Ontology for Segment Data Architecture: Tutorial

1. Ontology for Segment Data Architecture: Tutorial Brand Niemann Senior Enterprise Architect US EPA May 12, 2008 DRAFT http://gov2.wik.is/EPA_Enterprise_Architecture/Ontology_for_Segment_Data_Architecture

2. Introduction • 2008 is the year of Segment Architecture in Enterprise Architecture work. EPA needs a Data Architecture for its Segments. A recent book entitled "Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL contains and excellent example of the application of ontology to Enterprise Architecture, specifically, the Federal Enterprise Architecture Reference Model Ontology (FEARMO) (see pages 248-258). Collaboration and Innovation within and across Segments will be crucial to reduce the chaos and contention that arises from cross-program initiatives like Segments.

3. Purpose • The recent Enterprise Architecture training (documented in this Wiki) focused on the eleven segments defined by our Chief Architect, involved those leading and working on each segment, and supporting files on a CD-ROM for each. During this training the Data Architect and former Data Architect decided to collaborate on a data architecture across all the segments in preparation for a "Metadata Summit" Community of Practice/Community of Interest meeting to be held say in July and prepared an initial spreadsheet to start organizing that.

4. Purpose • The former Data Architect decided to take this one step further as part of his PARS 2008 by suggesting that one of his Critical Elements of Performance be the development of an OSDA (Ontology for Segment Data Architecture) which is an ambitious undertaking requiring community of practice collaboration, serious vocabulary/ontology development skills and supporting contractors, etc. This will also address the need of the Data Reference Model 2.0 for a taxonomy and will be integrated with the work on mapping the Spatial Data Themes and the National Data Book Taxonomies in support of the Geospatial Line of Business (see Web 2.0 Wiki). We already have already integrated the Agency's information architecture (facets and sub-facets) with National Data Book Data architecture (tables and data elements).

5. Definition • Ontology - categories of interest in a domain and the relationships among them (note this is independent of syntax and technology) • Source: John Sowa, Knowledge Representation, 2000. • Ontologies – semantic models that are the basis for Semantic Web systems. • Source: Semantic Web for the Working Ontologist, page 1.

6. Structure • The Vocabulary & The Knowledgebase: A vocabulary is a machine readable, formal description of the meaning of “things” in a domain, to facilitate understanding and interoperability amongst systems, services, and participants. It is a seriously engineered artifact!: No short-cuts! Must enable query, not just search! • Community Vocabulary Engineering: The meaning of “things” in a vocabulary must be established by a community. The purpose of the vocabulary is to operate at the level of a community. Community vocabularies facilitate: Interoperability, Integration, Federation, and Cross-domain Analysis. • Defining the Meaning of “things”: OWL / RDF / SPARQL. Extensible framework for adding any types of properties to “things”. Ability to rigorously constrain properties. Built-in properties for cross-vocabulary alignment.

7. Structure (continued) • Tagging vs. Mark-up: Tagging enables search (e.g. ISO 1179, Dublin Core, Taxonomies) (search for tags yields thousands of results which lacks precision). Mark-up enables query (OWL + RDF) (query over an ontology produces targeted result sets that can be used at run-time) • Engineering Collaboration: Engineered artifacts can be created in a collaborative framework. • Best Practices: Start with a seed vocabulary before engaging the broader community. Determine the level of complexity needed in your engineering to achieve your mission (s). Determine the process for incrementing change in the vocabulary. Set-up a Governance model. Use a Semantic Wiki like Knoodl. • Source: Michael Lang, Collaborative Vocabulary Management, SICoP Special Conference, February 5, 2008.

8. Interface (in process) • Following the basic process for building an ontology, we state the Concept, give a Definition, and provide a Specific Example of how the Definition relates to the Concept. • Then we drill down into the parts of each of the three basic concepts above (e.g. Segments are composed of, etc.) and map them one to another (define and specify the ontological relationships). We will also have to harmonize the data elements and metadata across the Segments, Systems, and Data Models (a big, but necessary job!)

9. Interface (in process) http://gov2.wik.is/EPA_Enterprise_Architecture/Ontology_for_Segment_Data_Architecture

10. To Do List • 1. Start the Tutorial (like with previous Web 2.0 Wiki Pilots) using the new book for topics and examples, especially the FEARMO. • 2. Continue to collect and enter into the Wiki the metadata for segments, systems, and data models from the individuals representing each Segment in the EA Training. • 3. Select a graphics capability to use within the Wiki environment for visualizing the relationships (data models and ontologies). See recent email from Brand Niemann, Jr. on that. • 4. Socialize the idea of a Segment Reference Model Ontology (SRMO) across the Federal Enterprise!

11. Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL • Preface • Chapter 1: What Is the Semantic Web? • Chapter 2: Semantic Modeling • Chapter 3: RDF – The Basis of the Semantic Web • Chapter 4: Semantic Web Application Architecture • Chapter 5: RDF and Inferencing • Chapter 6: RDF Schema • Chapter 7: RDFS-Plus

12. Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL • Chapter 8: Using RDFS-Plus in the Wild • Chapter 9: Basic OWL • Chapter 10: Counting and Sets in OWL • Chapter 11: Using OWL in the Wild • Chapter 12: God and Bad Modeling Practices • Chapter 13: OWL Levels and Logic • Chapter 14: Conclusions

13. Preface • We realized that for this technology to take off, people other than mathematicians and logicians would have to learn the basics of semantic modeling. • We concentrated on a particular essential skill for constructing the Semantic Web: building useful and reusable models in the World Wide Web setting. • Thus we have focused on the “working ontologist” who was trying to create a domain model on the Semantic Web.

14. Preface • This book does not include everything we know about the Semantic Web – the examples are limited to modeling issues that arise around the problem of distributing structured knowledge over the Web. • Thus, the treatment focuses on how information is modeled for reuse and robustness in a distributed environment. • You should consider how the examples could be changed, adapted, or retargeted to model something in your personal domain. • My Note: That is what we are going to do – especially see the Federal Enterprise Architecture Reference Model Ontology in Chapter 11! (I helped foster this within SICoP).

15. Chapter 1: What Is the Semantic Web? • The AAA slogan-Anyone can say Anything about Any Topic. One of the basic tenets of the Web in general and the Semantic Web in particular. • Open world/closed world-A consequence of the AAA slogan is that there could always be something new that someone will say; this means that we must assume that there is always more information than could be known. • Nonunique naming-Since the speakers on the Web won’t necessarily coordinate their naming efforts, the same entity could be known by more than one name. • The network effect-The property of a web that makes its grow organically. The value of the network increases with the number of people who have joined, resulting in a virtuous cycle of participation.

16. Chapter 2: Semantic Modeling • Fundamental Concepts: • Modeling-Making sense of unorganized information. • Formality/Informality-The degree to which the meaning of a modeling language is given independent of a particular speaker or audience. • Commonality and Variability-A fundamental aspect of the Semantic Web that a model can represent. • Expressivity-The ability of the modeling language to describe certain aspects of the world. More expressive modeling languages can express a wider variety of statements about the model. Modeling languages of the Semantic Web – RDF, RDFS, and OWL – differ in their levels of expressivity.