1. 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 http://web-services.gov/ http://web-services.gov/ and http://colab.cim3.net/cgi-bin/wiki.pl?SICoP

2. 2 Overview 1. Organization 2. White Paper Series 3. Workshops and Conferences 4. Pilots

3. 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 Council’s 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. 4 For the “Outstanding Leadership” as Co-Chair of the SICoP During Its Formation and First Year Presented at the Semantic Web Applications for National Security Conference, April 7-8, 2005. Special Recognition Rick (Rodler F.) Morris, U.S. Army, Office of the CIO Federal CIO Council’s Semantic Interoperability Community of Practice (SICoP) By SICoP Co-Chair, Brand Niemann, U.S. EPA, and SICoP Members

5. 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 28 th –(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. 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- bullet 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. 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. 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. 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. 10 For the “Outstanding Editorial Leadership” in the Completion of the SICoP White Paper “Introducing Semantic Web Technologies: Harnessing the Power of Information Semantics” Presented at the Semantic Web Applications for National Security Conference, April 7-8, 2005. Special Recognition Kenneth R. Fromm, Loomia, Inc. Federal CIO Council’s Semantic Interoperability Community of Practice (SICoP) By SICoP Co-Chairs, Rick Morris, U.S. Army, & Brand Niemann, U.S. EPA

11. 11 2. White Paper Series Module 2 – The Business Case for Semantic Technologies: –Preliminary Research: http://www.project10x.com/downloads/topconnexion/BusinessValue _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 R&D 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/MD_BizValue2005 _SWANS.pdf

12. 12 2. White Paper Series

13. 13 Example-1: Information in Context

14. 14 Example-1: Information in Context

15. 15 Example-1: Information in Context

16. 16 Example-1: Information in Context

17. 17 Example-1: Information in Context

18. 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 R&D and over $100M in partner R&D 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 I’ve 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. 19 Example-1: Information in Context

20. 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. 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. 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 E-Government Interoperability in the European Union, June 29-20, 2005 (tentative-see next slide) The Third Semantic Technologies for E-Government Conference, Fall 2005, Washington, DC XML 2005 Conference: From Syntax to Semantics, November 14- 18, Atlanta, Georgia (Keynote & Session Tracks)

23. 23 3. Workshops and Conferences E-Government Interoperability in the European Union Workshop (Draft): –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 O’Toole, CEO, Cape Clear Software –Only the Data Endures: Sean McGrath, CTO, Propylon –Panel Session and Audience Questions

24. 24 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) –4.8 Composite Application Solution with Semantic Technologies –4.7 Semantic Technology Profiles for the FEA Data Reference Model –4.8 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

25. 25 4.1 Formal Taxonomies for the U.S. Government OWL Listing: Etc. 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/formtax.html http://www.xml.com/pub/a/2005/01/26/formtax.html Transportation Class Hierarchy

26. 26 4.2 Ontology for Indicators Indicators TopicsOrganizationsJurisdictions The Economy Society Culture The Environment Cross-Cutting Publicly led Privately led Led by public-private partnership U.S. local/regional level U.S. state level National level outside the United States Supranational level Note that each of these classes can and do have multiple instances underneath them, etc. Schematic of the Ontology

27. 27 4.2 Ontology for Indicators See Best Practices Repository at http://web-services.gov The folder names are either the ontology or the knowledgebase instances.

28. 28 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.gov Submit FEARMO comments: –http://colab.cim3.net/cgi- bin/wiki.pl?HowToSubmitFEARMO_Comments

29. 29 4.3 Federal Enterprise Reference Model Ontology (FEA RMO) FEA-RMO at SWANS in SWOOP 2.2.1 from MindSwap Research Group (Jim Hendler). Concise Format Abstract Syntax RDF/XML Turtle Ontology List Ontology Hierarchy Reasoner: Pellet RDFS-like

30. 30 4.4 Building a National Health Information Network Ontology The Mind Map Book: How to Use Radiant Thinking to Maximize Your Brain’s 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 “Explorer’s Guide to the Semantic Web,” Thomas Passin, Manning Publications, 2004, pages 106 and 141.

