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The State of SICoP

Brand Niemann (US EPA), Chair,Semantic Interoperability Community of Practice (SICoP)

Best Practices Committee (BPC), CIO CouncilDRAFT for May 16, 2005

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

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Overview

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

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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.

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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 RecognitionRick (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

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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

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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.

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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.

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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.

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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

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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 RecognitionKenneth 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

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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

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2. White Paper Series

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Example-1: Information in Context

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Example-1: Information in Context

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Example-1: Information in Context

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Example-1: Information in Context

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Example-1: Information in Context

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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)

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Example-1: Information in Context

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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

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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

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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)

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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

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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• 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

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4.1 Formal Taxonomies for the U.S. Government

OWL Listing:<?xml version="1.0"?> <rdf:RDF

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:daml="http://www.daml.org/2001/03/daml+oil#" xmlns="http://www.owl-ontologies.com/unnamed.owl#" xmlns:dc="http://purl.org/dc/elements/1.1/" xml:base="http://www.owl-ontologies.com/unnamed.owl"> <owl:Ontology rdf:about=""/> <owl:Class rdf:ID="Transportation"/> <owl:Class rdf:ID="AirVehicle"> <rdfs:subClassOf rdf:resource="#Transportation"/> </owl:Class> <owl:Class rdf:about="#GroundVehicle"> <rdfs:subClassOf rdf:resource="#Transportation"/> </owl:Class> <owl:Class rdf:about="#Automobile"> <rdfs:subClassOf> <owl:Class rdf:ID="GroundVehicle"/> </rdfs:subClassOf> 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

Transportation Class Hierarchy

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4.2 Ontology for Indicators

Indicators

Topics Organizations Jurisdictions

The Economy

Society Culture

The Environment

Cross-Cutting

Publicly led

Privately led

Led by public-privatepartnership

U.S. local/regional level

U.S. state level

National level outsidethe United States

Supranational level Note that each of these classescan and do have multiple instancesunderneath them, etc.

Schematic of the Ontology

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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.

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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

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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 FormatAbstract SyntaxRDF/XMLTurtle

Ontology List

OntologyHierarchy

Reasoner:PelletRDFS-like

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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.

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4.4 Building a National Health Information Network Ontology

NHIN

generalorganizational & businessmanagement & operationalstandards & policiesfinancial, regulatory, & legalother

RFI

DR. BRAILER

WORK GROUPStechnical & architectureorganization & businessfinancial, regulatory, & legal

STRATEGIC PLAN GOALS regional initiativesclinical practicepopulation healthhealth interoperabilityFederal Health Architecture

ORGANIZATIONAL STRUCTURE

Inform Clinical PracticeInterconnect CliniciansPersonalize CareImprove Population Health

Possible/probable interrelationships

organizationaltechnicalsemantic

FRAMEWORKS

otherOTHER

NCVHSCCHITEtc.

STANDARDSORGANIZATIONS

standardsgovernanceprivacyregionalizationfinancingarchitectureregulation

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4.4 Building a National Health Information Network Ontology

See Best Practices Repository at http://web-services.gov

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4.4 Building a National Health Information Network Ontology

FAST Data Search: Search View

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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

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4.4.1 Gartner Magic Quadrant for Enterprise Search, 2004

Source: Gartner Research ID Number: M-22-7894, Whit Andrews, 17 May 2004.

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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.

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4.4.3 FAST Data Search: Categorization and Taxonomy Support

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4.4.4 FAST Data Search: Integration

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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.

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4.4.6 Public Domain Database

Content Source Content Type Pilot ExampleWeb Site Topics Children’s Health,

Mercury, Etc.Web Site Registries Substance

Exchange Network Nodes Pacific Water Quality

E-Gov E-Rulemaking Samples

Data Mart TBD TBD

Indicators Reports on the Indicators

EPA, Heinz, Etc.

GIS Maps Region 4 GeoBook

GIS Metadata Clearinghouse

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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.

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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

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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

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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, TradeShow, Broadstrokes, ImageMatters, MyStateUSA, Starbourne, and TargusInfo.

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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, TradeShow, Broadstrokes, ImageMatters, MyStateUSA, Starbourne, and TargusInfo.

Event Type Ontology in Context:Application in Unicorn Workbench: http//www.unicorn.com

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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

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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

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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.

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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

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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 3 Class 4

DomainOntology

Conceptualization

describes

modeled byspecified by

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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.

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4.7.4 Describing Semantics

Construct Description Related 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.

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4.7.5 Semantic Relationships

Functionality Relationship Summary Concept

Relating Constructs to each other

Relates individuals to classesRelates individuals to propertiesRelates classes with properties

Membership

Attribute values

Restrictions

Describing Relationships

SynonymyAntonymyHyponymyMeronymyHolonymy

SimilaritiesDifferencesSpecializationPart/WholeWhole/Part

Source: Lee Lacy, OWL – Representing Information Using the Web Ontology Language, Trafford, 2005, page 40.

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4.7.5 Semantic Relationships

ClassProperty

Individual

restrict

is an instance ofhas 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.

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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.

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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.

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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.

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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.

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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.