study
• Warren Buckley, CTO and Co-Founder, Polarlake
• Lunch
• Services-Oriented Architecture in the Government
  Sector Unlock your existing legacy assets
  without rip-and-replace
• Annrai OToole, CEO, Cape Clear Software
• Only the Data Endures
• Sean McGrath, CTO, Propylon
• Panel Session and Audience Questions

28
4. Pilots

• SICoP is Conducting Pilot Projects at the Request
  of the FEA PMO, AIC, and others
• 4.1 Formal Taxonomies for the U.S. Government
• 4.2 Ontology for Indicators
• 4.3 Federal Enterprise Reference Model Ontology
  (FEA RMO)
• 4.4 Building a National Health Information
  Network Ontology
• 4.5 Public Domain Databases for Semantic
  Searching and Ontology Building
• 4.6 Ontology and Taxonomy Coordinating Work Group
  (ONTACWG)
• 4.7 Composite Application Solution with Semantic
  Technologies
• 4.8 Semantic Technology Profiles for the FEA Data
  Reference Model
• 4.9 FEA Data Reference Model Use Cases

SICoP has mapped its activities to the FEA PMO
Action Plan FY 2005-2006 (see April 21st AIC
Meeting). FEA PMO Federal Enterprise
Architecture Program Management Office
29
4.1 Formal Taxonomies for the U.S. Government

• OWL Listing
• ww.w3.org/1999/02/22-rdf-syntax-ns"
  xmlnsxsd"http//www.w3.org/2001/XMLSchema"
  xmlnsrdfs"http//www.w3.org/2000/01/rdf-schema"
  xmlnsowl"http//www.w3.org/2002/07/owl"
  xmlnsdaml"http//www.daml.org/2001/03/damloil"
  xmlns"http//www.owl-ontologies.com/unnamed.owl
  " xmlnsdc"http//purl.org/dc/elements/1.1/"
  xmlbase"http//www.owl-ontologies.com/unnamed.ow
  l" rdfID"Transportation"/ rdfID"AirVehicle" rdfresource"Transportation"/
  
  
  
  "/ Etc.

Transportation Class Hierarchy
Source Formal Taxonomies for the U.S.
Government, Michael Daconta, Metadata Program
Manager, US Department of Homeland Security,
XML.Com, http//www.xml.com/pub/a/2005/01/26/formt
ax.html
30
4.2 Ontology for Indicators
Schematic of the Ontology
Indicators
Topics
Organizations
Jurisdictions
Publicly led
U.S. local/regional level
The Economy
Privately led
U.S. state level
Society Culture
Led by public-private partnership
National level outside the United States
The Environment
Supranational level
Cross-Cutting
Note that each of these classes can and do have
multiple instances underneath them, etc.
31
4.2 Ontology for Indicators
The folder names are either the ontology or the
knowledgebase instances.
See Best Practices Repository at
http//web-services.gov
32
4.3 Federal Enterprise Reference Model Ontology
(FEA RMO)

• This is a composite application with multiple
  ontologies!
• Online Version
• Coming soon at http//www.osera.gov
• Temporarily at http//web-services.gov
• Documentation
• Web Page at http//web-services.gov/fea-rmo.html
• Best Practices Repository at http//web-services.g
  ov
• Submit FEARMO comments
• http//colab.cim3.net/cgi-bin/wiki.pl?HowToSubmitF
  EARMO_Comments

33
4.3 Federal Enterprise Reference Model Ontology
(FEA RMO)
Concise Format Abstract Syntax RDF/XML Turtle
Ontology List
Ontology Hierarchy
Reasoner Pellet RDFS-like
FEA-RMO at SWANS in SWOOP 2.2.1 from MindSwap
Research Group (Jim Hendler).
34
4.4 Building a National Health Information
Network Ontology

• The Mind Map Book How to Use Radiant Thinking to
  Maximize Your Brains Untapped Potential (Tony
  Buzan)
• Before the web came hypertext. And before
  hypertext came mind maps.
• A mind map consists of a central word or concept,
  around the central word you draw the 5 to 10 main
  ideas that relate to that word. You then take
  each of those child words and again draw the 5 to
  10 main ideas.
• Mind maps allow associations and links to be
  recorded and reinforced.
• The non-linear nature of mind maps makes it easy
  to link and cross-reference different elements of
  the map.
• See next slide for examples from the Explorers
  Guide to the Semantic Web, Thomas Passin,
  Manning Publications, 2004, pages 106 and 141.

