1. TopQuadrant’s Linked Models Ontologies: VAEM, VOAG, DTYPE, QUDT, oeGOV
Irene Polikoff, Ralph Hodgson
TopQuadrant
June 29-30, 2011
"Valuing the Role of Semantic Web Technologies - A Ten Year Personal Reflection and a Vision For The Next Ten Years"
In government projects, we have worked on the questions: "What does the use of semantic technology mean to the users of data?", "Can semantic web technologies 'connect the dots' and break down data silos?", "What does it mean to link data?", "How can data access and visualization be improved?”, “How do Ontology models help data interoperability?”, "How can RDF and OWL co-exist with XML?", "What has to happen in an organization for semantic web technologies to be put to work effectively?", "what are the criteria for choosing technologies for implementing solutions?". The talk will address these questions with examples from government organizations where, data has mission criticality, and there are significant challenges to data aggregation and interoperability. At GSA, in 2003 my company created the FEA ontologies. Since 2002, at NASA, we have worked on the use of semantic technology in data architecture, systems engineering, simulation and Modeling, and Telemetry and Commanding for Space Systems Interoperability in support of Manned Space Missions. At the Netherlands Ministry of Justice, semantic technology is being used to generate XML Schemas that are compliant with UN/CEFACT Core Component Technical Specifications (CCTS). In other projects semantic web technologies are being used to standardize vocabularies. oeGOV.us was a personal effort to ontologize the structure of the US Government. All these efforts offer lessons on "how to put ontologies to work". This workshop offers us all a way to share predictions and plans for the next ten years.

2. As it grows, LOD needs models for interpreting and transforming data
2007
2011
2011
LinkedModels.org
ref1:
ref2:

3. Brief history of TQ’s ontology building projects in government
Capability Cases
FAA
FEA-RMO
OSERA
Business Reference Model (BRM)
Lines of Business
Agencies, Customers, Partners
Service Component Reference Model (SRM)
Service Layers, Service Types
Components, Access and Delivery Channels
Technical Reference Model (TRM)
Service Component Interfaces, Interoperability
Technologies, Recommendations
Data Reference Model (DRM)
Business-focused data standardization
Cross-Agency Information exchanges
Performance Reference Model (PRM)
Government-wide Performance Measures & Outcomes
Line of Business-Specific Performance Measures & Outcomes
Business-Driven Approach
(Citizen-Centered Focus)
Component-Based Architectures
NASA NExIOM
NATO
DoDAF
BEA
CCTS
oeGOV
References: (1) SKOS-based FEA-RMO Ontologies are at
(2) oeGOV ontologies are at

4. The Linked Open Data World
None
Tool-Based
Intrinsic
“Link-ability”
Life Sciences
Strong RDF/OWL + Controlled Vocabularies
QUDT
oeGOV
UK data.gov
Medium RDF
dbPedia
Information Architecture
US data.gov
RDF
(*)
XML
(263)
KML
(21)
XLS
(344)
PDF
Weak Non RDF/OWL
ESRI
(166)
CSV (955) F
(#)
Based on data.gov June 2009

5. NASA Constellation Program Information Architecture
Information architecture (IA) defines a model, processes and services for how information is represented, governed and used in systems, applications, databases, documents and activities in order to ensure compliance to naming and identifier rules, standard data and information types, controlled vocabularies and coding schemes.
NASA Information Architecture
Governance
Provenance
Names and Identifiers
Data Types
Information Types
Algorithms & Equations
Encoding Rules
Naming & Design Rules
Metadata
XML
OWL
Models
In the context of information systems design, information architecture refers to the analysis and design of the data stored by information systems, concentrating on entities, their attributes, and their interrelationships. It refers to the modeling of data for an individual database and to the corporate data models an enterprise uses to coordinate the definition of data in several (perhaps scores or hundreds) of distinct databases. The "canonical data model" is applied to integration technologies as a definition for specific data passed between the systems of an enterprise. At a higher level of abstraction it may also refer to the definition of data stores.

6. Key to Success is an Ontology Architecture – Five Dimensions turned out to be important
Time
Organization
Domain
Discipline
Specificity
Ontologies partitioned by domains, disciplines, organizations, specificity and time;
Named graphs aggregated using configuration ontologies according to need;
Two other dimensions also important: aspect and viewpoint.

7. VAEM: Vocabulary for Attaching Essential Metadata
VAEM stands for "Vocabulary for Attaching Essential Metadata".
VAEM attaches basic metadata properties and dimension properties, such as domain, discipline, aspect and viewpoint, to the 'owl:Ontology' class to fully qualify the ontology.
What VAEM regards as 'essential metadata' is data about dates and times, confidentiality, and other characterisitic qualifiers of the ontology.
Also references to where a ontology is documented (catalog) and where to find ontology Governance, Attribution and Provenance.
For the latter, some properties from the VOAG ontology are used, notably, 'voag:hasGovernance', 'voag:withAttributionTo', and 'voag:hasLicenceType'.