31. 31 4.4 Building a National Health Information Network Ontology NHIN general organizational & business management & operational standards & policies financial, regulatory, & legal other RFI DR. BRAILER WORK GROUPS technical & architecture organization & business financial, regulatory, & legal STRATEGIC PLAN GOALSregional initiatives clinical practice population health health interoperability Federal Health Architecture ORGANIZATIONAL STRUCTURE Inform Clinical Practice Interconnect Clinicians Personalize Care Improve Population Health Possible/probable interrelationships organizational technical semantic FRAMEWORKS other OTHER NCVHS CCHIT Etc. STANDARDS ORGANIZATIONS standards governance privacy regionalization financing architecture regulation

32. 32 4.4 Building a National Health Information Network Ontology See Best Practices Repository at http://web-services.gov

33. 33 4.4 Building a National Health Information Network Ontology FAST Data Search: Search View

34. 34 4.4 Public Domain Databases for Semantic Searching and Ontology Building 4.4.1 Gartner Magic Quadrant for Enterprise Search, 2004 4.4.2 Gartner Analysis: Leaders 4.4.3 FAST Data Search: Categorization and Taxonomy Support 4.4.4 FAST Data Search: Integration 4.4.5 Paradigm Shifts 4.4.6 Public Domain Database

35. 35 4.4.1 Gartner Magic Quadrant for Enterprise Search, 2004 Source: Gartner Research ID Number: M-22-7894, Whit Andrews, 17 May 2004.

36. 36 4.4.2 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.

37. 37 4.4.3 FAST Data Search: Categorization and Taxonomy Support

38. 38 4.4.4 FAST Data Search: Integration

39. 39 4.4.5 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: Children’s Health, Mercury, etc.

40. 40 4.4.6 Public Domain Database Content SourceContent TypePilot Example Web SiteTopicsChildren’s Health, Mercury, Etc. Web SiteRegistriesSubstance Exchange NetworkNodesPacific Water Quality E-GovE-RulemakingSamples Data MartTBD IndicatorsReports on the Indicators EPA, Heinz, Etc. GISMapsRegion 4 GeoBook GISMetadataClearinghouse

41. 41 4.5 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.

42. 42 4.5 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/OntologyTaxonomyCoordinatingWG

43. 43 4.5 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@colab.cim3.net Name: ontac-dev –Description: ONTAC Taxonomy-Ontology Development Discussion –Address: ontac-dev@colab.cim3.net Name: ontac-tool –Description: ONTAC Tools Development Discussion –Address: ontac-tool@colab.cim3.net

44. 44 4.6 Composite Application Solution with Semantic Technologies Train Derailment Example: –6 January 2005, 3:50 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.

45. 45 4.6 Composite Application Solution with Semantic Technologies 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. Event Type Ontology in Context: Application in Unicorn Workbench: http//www.unicorn.com

46. 46 4.7 Semantic Technology Profiles for the FEA Data Reference Model 4.7.1 Semantic Web 4.7.2 The W3C’s Semantic Web Services and Rules 4.7.3 Ontologies – Enablers of the Semantic Web 4.7.4 Describing Semantics 4.7.5 Semantic Relationships 4.7.6 Semantic Web’s Layered Architecture 4.7.7 Mapping to the Data Reference Model

47. 47 4.7.1 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

48. 48 4.7.2 The W3C’s 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.

49. 49

50. 50 4.7.2 The W3C’s 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

51. 51 4.7.3 Ontologies – Enablers of the Semantic Web Ontology Tbox Abox Fact Instances compliant with Class 1 Computer Science Gruber Ontology Definition Class 2 Class 3Class 4 Domain Ontology Conceptualization describes modeled by specified by

52. 52 4.7.3 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 Gruber’s definition.

