1. eXtended Metadata Registry (XMDR)
Interagency/International Cooperation on Ecoinformatics
Washington DC
May 23, 2005
Bruce Bargmeyer,
Lawrence Berkley National Laboratory
University of California
Tel:

2. XMDR Project Background
Collaborative, interagency effort
EPA, USGS, NCI, Mayo Clinic, DOD, LBNL …& others
Draws on and contributes to interagency/International Cooperation on Ecoinformatics
Involves Ecoterm, international, national, state, local government agencies, other organizations
Recognizes great potential of semantic computing, management of metadata
Improving collection, maintenance, dissemination, processing of very diverse data structures
Collaboration arises from needs for traditional data administration, for sharing data across multiple organizations, for managing complex semantics associated with data, and for emerging semantics computing capbilities
Collaborative, Interagency effort
Draws on and Contributes to Interagency Cooperation on Ecoinformatics Involving International, National, State and Local Government Agencies and other Organizations
Recognizes Great Potential of Semantics-based Computing, Management of Metadata for Improving Collection, Maintenance, Dissemination, Processing of Very Diverse Data Structures
Collaboration Arises from the Need to Share Diverse Data Across Multiple Organizations
Many Players, Many Interests…Shared Context

3. 11179 Metadata Registries Extensions
Register (and manage) any semantics that are useful for managing data.
E.g., this may include registering not only permissible values (concepts), definitions, but may extend to registration of the full concept systems in which the permissible values are found.
E.g., may want to register keywords, thesauri, taxonomies, ontologies, axiomitized ontologies….
Support traditional data management and data administration
Lay Foundation for semantic computing: Semantics Service Oriented Architecture, Semantic Grids, Semantics based workflows, Semantic Web ….

4. XMDR Draws Together Metadata Terminology Registries Users
CONCEPT
Referent
Refers To
Symbolizes
Stands For
“Rose”,
“ClipArt”
Metadata Registry
Terminology Thesaurus Themes
Data
Standards
Ontology
GEMET
Structured
Metadata
11179 Metadata Registry

5. What is Metadata? What is Terminology (a concept system)?
Users
Metadata
Registries
Terminology
CONCEPT
Referent
Refers To
Symbolizes
Stands For
“Rose”,
“ClipArt”
Metadata Registry
Terminology Thesaurus Themes
Data
Standards
Ontology
GEMET
Structured
Metadata
11179 Metadata Registry

6. Metadata Registries Data Element Concept Data Elements
Name: Country Identifiers
Context:
Definition:
Unique ID: 5769
Conceptual Domain:
Maintenance Org.:
Steward:
Classification:
Registration Authority:
Others
Afghanistan
Belgium
China
Denmark
Egypt
France
Germany
…………
Data Element
Concept
Data Elements
Afghanistan
Belgium
China
Denmark
Egypt
France
Germany
…………
AFG
BEL
CHN
DNK
EGY
FRA
DEU
…………
004
056
156
208
818
250
276
…………
Name:
Context:
Definition:
Unique ID: 4572
Value Domain:
Maintenance Org.:
Steward:
Classification:
Registration Authority:
Others
Name:
Context:
Definition:
Unique ID: 3820
Value Domain:
Maintenance Org.:
Steward:
Classification:
Registration Authority:
Others
Name:
Context:
Definition:
Unique ID: 1047
Value Domain:
Maintenance Org.:
Steward:
Classification:
Registration Authority:
Others
In order to reduce costs associated with managing metadata, we want to enable interchange of metadata including terminology between metadata registries.
Organizations that are responsible for particular terminology and data elements can propagate these changes to other metadata registries.
ISO 3166
English Name
ISO 3166
3-Alpha Code
ISO 3166
3-Numeric Code

7. What is Metadata/Terminology?
Fuji
Variety name
Product
look-up
(PLU) code
4129
Product of
Canada
Country of
origin
PLU codes consist of 4 to 5 numbers
4 numbers = conventional produce
5 numbers, starting with 9 = organic produce
5 numbers, staring with 8 = genetically engineered produce
PLU codes are established by the International Federation for Produce Coding,
A coalition of fruit and vegetable associations coordinated by the
Produce Marketing Association.

8. What is Metadata/Terminology?
Fuji
Variety name
Product
look-up
(PLU) code
4129
Product of
Canada
Country of
origin
New PLUs are assigned by the PEIB.  More information about the
PEIB may be found at their website:
Produce Electronic Information Board.

