1. Harmonization of Sensor Standards in Semantic Wikis: Sensor Standards Harmonization Working Group Meeting
Brand L. Niemann,
Senior Enterprise Architect, US EPA, and
Co-Chair, Semantic Interoperability Community of Practice (SICoP)
October 16, 2007

2. Broader Context
In SICoP's work on a semantic interoperability data management strategy for the community, the focus has shifted from applying it to EPA, which is nearly done, to the broader community:
1. SICoP delivered a semantic interoperability data management strategy to the Best Practices Committee of the Federal CIOC in June.
2. SICoP applied the semantic interoperability data management strategy to the US EPA in July - September as part of its ongoing work with federal agencies and presented this work at several conferences and had it reviewed and accepted by the Metatopia 2007 Committee for publication and use at future conferences.
3. SICoP is now applying the semantic interoperability data management strategy for the Net-Centric Operations Industry Consortium (NCOIC) with its members to selected agencies (e.g. FAA NextGen, Logistics, USCG, DHS, etc.

3. Broader Context
A New Enterprise Data Management Strategy for the US EPA:
Part 1: Overview (August 15, 2007)
Part 2: Inventory of Data Assets (August 29, 2007)
Part 3: Integration of Data Tables (September 5, 2007)
Part 4: Spatial Data (September 24, 2007)
Part 5: Land Quality and Water Quality Management Segments (September 25, 2007)
Part 6. Support for E-Discovery (in process)
Note: Chapters in an online, free, collaborative book at

4. Broader Context
SICoP delivered Three White Papers to the Best Practices Committee of the Federal CIO Council:
1. Introducing Semantic Technologies and the Vision of the Semantic Web ("DRM of the Future") (Translated into Japanese) (February 16, 2005).
2. Semantic Wave Executive Guide to the Business Value of Semantic Technologies (January 6, 2006).
3. Operationalizing the Semantic Web/Semantic Technologies: A roadmap for agencies on how they can take advantage of semantic technologies and begin to develop Semantic Web implementations (June 18, 2007).

5. Broader Context
SICoP is working on updates to each of those three White Papers as follows:
1. Semantic Interoperability Data Management Strategy: Net-Centric Operations Industry Consortium (NCOIC) and Others (September 2007 Draft)
2. Semantic Wave 2008: Industry Roadmap to Web 3.0, Mills Davis, Project 10X (October 2007 Draft).
3. Semantic Interoperability with Relational Databases (e.g. Data marts and Data warehouses): Solving the Schema Mismatch Problem with Ontology, Lucian Russell, Private Consultant (December 2007 Draft).

6. Broader Context
SICoP Supports the NCOIC in Three Principal Activities:
Lexicon: Definition of Terms.
SCOPE: Systems, Capabilities, Operations, Programs, and Enterprises (SCOPE) Model for Interoperability Assessment.
SIF: Semantic Interoperability Framework.
These Activities Support the Model-Driven Architecture and Ontology Driven Development Approach Being Used:
First develop a model of the system under study and then transform it into the real thing (e.g. an executable software entity).
For example, start from an ontology, transfer it to a UML platform-neutral domain model, and then generate a Java implementation.
See July 11-12, 2007, Building DRM 3.0 and Web 3.0 Knowledgebases: Where Do the Semantics Come From? at

7. Broader Context
Data Modeling and OWL: Two Ways to Structure Data, David Hay, Essential Strategies, Inc.:
Objectives of a Data Model:
Capture the semantics of an organization.
Communicate these to the business without requiring technical skills.
Provide an architecture to use as the basis for database design and system design.
Now: Provides the basis for designing Service Oriented Architectures.
See

8. Broader Context
Data Modeling and OWL: Two Ways to Structure Data, David Hay, Essential Strategies, Inc. (continued):
Synopsis:
Both data modeling and ontology languages represent the structure of business data (ontologies).
Data modeling represent data being collected, and filters according to the rules.
Ontology languages represent data being used, with ability to have computer make inferences.
Comment from Lucian Russell (SICoP White Paper 3 Author):
So ontology can improve data quality in legacy systems (David Hay agreed) and solve the Schema Mismatch Problem (recall slide 5).