53. 53 4.7.4 Describing Semantics ConstructDescriptionRelated Concepts Class Represents a group or set of individual objects with similar characteristics. Object class (OOP) and tables (RDBMS) Property Associates attribute/value pairs with individuals or restricts classes. Accessor methods (OOP) and table columns/fields (RDBMS) Individual Represents a specific object instance of a class. Objects/class instances (OOP) and table rows/ records (RDBMS) Source: Lee Lacy, OWL – Representing Information Using the Web Ontology Language, Trafford, 2005, page 40.

54. 54 4.7.5 Semantic Relationships FunctionalityRelationshipSummary Concept Relating Constructs to each other Relates individuals to classes Relates individuals to properties Relates classes with properties Membership Attribute values Restrictions Describing Relationships Synonymy Antonymy Hyponymy Meronymy Holonymy Similarities Differences Specialization Part/Whole Whole/Part Source: Lee Lacy, OWL – Representing Information Using the Web Ontology Language, Trafford, 2005, page 40.

55. 55 4.7.5 Semantic Relationships Class Property Individual restrict is an instance of has value for 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.

56. 56 4.7.6 Semantic Web’s 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.

57. 57 4.7.7 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.

58. 58 4.7.7 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.

59. 59 4.7.7 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 W3C’s OWL and OMG’s MOF specifications. –Resource Descriptions: Same as the W3C’s 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.

60. 60 4.7.7 Mapping to the Data Reference Model Semantic Web’s 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 Web’s architecture. –RDF Schema (RDFS): RDF’s 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.

61. 61 4.8 FEA Data Reference Model Use Cases Potential Use Cases – May 16 th 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.

62. 62 4.8 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/nhinrfiontology04052005.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).

63. 63 4.8 FEA Data Reference Model Use Cases TypePurposeExampleURL Unstruc- tured Categorization & Search Enhancement DCDOT http://www.ukoln.ac. uk/metadata/dcdot/ http://www.w3.org/R DF/Validator/ Semi- structured SharingFOAF http://www.ldodds.co m/foaf/foaf-a- matic.html http://rdf.burningbird. net/ StructuredIntegration ConvertToRDF http://www.mindswa p.org/~mhgrove/Con vertToRDF/

64. 64 4.8 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.

65. 65 4.8 FEA Data Reference Model Use Cases http://rdf.burningbird.net/ and http://www.oreilly.com/catalog/pracrdf/ 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.

66. 66 4.8 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/convertOnt.rdf

67. 67 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.

68. 68 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 (owl:Ontology) –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 organization’s ontologies (see schematic diagram). Source: Lee Lacy, OWL – Representing Information Using the Web Ontology Language, Trafford, 2005, Chapter 11.

69. 69 Appendix 1: FEA-RMO Tutorial OWL Ontology Document: –Header: XML Declaration and RDF Start Tag: – –<rdf:RDF »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.

70. 70 Appendix 1: FEA-RMO Tutorial Structure of the PRM: –Header: –Start Tag and Namespaces: <rdf:RDF xmlns:rdfs=“ –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

71. 71 Appendix 1: FEA-RMO Tutorial Source: Lee Lacy, OWL – Representing Information Using the Web Ontology Language, Trafford, 2005, page 144. Standard namespaces: XMLS Datatype OWL Specification RDFS Specification RDF Specification Ontologies on Web Servers Ontology Steward’s Web Server Information Publisher’s Web Server RDF Instance Data OWL Imports Ontology Ontology- specific Datatypes OWL Imports compliant with Ontologies being Used/Extended namespace references

72. 72 Appendix 1: FEA-RMO Tutorial http://web-services.gov/owl/2004/11/fea/FEA_PRM_s.owl This ontology references and imports other parts of the FEA-RMO.

73. 73 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.

74. 74 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) FEA’06 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