35
4.4 Building a National Health Information
Network Ontology
standards governance privacy regionalization finan
cing architecture regulation

organizational technical semantic
general organizational business management
operational standards policies financial,
regulatory, legal other
DR. BRAILER
RFI
FRAMEWORKS
STANDARDS ORGANIZATIONS
NHIN
WORK GROUPS
NCVHS CCHIT Etc.
technical architecture organization
business financial, regulatory, legal
ORGANIZATIONAL STRUCTURE
OTHER
other
STRATEGIC PLAN GOALS
regional initiatives clinical practice population
health health interoperability Federal Health
Architecture
Possible/probable interrelationships
Inform Clinical Practice Interconnect
Clinicians Personalize Care Improve Population
Health
36
4.4 Building a National Health Information
Network Ontology
See Best Practices Repository at
http//web-services.gov
37
4.4 Building a National Health Information
Network Ontology
FAST Data Search Search View
38
4.5 Public Domain Databases for Semantic
Searching and Ontology Building

• 4.5.1 Gartner Magic Quadrant for Enterprise
  Search, 2004
• 4.5.2 Gartner Analysis Leaders
• 4.5.3 FAST Data Search Categorization and
  Taxonomy Support
• 4.5.4 FAST Data Search Integration
• 4.5.5 Paradigm Shifts
• 4.5.6 Public Domain Database

39
4.5.1 Gartner Magic Quadrant for Enterprise
Search, 2004
Source Gartner Research ID Number M-22-7894,
Whit Andrews, 17 May 2004.
40
4.5.2 Gartner Analysis Leaders

• Fast Search Transfer (FAST) now is counted in
  the Leaders quadrant, moving from the Visionaries
  quadrant. The vendor has experienced explosive
  growth, providing better-than-average means and
  an expanding list of approaches of determining
  relevancy. Its architecture is superior among
  search vendors, and sales are strong. (Sales of
  enterprise search technology were 42 million in
  2003, up from 36 million in 2002.) Its
  acquisition of the remainder of AltaVista's
  business has had no real impact on operations.
• Critical questions include whether FAST will
• 1) remain a specialist in search technologies
• 2) pursue "search-derivative applications"
  FAST's term for the general application category
  founded on search platforms, including customer
  relationship management (CRM) knowledge base
  support tools and scientific research managers
  or
• 3) focus on original equipment manufacturer
  arrangements or on a broader suite of
  applications, such as those included in a smart
  enterprise suite. Search vendors typically follow
  an arc that leads to their acquiring a company,
  to failure or to a position as an enduring
  leader. FAST has the opportunity to pursue the
  last path.
• Note added by Brand Niemann FAST acquired
  NextPage in December 2004 which provides
  electronic publishing software to 6 of the 9
  leading electronic publishers in the world. I
  have used NextPage in the pilots to date.

41
4.5.3 FAST Data Search Categorization and
Taxonomy Support
42
4.5.4 FAST Data Search Integration
43
4.5.5 Paradigm Shifts

• Paradigm Shifts
• From Indicator Frameworks to
• Ontologies for and of Indicators based on..
• Enterprise Search of Everything, Everywhere!
• Explanation
• Ontologies of and for Indicators provide the
  structure that make indicators more useful and
  reveal the gaps.
• Everything, Everywhere is achieved by a Search
  Platform Architecture that supports both Crawl
  (Pull) and API (Push).
• Of special interest to EPA Childrens Health,
  Mercury, etc.

44
4.5.6 Public Domain Database
45
4.6 Ontology and Taxonomy Coordinating Work Group
(ONTACWG)

• For example, NASA has at least the groups working
  on ontologies and taxonomies that want help with
  coordination
• NASA JPL has Semantic Web for Earth and
  Environmental Terminology (SWEET)
• See http//sweet.jpl.nasa.gov/
• NASA Goddard, Central Library and Mission
  Engineering Systems Analysis Division
  (Integrate STEP, UML, and OWL)
• NASA Ames, NASA System Ontology (TopQuadrant)
• Also see The Power of Team The Making of a
  CIO, 2002, Department of Navy Chief Information
  Officer (DON CIO)
• Section 5.5 Knowledge Taxonomy
• Enterprise Knowledge Management Taxonomy and
  Ontology Framework in XML.