9. What is Metadata/Terminology?
Fuji
Variety name
4129
Product
look-up
(PLU) code of
Canada
Country
origin
4128
Apple, Cripps Pink, Small, 100 size and smaller, less than 205g, [Notes: As of 1 Jun 2001, Pink Lady® is a registered trademark of certain Cripps Pink apples, Last revised: 1 Jun 2002]
4129
Apple, Fuji, Small, 100 size and smaller, less than 205g
4130
Apple, Cripps Pink, Large, 88 size and larger, 205g and above, [Notes: As of 1 Jun 2001, Pink Lady® is a registered trademark of certain Cripps Pink apples, Last revised: 1 Jun 2002]
4131
Apple, Fuji, Large, 88 size and larger, 205g and above

10. What is Metadata/Terminology?
Fuji
Variety name
4129
Product
look-up
(PLU) code of
Canada
Country
origin
Fruit: the developed ovary of a seed plant with its contents and accessory parts, as the pea pod, nut, tomato, or pineapple.
Fruit
Apple
Orange

11. What is Metadata/terminology?
Fuji
Variety name
4129
Product
look-up
(PLU) code of
Canada
Country
origin
fruit
Fruit (frÁt), n., pl. fruits, (esp. collectively) fruit, v. –n.
1. any product of plant growth useful to humans or animals.
2. the developed ovary of a seed plant with its contents and accessory parts, as the pea pod, nut, tomato, or pineapple.
3. the edible part of a plant developed from a flower, with any accessory tissues, as the peach, mulberry, or banana.
4. the spores and accessory organs of ferns, mosses, fungi, algae, or lichen.
5. anything produced or accruing; product, result, or effect; return or profit: the fruits of one's labors.
6. Slang (disparaging and offensive). a male homosexual.
7. –v.i., v.t. to bear or cause to bear fruit: a tree that fruits in late summer; careful pruning that sometimes fruits a tree.

12. What is Metadata/Terminology?
Fuji
Variety name
4129
Product
look-up
(PLU) code of
Canada
Country
origin
fruit
Fruit
Apple
Orange
Fly
Horse
Fruit
Flies
Fruit flies lay eggs in fruit

13. What is Terminology? “Rose”, “ClipArt” Refers To Symbolizes Stands For
CONCEPT
Referent
Refers To
Symbolizes
Stands For
“Rose”,
“ClipArt”
C.K. Ogden/I.A. Richards, The Meaning of Meaning
A Study in the Influence of Language upon Thought and The Science of Symbolism
London 1923, 10th edition 1969

14. Registering Terminology
Definition: Any of several game fishes of the genus Salmo, related to the salmon...
Concept
Refers To
Symbolizes
Term
Referent
Stands For
trout
Salmo trutta
brown trout
truite

15. Registering Terminology
any of several game fishes of the genus Salmo, related to the salmon...
Concept
Terms
Context
trout
Salmo trutta
truite
common name
scientific name
French name
UID=6349

16. Concepts into Data Data Elements Name: trout species
Definition: The names of species of trout.
Values:
brook trout Salvelinus fontinalis brown trout Salmo trutta cutthroat trout Oncorhynchus clarkii
Terms
Context
Concept
Brown trout
Salmo trutta
truite
common name
scientific name
French name
UIN=6349

17. XML Schemas EDI Messages
Systems: STORET Envirofacts
DBMS Query
XML Schemas EDI Messages
Data Interchange
W3C RDF
Vocabularies
Ontology
Data Elements
Terms
Context
Concept
Brown trout
Salmo trutta
truite
common name
scientific name
French name
UIN=6349

18. Continuing Challenge Synonyms, Homonyms, Provenance
Synonyms: so many ways to name, identify, and state the same thing (one concept--many terms)
Homonyms: different meanings for the same terms and identifiers (one term—many concepts)
Provenance: How to record the who, where, when, why, and how that is relevant to data

19. Two Points of View I wanna be free:
Programs, system developers, scientists, … that want to get something done quickly, without the drag of documentation and uniformity.
Let me do it quickly, my way, and let others accept it.
Coherence within some large Universe of Discourse
Data users who want to get a coherent view across the boundaries of individual programs, organizations, scientific studies. E.g., media specific programs.
Harmonize and standardize data and terminology. Document data/terminology in structured ways. Then easier to find, access, analyze, understand and use data.
Market driven approaches to data management may provide a means to draw these closer together. E.g., anyone can register anything, and a community of interest gives it some declared level of acceptance.

