1. Direction of Proposals for New Edition (E3) of ISO/IEC 11179
XMDR Working Group
Presentation to SC 32/WG 2 meeting
September, 2005
Toronto, Canada

2. Where have we been? Where are we now?…& where are we planning to go?
System manuals
Semantic grids
Data dictionaries
Semantics services (SSOA)
11179 E1
XMDR Project
11179 E2
XML & related standards
11179 E3
Terminologies, ontologies, etc.
Complex semantics management
Data engineering/XML Data
Semantics management for data
Data Standards/Data Administration
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3. Improvements in Semantic Management Technology
Semantics
Management
Code sets (E1) (E2) (E3) …
20943 & &
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4. The semantics challenge has evolved
Computer Era: 3rd Generation Languages - Challenge: Automated Data Processing – convert paper data systems to automated systems and improve processing. Coded data to save memory, disk & tape.
Began to identify data with meaningful names
Data naming methods were innovative and helpful
Described data using unstructured text in manuals and/or with comments embedded in software
Only visible & useful to programmers
Text/documents were not computerized (remember typewriters, stencils, mimeographs, carbon paper?)
ISO/IEC JTC 1/SC 14 developed standard code sets (valid values)
Focus: Nomenclature for data only
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5. The semantics challenge has evolved
Computer Era: Early DBMS, 4th GL query systems, word processing - Challenge: Manage data – schema integration, eliminate “bit-twiddling”
Document data in data dictionaries, software packages usually linked to a DBMS. Enforce “integrity constraints (e.g., valid values)”. Use “description” field to describe data
Manage data life cycle
Standard code sets (valid values) were useful, but difficult to manage – tended to be left behind by programming changes required to keep up with real world changes.
Data naming methods failed to achieve interoperation of content between applications and between organizations, but remain useful as human friendly identifiers
SC 14 began to develop methodology for data element standardization Edition 1 - Part 3, written in text, had ~15 attributes for data elements (editor-Netherlands)
Focus on standards for data elements
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6. The semantics challenge has evolved
Computer Era: DBMS, query systems, word processing - challenge: Manage data – DBMS schema integration, data quality (continued)
Began to model data and processes Modeling standards became useful, ERD, NIAM, UML
Word processing began to capture text documents
Keywords, glossaries, thesauri, and taxonomies became “machine readable”, but were treated as documents and were used manually
SC 14 Developed methodology for data element standardization
11179 (E1) Parts 4 & 5 covered data definitions and names.
Part 6 covered registration
Part 2 WD suggested development of a global taxonomy, then changed to specify classification attributes (term, definition & identifier) in Part 2 (E1)
All parts of were written in text.
Focus on managing data elements and classification of data elements
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7. The semantics challenge has evolved
Computer Era: Maturing Relational DBMS, Metadata Registries, XML, early WWW - challenge: Manage metadata, use terminology for data integration, data interoperability, data provenance, XML schema integration
SC 14 -> SC 32/WG 2. Developed Edition 2:
Broadened from data elements to management of all “administered items”.
11179 theme became “metadata registries”
Part 3 was expressed as a metamodel.
Part 3 included a “classification scheme region” (nodes & relationships) to improve semantics management
Link terms in definitions and valid values to terms and definitions in vocabularies and terminologies
Align concepts used in data with concepts used in text
Use computers to create and manage terminologies, thesauri, taxonomies
Part 2 (E2) restated the classification scheme region attributes from Part 3. (All Part 2 E1 attributes were included in Part 3 (E2)).
Focus on semantics for data and text
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8. The semantics challenge has evolved
Computer Era: WWW, Concept systems, XMDR - challenge: Semantics management & semantics Services
SC 32/WG 2 developing (E3). Proposals are made to extend semantics management and semantics services for MDR
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9. 11179 MDR Uses Data administration (design time) Data engineering
Design of databases, DB applications, XML Schemas
Design of messages
Concept specification: Terminologies, Taxonomies, Ontologies …
Data documentation
Data Integration & harmonization (design + run time)
Federated queries, data warehousing
Discovery of hidden relationships between data
Provide links between concepts and data
Support for interactive users (run time)
Data entry forms, output explanation
Data discovery
Show data provenance
Provide understanding of data and related concepts
Semantic Services (design + run time)
MDR metadata interchange
Ground concepts found in RDF statements and ontologies
Semantic computing
Semantic grids & semantic web services
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10. ISO/IEC 11179 MDR Standard Goals
Used to record and link:
Data elements
Data element concepts
Conceptual Domains
Value Domains: e.g, enumerated value domains
Classification Schemes
…..