46
4.6 Ontology and Taxonomy Coordinating Work Group
(ONTACWG)

• ONTAC is a working group within the Semantic
  Interoperability Community of Practice (SICoP) to
  provide a mechanism for voluntary coordination of
  all activities within the Federal Government and
  among other interested parties, in developing
  Knowledge Classification and Representation
  systems such as ontologies, taxonomies, thesauri,
  and graphical knowledge representations.
• Led by Pat Cassidy, Ontologist, Mitre
• See http//colab.cim3.net/cgi-bin/wiki.pl?SICoP/On
  tologyTaxonomyCoordinatingWG

47
4.6 Ontology and Taxonomy Coordinating Work Group
(ONTACWG)

• See forum archives at http//colab.cim3.net/forum
  /
• Three separate forums
• Name ontac-forum
• Description ONTAC-WG General Discussion
• Address ontac-forum_at_colab.cim3.net
• Name ontac-dev
• Description ONTAC Taxonomy-Ontology Development
  Discussion
• Address ontac-dev_at_colab.cim3.net
• Name ontac-tool
• Description ONTAC Tools Development Discussion
• Address ontac-tool_at_colab.cim3.net

48
4.7 Composite Application Solution with Semantic
Technologies

• Train Derailment Example
• 6 January 2005, 350 A.M. Graniteville, SC
• Chlorine Tank Car Toxic Release
• Matching ResponseType with EventType
• Using Open Public Standards
• Getting the Right Information to the Right People
  at the Right Time

Source Putting Context to Work Semantic Keys to
Improve Rapid First Response, Semantic Web
Applications for National Security Conference,
April 8, 2005, Trade Show, Broadstrokes,
ImageMatters, MyStateUSA, Starbourne, and
TargusInfo.
49
4.7 Composite Application Solution with Semantic
Technologies
Event Type Ontology in Context Application in
Unicorn Workbench http//www.unicorn.com
Source Putting Context to Work Semantic Keys to
Improve Rapid First Response, Semantic Web
Applications for National Security Conference,
April 8, 2005, Trade Show, Broadstrokes,
ImageMatters, MyStateUSA, Starbourne, and
TargusInfo.
50
(No Transcript)
51
4.8 Semantic Technology Profiles for the FEA Data
Reference Model

• 4.8.1 Semantic Web
• 4.8.2 The W3Cs Semantic Web Services and Rules
• 4.8.3 Ontologies Enablers of the Semantic Web
• 4.8.4 Describing Semantics
• 4.8.5 Semantic Relationships
• 4.8.6 Semantic Webs Layered Architecture
• 4.8.7 Mapping to the Data Reference Model

52
4.8.1 Semantic Web

• Semantics not new
• Web not new
• Semantic Web putting semantics on the Web is
  new
• Sir Tim Berners-Lee
• "The Semantic Web is an extension of the current
  web in which information is given well-defined
  meaning, better enabling computers and people to
  work in cooperation.
• A new form of web content that is meaningful to
  computers that will unleash a revolution of new
  possibilities.
• OWL is an important step for making data on the
  Web more machine processable and reusable across
  applications.
• Just as databases tables are connected through
  joins, multiple distributed information
  representations can be strung together through
  semantic joins.
• Lee Lacy, OWL Representing Information Using
  the Web Ontology Language, Trafford, 2005, 282
  pages.
• See http//www.trafford.com/robots/04-1276.html

53
4.8.2 The W3Cs Semantic Web Services and Rules

• W3C Workshop on Rule Languages for
  Interoperability, 27-28 April 2005, Washington,
  D.C., USA.
• Ontology and Rules on the same level, connected,
  and both treated as data.
• W3C Workshop on Frameworks for Semantics in Web
  Services, June 9-10, 2005, Digital Enterprise
  Research Institute (DERI), Innsbruck, Austria.

54
(No Transcript)
55
4.8.2 The W3Cs Semantic Web Services and Rules

• The formal foundation of the OWL language is a
  branch of knowledge representation and reasoning
  called description logics. While this foundation
  is promising, there is a different approach to
  representation and reasoning based on rules. Its
  main advantages are
• Rule engines exist and are quite powerful.
• Rules are well known and used in mainstream IT,
  and is easier for users to learn.
• Rule systems can be seen as an extension, or as
  an alternative to OWL. The first idea is driving
  current research attempting to integrate
  description logics and rules, while maintaining
  somewhat efficient reasoning support. The latter
  idea studies the use of RDF/S in conjunction with
  rules as the basis of an alternative Web ontology
  language.

