The following was presented at the Semantic Technology conference in March of 2006 in San Jose California. This case study examines the extension of the National
Information Exchange Model NIEM to include K-12
education metadata. NIEM’s compliance with ISO/IEC
11179 metadata standards was found to be critical for
cost-effective system interoperability. This study indicates
that extending the NIEM can be compatible with newer
RDF and OWL metadata standards. We discuss how this
strategy will dramatically lower data integration costs and
make longitudinal data analysis more cost-effective. We
make recommendations for state education agencies,
federal policy makers, and metadata standards
organizations. The conclusion discusses the possible
impacts of recent innovations in collaborative metadata
standards efforts.
5. 1970 Sci-Fi Classic: “The Forbin Project” A New Intersystem Language! Lesson: Before you take over the world you must exchange semantically precise metadata!
6. Big Hairy Audacious Goals: Search Agents Legislator: What statewide programs increase test scores? District Superintendent: What “subgroups” in my district need the most help in math to meet NCLB guidelines? School Principal: What areas do new teachers need help in? Teacher: What areas do my students need the most help to pass statewide assessments?
7. “ Shopping” for Metadata Your “shopping cart” is full of Data Elements
14. NIEM Type “Classification Scheme” Domain Specific Student Teacher Common Aircraft Assessment Boat Case Clothing Activity Address Document Event Image Long/Lat Location Organization Person Residence Street Vehicle Universal Contact
15.
16.
17.
18.
19.
20.
21.
22. Hypertext Links and Data Element Links The Semantic Web Metadata Registry A Metadata Registry B The semantic web is about linking data elements in published metadata registries The Hypertext Web The current web is focused on linking published documents with HTML
23.
24.
25.
26.
27. Diagram From ISO-11179 Specification (1:1) DATA ELEMENT CONCEPT DATA ELEMENT Property (1:N) Object Class (1:1) Property (1:N) Object Class Representation (1:1) (1:1) Taken from Figure 1 "Fundamental Model for Data Elements" ISO/IEC 11179:1:2004(E) page 11 (non-normative) (1:N)
28. UML Model for RDF RDF Statement Subject Predicate ResourceValuedStatement LiteralValuedStatement Object Resource Property Literal TypedLiteral Object See Lee W. Lacy: OWL: Representing Information Using the Web Ontology Language p 82
29.
30.
31.
32.
33.
34. Model-Driven Development XML Form Editors Data Elements (500 Small XML Files) Data Dictionary (Single, Large XML File) Transforms (Saxon 8) Apache Ant HTML OWL FreeMind PDF MindManager Excel SQL Subversion RDBMS OLAP Cubes SemanticWorks Protégé Intranet Public Web Server
37. Store Semantic Mappings to Foreign Data Elements Directly in the Metadata Registry Current metadata registry standards do not clearly specify where and how semantic equivalence and precision is stored.
38.
39. Future: Semantic Mappers and Semantic Brokers Report Request In Model A Gartner: Vocabulary-based transformation XMLA: XML for Analysis Metadata Translation Service XML Response In Model A TDS In Model B Metadata Registry Model A Model B M etadata Mappings RDF Queries XML Results Data Warehouse (RDBMS) SQL or XMLA Queries In Model B
40.
41.
42.
43.
44.
45.
46.
47.
48.
Notes de l'éditeur
This is a case study. It is a story of how a team tried to build a data dictionary for the Minnesota Department of Education driven by the need for accurate long-term student assessment. I have struggled with weather to use “Ontology” in the title. I did not want to scare people off, but I feel that is what we are building. Some sources indicate that the difference between an taxonomy and an ontology is the difference between a tree and a graph but since my representation is much more complex then just a simple tree of data elements, I really should the word Ontology.