9. Broader Context Standards: Sensors:
New - Develop ontology at the onset
Existing - Refactor to develop an ontology*
This is what I have been doing!
Sensors:
Existing - Refactor to develop an ontology
This is what we are doing in the NCOIC Pilots!
The Modular Ontology Approach, June 26, 2007, SSHWG Meeting.
* Note: A data model expressed as an ontology, not just an XML Schema.

10. Broader Context Refactor ontologies: Multiple federated ontologies:
Object-oriented developers use the notion of refactoring to describe evolutionary changes that clean up the model design without breaking existing functionality. Tools like Eclipse and IntelliJ provide automated refactoring support for Java classes and their members. The same idea is supported by TopBraid Composer.
Multiple federated ontologies:
Are usually smaller, so more manageable, can evolve semi-autonomously, can be reused as components in different contexts, and multiple people and teams can work on separate parts of a large ontology at any one time.

11. Demonstrations
1. A Semantic Approach to Data Management in Sensor Networks (November 6, 2006).
2. CBRN Data Model as an Ontology in a Semantic Wiki (March 28, 2007).
3. Scalable Semantic Web Applications (TopQuadrant and Franz, Inc.) (July 16, 2007).
4. July 19, 2007, First Semantic Interoperability Mashup with NCOIC Semantic Interoperability WG, Spatial Ontology CoP, and SICoP.
5. Google Maps Enterprise Webinar (August 16, 2007).
6. Semantic Web, Google Maps, and OGC Sensor Standards Mashup (October 16, 2007).

12. Demonstrations
1. A Semantic Approach to Data Management in Sensor Networks (November 6, 2006):
This published paper describes an application of ontology technology within an architecture for processing sensors that monitor conditions in grain and storage silos. The authors show that historical and streaming sensor measurements can be combined to support expressive SPARQL queries over data modelled in OWL and stored as RDF files.”
See Proceedings of the Semantic Sensor Network Workshop, November 6, 2006, Website:

13. Demonstrations
2. CBRN Data Model as an Ontology in a Semantic Wiki (March 28, 2007):
In the Sensor Standards Harmonization WG, we converted the CBRN Data Model spreadsheet to an ontology in one of our SICoP Semantic Wikis (Knoodl.com) for a demonstration to the SSHWG members that asked to see how we could work with it in a "Semantic Web way" because it was made available at the August 2006 Workshop in Oak Ridge and it represented a great example of a spreadsheet-to-ontology conversion. We have removed this because our intent all along was to harmonized the multiple standards and data models in support of the SSHWG so our result will not be the release of “the CBRN data model spreadsheet" but a new semantically harmonized and machine processible ontology. See and

14. Demonstrations
3. Scalable Semantic Web Applications (TopQuadrant and Franz, Inc.) (July 16, 2007):
An ontology is a formal description of the meaning of the information used by software systems. Just like relational databases use SQL as a query language, ontologies developed using Semantic Web standards are queried with a query language called SPARQL. SPARQL is a simple yet powerful language. A single SPARQL query can combine the selection criteria based on the data values as well as their meaning. Unlike relational databases and SQL which are tightly bound to a specific data model, ontologies are highly flexible making it possible to (1) easily accommodate changes in the data model, and (2) create generic queries that work in multiple situations and don't need changing when the data model must change. These special qualities make semantic applications more agile, flexible, and faster to develop than traditional approaches.

15. Demonstrations
3. Scalable Semantic Web Applications (TopQuadrant and Franz, Inc.) (July 16, 2007) (continued):
Developing and deploying semantic applications for an enterprise requires both a production strength ontology design and development environment and a robut datastores. By integrating Franz’s AllegroGraph RDFStore (Jans Aasman) with TopQuadrant’s TopBraid Compose (Dean Allemang), users now have the essential foundation to build their semantic solutions with – a scalable, interactive graphical development platform. These components work together to enable developers to build comprehensive, large-scale semantic models (ontologies) that can be stored and queried very efficiently.
The Webinar demonstrated building an ontology model in RDF/OWL and querying RDF data stored in AllegroGraph and discussed the integration of information from disparate data sources and the use of inferencing. The TopQuadrant Tutorial at the 2007 Semantic Technology Conference (May in San Jose, CA) featured a Semantic Web Mashup with Google Maps. Tammera Countryman recommended the July 16th Webinar to the SSHWG members.