Source Chapter 5. Logic and Inference Rules in
A Semantic Web Primer, Grigoris Antoniou and
Frank van Harmelen, The MIT Press, Cambridge,
Massachusetts, London, England
56
4.8.3 Ontologies Enablers of the Semantic Web
Gruber Ontology Definition
Computer Science
Domain
Ontology
Ontology
describes
Tbox
specified by
modeled by
compliant with
Class 1
Class 2
Abox
Class 3
Class 4
Fact Instances
Conceptualization
57
4.8.3 Ontologies Enablers of the Semantic Web

• Computer Science
• Tbox vocabularies define concepts that have
  associated Abox facts that represent a
  knowledgebase.
• Thomas Gruber
• An ontology is a formal specification of a
  conceptualization.
• Deborah McGuiness
• A continuum of ontology formalisms.
• Web Ontology Language (OWL)
• An OWL-encoded web-distributed vocabulary of
  declarative formalisms describing a model of a
  domain.
• So focuses on Tbox part and extends Grubers
  definition.

58
4.8.4 Describing Semantics
Source Lee Lacy, OWL Representing Information
Using the Web Ontology Language, Trafford, 2005,
page 40.
59
4.8.5 Semantic Relationships
Source Lee Lacy, OWL Representing Information
Using the Web Ontology Language, Trafford, 2005,
page 40.
60
4.8.5 Semantic Relationships
has value for
Individual
is an instance of
restrict
Property
Class
The most important inter-concept relationships
include is an instance of (individual to
class), has value for (individual to property,
and restrictions (between) classes and properties.
61
4.8.6 Semantic Webs Layered Architecture

• Applications
• Ontology Languages
• RDF Schema and Individuals
• RDF and RDF/XML
• XML and XMLS Datatypes
• URIs and Namespaces

• Implementation Layer
• Logical Layer
• Ontological Primitive Layer
• Basic Relational Language Layer
• Transport/Syntax Layer
• Symbol/Reference Layer

OWL Full, OWL DL, and OWL Lite. Source Lee
Lacy, OWL Representing Information Using the
Web Ontology Language, Trafford, 2005, page 44.
62
4.8.7 Mapping to the Data Reference Model

• DRM Volume Strategy
• Volume 3 Description
• Volume 4 Data Sharing
• Volume 5 Data Context
• Note Volumes 4 and 5 would include examples for
  several business use cases.

• Semantic Web Standards
• RDF and RDF/XML
• RDF Schema and Individuals
• Ontology Languages (OWL-DL)
• Note Each layer depends on the layers beneath
  and uses their features to provide its capability.

See next slide for definitions DRM Volume
Strategy Version 4, April 18, 2005.
63
4.8.7 Mapping to the Data Reference Model

• DRM Volume Strategy Definitions
• Data Description This model provides the
  creation of logical data models for structured,
  semi-structured and unstructured resources.
• Data Sharing The meta-model for creating
  exchange packages between systems and the
  metadata required for federated access to data
  sources.
• Data Context The meta-model for creating
  categorization schemes (like taxonomies) for data
  assets.

64
4.8.7 Mapping to the Data Reference Model

• DRM Volume Strategy Definitions (continued)
• Meta-model
• Modeling constructs used to build the DRM models
  for data description, sharing, and context. These
  constructs have been standardized in the W3Cs
  OWL and OMGs MOF specifications.
• Resource Descriptions Same as the W3Cs Resource
  Description Framework (RDF).
• The most fundamental benefit of RDF compared to
  other meta-data approaches is that using RDF, you
  can say anything about anything. Anyone can make
  RDF statements about any identifiable resource.
  Using RDF, the problems of extending meta-data
  and combining meta-data of different formats,
  from different schemas disappear, as RDF does not
  use closed documents.
• Source Lee Lacy, OWL Representing Information
  Using the Web Ontology Language, Trafford, 2005,
  page 75.