20. Data Management Evolution
Trying to manage semantics: What does data mean? Can data be compared? What is the provenance of data? [Freedom vs. Coherence]
3rd Generation languages – naming conventions, system documentation
Data Base Management Systems – Data dictionary for schema, valid values, etc. Metadata Registries for data sharing organization-wide or across environmental domain of discourse
XML – Metadata Registries and XML registries for managing XML tags, data, and XML artifacts.
Semantic computing – Metadata Registries for managing the “vocabulary” and concept systesm, e.g., ontologies.

21. Movement Toward Semantics Management
Going beyond traditional Data Standards and Data Administration
In addition to anchoring data with definitions, we want to process data and concepts based on context and relationships, possibly using inferences and rules.
In addition to natural language, we want to capture semantics with more formal description techniques
FOL, DL, Common Logic, OWL
Going beyond information system interoperability and data interchange to processing based on
inferences and
probabilistic correspondence between concepts found in natural language (in the wild) and both data in databases and concepts found in concept systems.

22. Purposes of XMDR Project
1. Propose revisions to Parts 2 & 3 (3rd Ed.) – to serve as the design for the next generation of metadata registries.
2. Demonstrate Reference Implementation – to validate the proposed revisions
Extend semantics management capabilities
Enable registration of correspondences between multiple concept systems and between concept systems and data
Explore uses of terminologies and ontologies
Systematize representation of concepts and relationships
Enable registration of metadata for knowledge bases
Adapt & test emerging semantic technologies
Provide an environment for developing and interrelating ontologies

23. What is an ontology?
The subject of ontology is the study of the categories of things that exist or may exist in some domain. The product of such a study, called an ontology, is a catalog of the types of things that are assumed to exist in a domain of interest D from the perspective of a person who uses a language L for the purpose of talking about D. The types in the ontology represent the predicates, word senses, or concept and relation types of the language L when used to discuss topics in the domain D.
Building, Sharing, and Merging Ontologies-John F. Sowa

24. Terminolocgical & Formal (Axiomatized) Ontologies
The difference between a terminological ontology and a formal ontology is one of degree: as more axioms are added to a terminological ontology, it may evolve into a formal or axiomatized ontology.
Cyc has the most detailed axioms and definitions; it is an example of an axiomatized or formal ontology. EDR and WordNet are usually considered terminological ontologies.
Building, Sharing, and Merging Ontologies
John F. Sowa

25. An Axiom for an Axiomatized Ontology
Definition: The resource_cost_point predicate, cpr, specifies the cost_value, c, (monetary units) of a resource, r, required by an activity, a, upto a certain time point, t.
If a resource of the terminal use or consume states, s, for an activity, a, are enabled at time point, t, there must exist a cost_value, c, at time point, t, for the activity, a,that uses or consumes the resource, r. The time interval, ti = [ts, te], during which a resource is used or consumed byan activity is specified in the use or consume specifications as use_spec(r, a, ts, te, q) or consume_spec(r, a, ts, te, q) where activity, a, uses or consumes quantity, q, of resource, r, during the time interval [ts, te]. Hence,
Axiom:∀ a, s, r, q, ts, te, (use_spec(r, a, ts, te, q)∧ enabled(s, a, t))∨ (consume_spec(r, a, ts, te, q)∧ enabled(s, a, t))≡∃c, cpr(a,c,t,r)
Cost Ontology for TOronto Virtual Enterprise (TOVE)

26. Samples of Eco & Bio Graph Data
Nutrient cycles in microbial ecologies These are bipartite graphs, with two sets of nodes, microbes and reactants (nutrients), and directed edges indicating input and output relationships. Such nutrient cycle graphs are used to model the flow of nutrients in microbial ecologies, e.g., subsurface microbial ecologies for bioremediation.
Chemical structure graphs: Here atoms are nodes, and chemical bonds are represented by undirected edges. Multi-electron bonds are often represented by multiple edges between nodes (atoms), hence these are multigraphs. Common queries include subgraph isomorphism. Chemical structure graphs are commonly used in chemoinformatics systems, such as Chem Abstracts, MDL Systems, etc.
Sequence data and multiple sequence alignments . DNA/RNA/Protein sequences can be modeled as linear graphs
Topological adjacency relationships also arise in anatomy. These relationships differ from partonomies in that adjacency relationships are undirected and not generally transitive.