Goal:
To record the unambiguous meaning of data
Human understandable semantics: Current paradigm is natural language definitions
For E3: Machine “Understandable”: Formal definitions (and axioms). Machine “understandable” in sense that computer can make use of concept systems for processing
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11. Advanced 11179 E3 Use Scenario
A User is concerned about a specific type of cancer
Wants to discover any documents on the web (reliable and unreliable sources) about the disease, causes, treatment, victims, and researchers
Wants to link concepts and individuals found in text to metadata and data in databases (where metadata/data relate to the concepts/individuals)
Wants to find relevant information where the terms used for the concepts vary: by regions, disciplines, scientific nomenclature, vernacular usage, language, and names of individuals.
Want to find information that is related through generalization and specialization and other relationships.
Note: No assumption of federation or central control over data and text generation. However, well managed concept systems and metadata (e.g., data definitions) help.
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12. Finding Hidden Information
Waterfowl
Waterfowl
Goose
Duck
Goose
Duck
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13. *Concept Use and Integration with 11179 Part 3, Edition 2
Conceptual Domain
Agent
Object Class
Chemopreventive
Agent
Valid Values
Cyclooxygenase Inhibitor
Doxercalciferol
Eflornithine …
Ursodiol
Data Element Concept
Chemopreventive Agent
NSC Number
Value Domain
NSC Code
Classification Schemes
caDSRTraining
Property
NSCNumber
Representation
Code
We talked about a Data Element being formed by a concept taking on a specific representation. In ISO terms this translates to the combination of a specific Data Element Concept and a specific Value Domain, you see this denoted in the middle of this chart by the yellow box outlined in red. caDSR administered items are backed by the use of externally defined terminologies and controlled vocabularies. With UMLS as a framework NCI has developed vocabulary services that are accessed via API – application to application interfaces – to provide touch points during creation of content, resulting in administered components that are bound to immutable concept codes. These touchpoints, denoted by the EVS logos, are currently implemented at the Object Class, Property, Representation Term, Value Domain and Valid Value levels of the metadata model.
Data Element
Chemopreventive Agent Name
Context
caCORE
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14. Semantic Management Extensions Goals for Edition 3
Sharable data that can easily be identified, shared, integrated, and made interoperable across information systems and organizations (a continuing challenge)
Unambiguous metadata characteristics to register semantic, syntactic and lexical information about data and text
Human AND machine “understandable”
Maintain backward compatibility with (E2) implementations.
Registration and management of any semantic information useful for administering and managing the content of data and text
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15. Semantic Management Extensions Goals for Edition 3
Specify disciplined way to manage linkage of concept systems (KOS) to administered items.
Improve the linkage of concept systems to data and text
Enable users to find correspondences between concepts in text and in data, where these are found in dispersed documents and databases. Concepts may be given linguistic expression with terms that vary by synonymy, discipline, region, language, etc.
Registration of semantics to facilitate concept (and data) mapping, inference, aggregation
Manage metadata for not only DBMS & XML schemas, but also for knowledge bases, concept systems, …
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16. Semantic Management Extensions Goals for Edition 3 (Continued)
Manage both data life cycle and ontology life cycle
Help to harmonize ontologies
Manage metamodels, reference ontologies & local ontologies
Restate Part 3 as an ontology and in Common Logic to enable use in Semantics technologies (Semantic Web, inference engines, reasoners, …).
Restate Part 3 using MOF
11179 registries provide support for ISO/IEC
Specify semantics services for a semantics service oriented architecture. Enabler for semantic computing, semantic agents, semantic grids.
Semantic services needed for semantic web and semantic grids to become part of ISO/IEC
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17. XMDR Intentions
We want to try capture existing thesauri, terminologies, ontologies as sources for the semantic specification of data elements to be used in databases, XML documents, messages, etc.
We want to incorporate more formal semantic specifications (e.g., ontologies, formal statements (axioms, sentences, ...)) to permit more precise semantic specifications (cf. to natural language definitions).
We want to incorporate formal semantic specifications to facilitate machine processing of semantic specifications, e.g., by inference engines, agents, etc. Such machine processing of semantic specifications can be used in support of federated database access, web service identification and coordination, agent-based computations, etc.
We want to provide a framework for the registration, harmonization, evolution and standardization of ontologies.
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18. Conceptual vs. Information Centric Metadata Standards
Information Artifacts
Metadata
OMG Standards:
MOF, CWM, UML
Ontology
Standards:
OWL, KIF, CL, ...
Connections ???
Terminology
Standards
Conceptual Level
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19. Space of Metadata Standards
ISO/IEC connects both conceptual models
and information artifacts.
About information artifacts:
data elements, schemas,
UML models, ...
Ontology Standards:
OWL, KIF, CL, XTM,
....
MMF &
ISO/IEC 11179
Edition 3
Metadata Registry
Standards
OMG Standards:
MOF, UML, CWM
Terminology
Standards
Conceptual models
of the “real world”
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20. ISO/IEC 11179 Metadata Registry Standard
Connects both:
Conceptual models of the real world:
Concepts, data element concepts, classification schemes
Terminologies, taxonomies, ontologies
Information Artifacts
Data elements, enumerated values, ...
UML models (e.g., in caDSR)
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21. “ ” US
SEMANTICS
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