65
4.8.7 Mapping to the Data Reference Model

• Semantic Webs Layered Architecture Definitions
• RDF and RDF/XML RDF is the model and RDF/XML is
  the XML syntax for storing the model. RDF is used
  to specify OWL instances. It is the most
  important value-added layer of the Semantic Webs
  architecture.
• RDF Schema (RDFS) RDFs vocabulary description
  language, is the a semantic extension of RDF. It
  provides the mechanisms for describing groups of
  related resources and the relationships between
  these resources.
• OWL permits the definition of sophisticated
  ontologies, a fundamental requirement in the
  integration of heterogeneous information content.
  OWL ontologies will also be important for the
  characterization of interoperable services for
  knowledge-intensive processing on the Web (e.g.,
  Grid and Pervasive Computing).
• Source Lee Lacy, OWL Representing Information
  Using the Web Ontology Language, Trafford, 2005,
  pages 83, 111 , and 133.

66
4.9 FEA Data Reference Model Use Cases

• Potential Use Cases May 16th DRM WG Meeting
  (Andy Hoskinson and Joe Chiusano)
• Purpose
• Several real-world scenarios that would benefit
  from XML-based instances.
• Tie DRM XML Schema elements to information
  processing activities.
• Introduce new uses cases for unstructured data
  (80 of government information).
• Use cases to date have focused on structured
  data.
• Titles Automated Categorization, Search
  Enhancement, Enterprise Information Integration,
  Service-oriented Application, and Database of
  Data Models.

67
4.9 FEA Data Reference Model Use Cases

• My Comments and Suggestions
• See Section 4.4 Building a National Health
  Information Network Ontology pilot for automated
  semantic concept extraction and taxonomy
  generation from unstructured text (110 MB) with
  software and expert advice from Content Analysts
  and FAST
• See http//web-services.gov/nhinrfiontology0405200
  5.ppt
• Still need professional ontologist and common
  upper ontology for vertical interoperability
  (Ontolog Forum).
• See Aquaint for a beyond taxonomy/ontology
  approach (Lucian Russell, CSC) in the
  Intelligence Community
• See http//conference.brtrc.com/aquaint_spring/
• Need to use non-XML schematic diagrams to explain
  the concepts and to make non-XML interface tools
  available to get non-XML people to use this.
• See for example, http//et.gov/
• Build on successful Web applications for
  unstructured, semi-structured, and structured
  data and information with an incremental approach
  (see next slides).

68
4.9 FEA Data Reference Model Use Cases
69
4.9 FEA Data Reference Model Use Cases
This service will retrieve a Web page and
automatically generate Dublin Core metadata,
either as HTML tags or as RDF/XML,
suitable for embedding in the ...
section of the page. The generated metadata can
be edited using the form provided and converted
to various other formats (USMARC, SOIF,
IAFA/ROADS, TEI headers, GILS, IMS or RDF) if
required.
70
4.9 FEA Data Reference Model Use Cases
FOAF is a project for machine-readable modelling
of social networks. The heart of the project is
its specification which defines what statements
you can make about someone, such as Name, Gender,
Homepage, Weblog, etc. It is based on RDF, and
can be easily extended with more specific
relationship definitions. RDF can be used to
build web sites and describe photo collections.
http//rdf.burningbird.net/ and
http//www.oreilly.com/catalog/pracrdf/
71
4.9 FEA Data Reference Model Use Cases
http//www.topquadrant.com/people/rhodgson/foaf.xm
l do View Source to see RDF
72
4.9 FEA Data Reference Model Use Cases
The ConvertToRDF tool is designed to take
plain-text delimited files, like .csv files
dumped from Microsoft Excel, and convert them to
RDF. To use it all you need to write is a file to
map from one form to the other. The ontology for
creating the mapping file is shown here.
http//www.mindswap.org/mhgrove/ConvertToRDF/conv
ertOnt.rdf
73
Appendix 1 FEA-RMO Tutorial

• Federal Enterprise Architecture Reference Model
  Ontology (FEA-RMO) Documentation
• Project Plan.
• OSERA (Open Source E-Government Reference
  Architecture) Open Source Agreement.
• FEA Reference Model Ontology (FEA RMO), February,
  2005, GSA OSERA Deliverable Version 1.2
• Candidate Uses Cases.
• FEA Ontology Models Design Options for PRM
  Discussion Draft (PowerPoint).
• Semantic Technology and Ontology Engineering for
  Enterprise Architecture (PowerPoint for
  Enterprise Architecture Summit, May 22-24, Miami,
  Florida).

74
Appendix 1 FEA-RMO Tutorial

• Lessons Learned
• Good natural language Reference Models help
  ontology development.
• Modeling Principles and Patterns are key often
  evolve iteratively.
• Modular Architecture benefits concurrent
  lifecycle management.
• OWL works and reasoning pays off in generic code.
• Semantic Application can be built quickly.

