David Webber OASIS SET TC / CAM TC (with excerpts and summary from main presentation by

1. OASIS SET TC Use Case - iSURF Dictionary based interchanges for iSURF -An Interoperability Service Utility for Collaborative Supply Chain Planning across Multiple Domains David Webber OASIS SET TC / CAM TC (with excerpts and summary from main presentation by Prof. Dr. Asuman Dogac METU-SRDC Turkey)

2. METU OASIS SET TC Use Case • Part I: iSURF - Document Interoperability Requirements • Part II: Using Dictionary based approach and SET Tools for aligning structure components across syntax vocabularies

3. Part I: iSURF – Document Interoperability Requirements

4. Research Objectives: Public Domain Tools Supporting SMEs for Collaborative Supply Chain Planning • iSURF Semantic Interoperability Service Utility • iSURF Global Data Synchronization and Transitory Collaboration Service Utility for dynamic transient supply chain relationships for the SMEs

5. Existing iSURF Domain Syntax Format Alignment

6. Dictionary approach summary • If the document components of two different CCTS based standard share the same semantic properties: • Use this as an indication that they may be similar • Some explicitly defined semantic properties may imply further implicit semantic relationships: • Use a reasoner to obtain implicit relationships • Align to dictionary definitions allowing crosswalk • Create harmonized dictionary lookup • Use abstract UID as common reference (linkage between language specific named types/objects) • Explicate semantics related with the different usages of document data types in different document schemas to obtain some desired interpretations by means of such informal semantics • Determine similar/match relationships and rules for constraint alignment and compound component relationships (e.g. date-time vice date and time) • Provide dictionary structure format for managing relationships • Leverage existing OASIS CAM and ebXML Registry TC work

7. The current SET Harmonized Ontology • The current version of the harmonized ontology contains the ontological representations of: • All of the CCs and BIEs in CCL 07B • All of the BIEs in the common library of UBL 2.0 • All of the OAGIS 9.1 Common Components and Fields • All of the elements in the common library of GS1 XML • For supply chain applications these can be exactly related to existing well established UN/CEFACT dictionary objects (also foundation for CCTS) • Each UN/CEFACT dictionary object has explicit unique element designator – UID (any new items well be assigned their own domain UID).

8. Part II: Using dictionary based approach as SET Tools for aligning iSURF documents in different syntax

9. Semantic Properties of UN/CEFACT CCTS based Standards • The Core Components have the following semantic properties: • Core Component Data Types • Context • Code Lists • Object Class Term • Representation Term • The semantics that a BIE is based on a “Core Component” • UID labelling mechanism

10. The Upper Ontology for the Semantics Exposed by the CCTS Framework

11. A Specific Instance of the Problem • How to transform • UBL 2.0 Forecast Instance, to • GS1 XML Forecast Instance?

13. The first step… • Convert the XSDs of these document instances to CAM templates (forms abstraction layer for inspection by XSLT tools) • Extract dictionary definitions from templates into domain dictionaries; assign UID designators. • Merge dictionaries into one master dictionary • Combination of name, type and OWL ontology matching • Compare to UN/CEFACT dictionary – align UID designators • Assign similar / match rules for constraints/components • CAM xslt tool can be used to generate the dictionaries • Store results in harmonized dictionary format • http://camprocessor.sourceforge.net

14. CAM dictionary generation overview XSD schemas CAM Templates XSLT script Compare & Merge Components: Name Description Type Restrictions UID Master Dictionary

15. Dictionary Tools • Generate a dictionary of core components from a set of exchange templates • Separate dictionary content by namespace • Merges annotations and type definitions from exchange template into dictionary • Compare each exchange template to the master domain dictionary • Produce spreadsheet workbooks • Update spreadsheet and export back to dictionary core components

16. Create Dictionary – CAM process Select Dictionary; empty for new create, or existing for merge Output dictionary filename Select template content namespace to match with Merge mode; use true to combine content

17. Compare to Dictionary Pick dictionary to compare with Name of result cross-reference file

18. Open Cross-Reference as Spreadsheet

19. Explicate semantics related with the different usages of document data types • Different document standards use CCTS Data Types differently • For example, “Code.Type" in one standard is represented by “Text.Type" in another standard and yet with “Identier.Type" in another standard • This knowledge in real world is expressed through class equivalences so that not only the humans but also the reasoner knows about it • Code.Type ≡ Text.Type • Name.Type ≡ Text.Type • Identier.Type ≡ Text.Type • Can cross-reference via UID as well as type

20. Second Step • Human / OWL inspectors • Dictionary alignment report produces known equivalents listing (confidence 100%), and then lesser equivalence rankings based on matching factors • Component compound relationships resolved using CAM template structure layouts • Human inspection then reviews and resolves and updates dictionary (using Excel spreadsheet workbook format) • New dictionary produced • Iterative refinement over time can enhance alignment along with common practices through industry agreements

21. Addressing Structural Differences in Document Schemas • The harmonized ontology is effective only to discover equivalence of both semantically and structurally similar document artifacts • However Different document standards use core components in different structures • A problem in finding the similar artifacts in two different document schemas is that the semantically similar artifacts may appear at structurally different positions • This is solved using CAM templates and dictionary crosswalks on UID values along with match/similar designators and associated crosswalk rules

22. Example & UID alignment CAM templates + UID lookup in dictionary resolve structurally different schemas

23. CAM template / Dictionary / OWL R U L E S Source OWL Instance Target/Source XSD Document Schemas Upper Ontologies Harmonized Ontology CAM Template Equality Relations Dictionary Subsumption Relations Knowledge Base Rule Engine & Reasoner Source XML Instance Target XML Instance XSLT Script DATA LEVEL KNOWLEDGE LEVEL DATA LEVEL

24. Back to our problem: Translating iSURF Planning Documents Conforming to Different CCTS based Standards

25. A Specific Instance of the Problem • How to transform • UBL 2.0 Forecast Instance, to • GS1 XML Forecast Instance?

26. The above equivalences are discovered through the UID dictionary cross-references and can be stored back into CAM templates <Extensions> section for runtime crosswalk use.

27. Runtime crosswalks between template structure member items

28. Summary • Develop crosswalks: • Convert XSD schema to CAM templates • Leverage template structure and XPath rules to build dictionaries with UID labels • Build OWL relationships from schema • Compare each dictionary to master dictionary and reference OWL and type knowledge bases to align • Produce spreadsheet for manual review • Save final results back to master dictionary • Build runtime templates: • Compare individual CAM templates to master dictionary, generate cross-walk section between components • Cross-walk can contain alignment rules in XPath for content handling (e.g. code values and re-formatting)