16. Demonstrations
4. July 19, 2007, First Semantic Interoperability Mashup with NCOIC Semantic Interoperability WG, Spatial Ontology CoP, and SICoP:
This meeting featured Snoogle Demonstration by BBN. Snoogle is a graphical, SWRL-based ontology mapper to assist in the task of OWL ontology alignment. It allows users to visualize ontologies and then draw mappings from one to another on a graphical canvas. Users draw mappings as they see them in their head, and then Snoggle turns these mappings into SWRL/RDF or SWRL/XML for use in a knowledge base.
See and
Semantic Web Tools at

17. Demonstrations 5. Google Maps Enterprise Webinar (August 16, 2007):
See
SICoP is collaborating with Google, etc. on the Federal Sitemaps Initiative
See
SICoP is also in discussions with Google about “Google 2.0 Embraces the Semantic Web”
See

18. Demonstrations
6. Semantic Web, Google Maps, and OGC Sensor Standards Mashup (October 16, 2007):
The presentation at the Net-Ready Sensor Standards Harmonization Meeting at NIST on June 26, 2007, recommended using a “Modular Approach to SSHWG Ontology” and the VK Test Semantic Wiki with its new graphical ontology development module.
See
and

19. Demonstrations
6. Semantic Web, Google Maps, and OGC Sensor Standards Mashup (October 16, 2007) (continued):
Conceptually, an ontology can be developed for each sensor standard by extracting the concepts and their relationships from the standards documents and/or extending the XML Schema for the standard it one exists. Then ontology mapping tools like Snoogle can be used for “harmonization” and even scalable Semantic Web applications can be built using the tools like TopQuadrant/AllegroGraph combination.
Initial discussions with SOCoP and OGC revealed the need to first harmonize Google’s KML and OGC’s GML, but further investigation suggests the need for further clarification (see on next page).

20. Demonstrations For the next meeting:
Select several standards and vendor sensors;
Create the modular formal ontologies;
Create a mashup with Google Maps to integrate and display the sensor data.
Who wants to participate and learn how to do this?
Use the Semantic Wikis (Knoodl and Visual Knowledge)
Use the new Semantic Desktop from Radar Networks (tentative).

21. Discussion Before and After
Problem: Have multiple standards and sensors:
Solutions:
Custom interoperability interface between each pair (the classic N(N-1)/2 problem. Okay when N is small, but brittle and doesn’t scale for larger N.
Modular ontologies for each standard and sensor.
The output of each sensor needs to be a discoverable knowledgebase defined as a semantic model/ontology and instances in RDF so one can validate the sensor against standards and make sensor data streams semantically interoperable at run-time.

22. Discussion Before and After
Problem: Have multiple standards and sensors:
Solutions (continued):
Plug-fest creates “one-off” solutions for a particular set of standards, sensors, and data sets.
Try to simulate a sensor with software that uses an ontology and actually transfers its data model and data to the Internet to support a net-centric (Web 3.0 and 4.0) data management and data integration (see next slide).
Kang Lee - A talented student could probably do this for our future meetings!
Steve Ray of NIST supports ontology development for standards compliance and testing (see slide 24).

23. Semantic Wave 2007 | Semantic Technology | Social Computing
Web Number
Brief Description
1.0
Connect information
2.0
Connect people
3.0
Connect knowledge
4.0
Connect intelligences

24. Discussion Before and After
“I believe that semantic technologies are still greatly underappreciated in terms of their potential benefits. Coming from a documentary standards perspective, I am convinced that future information exchange standards will ultimately be specified in formal, semantic terms that provide not only computer interpretability, but also the degree of rigor and precision needed for a well-defined standard. This precision should dramatically reduce many of the interoperability problems we see today.”
Steven R. Ray, Chief
Manufacturing Systems Integration Division
National Institute of Standards & Technology
Source: under Endorsements.