Ralph Hodgson, Semantic Technology and Ontology
Engineering for Enterprise Architecture,
Enterprise Architecture Summit, May 22-24, Miami,
FL.
75
Appendix 1 FEA-RMO Tutorial

• Encoding an OWL Ontology
• Phases
• Object-oriented requirements analysis
• Develop common expectations for the domain
  description.
• Knowledge acquisition
• Use authoritative sources for the domain.
• Knowledge engineering
• Describe a structured interpretation of the
  domain that references the authoritative sources.
• Design
• Use graphical design languages like UML to
  visualize the relationships.

Source Lee Lacy, OWL Representing Information
Using the Web Ontology Language, Trafford, 2005,
page 143.
76
Appendix 1 FEA-RMO Tutorial

• OWL Ontology File Structure (see next slide)
• OWL Header (usually reused)
• XML Declaration and RDF Start Tag
• Namespaces
• Versioning Information and Import Statements
• Ontology Element (owlOntology)
• Body
• Statements about classes, properties, and their
  relationships.
• Makes the open-world assumption just because
  something is not specified, you cannot assume it
  to be false it might be specified somewhere on
  the web.
• Footer Closing Tag.
• Example An organization standardizes their
  ontologies that extend another organizations
  ontologies (see schematic diagram).

Source Lee Lacy, OWL Representing Information
Using the Web Ontology Language, Trafford, 2005,
Chapter 11.
77
Appendix 1 FEA-RMO Tutorial

• OWL Ontology Document
• Header
• XML Declaration and RDF Start Tag
• Namespace Declarations
• Ontology Element
• Version Information
• Imports Element
• Body
• Class, Property, and Individual Statements
• Footer
• RDF End Tag

Source Lee Lacy, OWL Representing Information
Using the Web Ontology Language, Trafford, 2005,
page 146.
78
Appendix 1 FEA-RMO Tutorial

• Structure of the PRM
• Header
• Start Tag and Namespaces
• Start Ontology
• Class IDs (and about) and Subclass relationships
• Object Property
• Datatype Property
• Functional Property
• Inverse Function Property
• Labels
• End Tag and Comment
• -

http//web-services.gov/owl/2004/11/fea/FEA_PRM_s.
owl
79
Appendix 1 FEA-RMO Tutorial
Ontologies on Web Servers
Standard namespaces XMLS Datatype OWL
Specification RDFS Specification RDF Specification
Ontologies being Used/Extended
OWL
namespace references
Ontology Stewards Web Server
Imports
Ontology
Information Publishers Web Server
Ontology- specific Datatypes
compliant with
OWL
OWL
Imports
RDF
Instance Data
Source Lee Lacy, OWL Representing Information
Using the Web Ontology Language, Trafford, 2005,
page 144.
80
Appendix 1 FEA-RMO Tutorial
This ontology references and imports other
parts of the FEA-RMO.
http//web-services.gov/owl/2004/11/fea/FEA_PRM_s.
owl
81
Appendix 2 Dynamic Knowledge Repository

• Semantic Technologies Interoperability
• Proceedings from the One-Day Conference Semantic
  Technologies for E-Government, White House
  Conference center, September 8, 2003.
• A Semantic Web Primer, Grigoris Antoniou and
  Frank van Harmelen, The MIT Press, Cambridge,
  Massachusetts, London, England, Contents (from
  Web Site).
• SICoP White Paper Series Module 1 Introducing
  Semantic Technologies and the Vision of the
  Semantic Web, Updated on 2/16/2005, Version 5.4.
• Semantic Web Applications for National Security
  (SWANS) Conference, April 7-8, 2005.

82
Appendix 2 Dynamic Knowledge Repository

• FEA 2005 Semantic Interoperability Repository
• Federal Enterprise Architecture 2005
• Mapping the PRM, PART, and OMB 300
• FEA PMO Action Plan (2005 - 2006)
• FEA06 Revision Summary
• Performance Reference Model
• Business Reference Model
• Service Component Reference Model
• Data Reference Model
• Technical Reference Model
• FY 2006 OMB 300 Schema Version 2.95 Data
  Dictionary
• Program Assessment Rating Tool (PART)
• Federal Enterprise Architecture Reference Model
  Ontology (FEA-RMO) Documentation