8. VAEM QUAD Chart Motivations Work Accomplished Issues Next Objectives
characterize a graph’s role, scope, and intent with respect to a framework of dimensions
Provide catalog linkage (using VOAG) for governance and provenance
Work Accomplished
Established dimensions for domain, discipline, aspect and viewpoint
Published version 1.2 catalog entry
Issues
Alignment with VANN
More use of Dublin Core?
Concerns that importing DC causes "ontology glut", through the import of many DC Terms.
Next Objectives
Provide better descriptions of dimensions and give examples
Align with VANN
Reuse more of Dublin Core

9. VAEM

10. VOAG: Vocabulary for Attribution and Governance
VOAG stands for "Vocabulary Of Attribution and Governance".
The ontology is intended to specify licensing, attribution, provenance and governance of an ontology.
Unlike VAEM is intended to be used “outside” of an ontology that is being described
VOAG captures many common license types and their restrictions.
Where a license requires attribution, VOAG provides resources that allow the attribution to be made.
Provenance is defined in terms of source and pedigree.
A minimal model of governance is provided based on how issues, releases and changes are managed.
VOAG makes uses of some concepts from VOID ( notably void:Dataset.

11. VOAG QUAD Chart Motivations Work Accomplished Issues Next Objectives
The need to specify licensing, attribution, governance, provenance and pedigree of a model or dataset
Work Accomplished
Attribution class and properties
Catalog entry support
Basic model of governance and provenance
Over 100 license types
Published version 1.0 catalog entry
Issues
Alignment with W3C Provenance Group (TopQuadrant is a member)
Provenance Model should be separate to VOAG for use in contexts where other aspects of VOAG are not needed
Next Objectives
Factor out Provenance Model

12. VOAG

13. DTYPE: Data Type Ontology (Level 1)
The ontology 'dtype' provides a specification of simple data types such as enumerations and codelists.
Examples of use:
Conversion of XML Schemas and UML Models to OWL
Modeling of NIEM code-lists

14. DTYPE QUAD Chart Motivations Work Accomplished Issues Next Objectives
Need to represent codelists and enumerations, where members can have codes, order and literals
Also supports derived enumerations and codelists such as sublists of countries and currencies
Anticipated need for other data types such as arrays
Work Accomplished
Basic treatment of codelists and enumerations
Published version 1.0 catalog entry
Issues
None
Next Objectives
Provide examples from NIEM

15. DTYPE

16. QUDT: Quantities, Units, Dimensions and Types

17. QUDT QUAD Chart Motivations Work Accomplished Issues Next Objectives
provide a unified model of, measurable quantities, units for measuring different kinds of quantities, the numerical values of quantities in different units of measure and the data structures and data types used to store and manipulate these objects in software
Work Accomplished
Dimensional Units for all SI and non- SI units and quantites
Published version 1.1 catalog
Issues
Unit codes
Next Objectives
More coverage of non-engineering domains

18. QUDT

19. oeGOV: Ontologies of US Government
An Ontology of the US Government containing over 600 government bodies.

20. oeGOV: DHS Example usgov:DHS a gov:CivilAgency , gov:ExecutiveAgency ;
rdfs:label "Department of Homeland Security (DHS)"^^xsd:string ;
dc:description "Homeland Security leverages resources within federal, state, and local governments, coordinating the transition of multiple agencies and programs into a single, integrated agency focused on protecting the American people and their homeland. More than 87,000 different governmental jurisdictions at the federal, state, and local level have homeland security responsibilities. The comprehensive national strategy seeks to develop a complementary system connecting all levels of government without duplicating effort. Homeland Security is truly a 'national mission'."^^xsd:string ;
gc:url " ;
gov:adminstration usgov:TSA ; gov:agency usgov:CG , usgov:SS , usgov:FEMA ;
gov:center usgov:FLETC ;
gov:committee usgov:HSSTAC , usgov:DHS-TFNA ;
gov:council usgov:DHS-HSAC , usgov:DHS-NIAC , usgov:CIPAC , usgov:DHS-ICCEPID ;
gov:department usgov:DHS-ICE , usgov:DHS-CBP ;
gov:directorate usgov:DHS-DNPP , usgov:DHS-DST , usgov:DHS-DM ;
gov:mission "The Department of Homeland Security’s overriding and urgent mission is to lead the unified national effort to secure the country and preserve our freedoms. While the Department was created to secure our country against those who seek to disrupt the American way of life, our charter also includes preparation for and response to all hazards and disasters. The citizens of the United States must have the utmost confidence that the Department can execute both of these missions."^^xsd:string ;
gov:office usgov:DHS-PO , usgov:DHS-ESEC , usgov:DHS-OGC , usgov:DHS-OP , usgov:DHS-OIG , usgov:DHS-OHA , usgov:DHS-OLA , usgov:DHS-OIA , usgov:DHS-OCRCL , usgov:DHS-CISO , usgov:DHS-OPA , usgov:DHS-OS , usgov:DHS-MAO , usgov:DHS-CNE , usgov:DHS- OOC ; gov:reportsTo usgov:EOP ;
gov:service usgov:CIS ; gov:suborganization usgov:DHS-CBP .

21. oeGOV: Ontologies of US Government
DHS
NARA
An Aggregation Graph that composes each Agency Graph
HUD
Government “Core”
Dublin“Core”
Government Ontology
Specialization and extension of the Government Ontology to represent the US Government
USDA

22. oeGOV QUAD Chart Motivations Work Accomplished Issues Next Objectives
Provide controlled vocabularies for referring to government bodies
Build an ontology model of the US Government for Linked Government Data Provenance
Work Accomplished
US Government Ontology with over 600 Government bodies
Partitioning by Agency
Published at
Issues
Completeness and Correctness Checks
Provenance is needed
Next Objectives
Build catalogs like the VAEM, VOAG and QUDT catalogs
Build a community – can’t do this on our own

23. oeGOV

24. Thank You Irene Polikoff E-mail: irene@topquadrant.com
@oegovnews