27. Eco & Bio Graph Data (Continued)
Taxonomies of proteins, chemical compounds, and organisms, ... These taxonomies (classification systems) are usually represented as directed acyclic graphs (partial orders or lattices). They are used when querying the pathways databases. Common queries are subsumption testing between two terms/concepts, i.e., is one concept a subset or instance of another. Note that some phylogenetic tree computations generate unrooted, i.e., undirected. trees.
Metabolic pathways: chemical reactions used for energy production, synthesis of proteins, carbohydrates, etc. Note that these graphs are usually cyclic.
Signaling pathways: chemical reactions for information transmission and processing. Often these reactions involve small numbers of molecules. Graph structure is similar to metabolic pathways.
Partonomies are used in biological settings most often to represent common topological relationships of gross anatomy in multi-cellular organisms. They are also useful in sub-cellular anatomy, and possibly in describing protein complexes. They are comprised of part-of relationships (in contrast to is-a relationships of taxonomies). Part-of relationships are represented by directed edges and are transitive. Partonomies are directed acyclic graphs.
Data Provenance relationships are used to record the source and derivation of data. Here, some nodes are used to represent either individual "facts" or "datasets" and other nodes represent "data sources" (either labs or individuals). Edges between "datasets" and "data sources" indicate "contributed by". Other edges (between datasets (or facts)) indicate derived from (e.g., via inference or computation). Data provenance graphs are usually directed acyclic graphs.

28. A graph theoretic characterization
Readily comprehensible characterization of metadata structures
Graph structure has implications for:
Integrity Constraint Enforcement
Data structures
Query languages
Combining metadata sets
Algorithms for query processing

29. Definition of a graph Graph = vertex (node) set + edge set
Nodes, edges may be labeled
Edge set = binary relation over nodes
cf. NIAM
Labeled edge set
RDF triples (subject, predicate, object)
predicate = edge label
Typically edges are directed

30. Example of a graph
infectious disease
is-a
is-a
influenza
measles

31. Types of Metadata Graph Structures
Trees
Partially Ordered Trees
Ordered Trees
Faceted Classifications
Directed Acyclic Graphs
Partially Ordered Graphs
Lattices
Bipartite Graphs
Directed Graphs
Cliques
Compound Graphs

32. Graph Taxonomy Graph Directed Graph Undirected Graph
Directed Acyclic Graph
Bipartite Graph
Clique
Partial Order Graph
Faceted Classification
Lattice
Partial Order Tree
Tree
Note: not all bipartite graphs
are undirected.
Ordered Tree

33. Trees In metadata settings trees are almost most often directed
edges indicate direction
In metadata settings trees are usually partial orders
Transtivity is implied (see next slide)
Not true for some trees with mixed edge types.
Not always true for all partonomies

34. Example: Tree California part-of part-of Alameda County
Santa Clara County
part-of
part-of
part-of
part-of
Oakland
Berkeley
Santa Clara
San Jose

35. Trees - cont. Uniform vs. non-uniform height subtrees
fixed number of levels
common in dimensions of multi-dimensional data models
Non-uniform height subtrees
common terminologies

36. Partially Ordered Trees
A conventional directed tree
Plus, assumption of transitivity
Usually only show immediate ancestors (transitive reduction)
Edges of transitive closure are implied
Classic Example:
Simple Taxonomy, “is-a” relationship

37. Example: Partial Order Tree
Disease
is-a
is-a
Infectious Disease
Chronic Disease
is-a
is-a
is-a
is-a
Polio
Smallpox
Diabetes
Heart disease
Signifies inferred is-a relationship

38. Ordered Trees
Order here refers to order among sibling nodes (not related to partial order discussed elsewhere)
XML documents are ordered trees
Ordering of “sub-elements” is to support classic linear encoding of documents

39. Example: Ordered Tree Paper part-of part-of part-of Title page Section
Bibliography
Note: implicit ordering relation among parts of paper.

40. Faceted Classification
Classification scheme has mulitple facets
Each facet = partial order tree
Categories = conjunction of facet values (often written as [facet1, facet2, facet3])
Faceted classification = a simplified partial order graph
Introduced by Ranganathan in 19th century, as Colon Classification scheme
Faceted classification can be descirbed with Description Logc, e.g., OWL-DL

41. Example: Faceted Classification
Wheeled Vehicle Facet
Vehicle Propulsion Facet
is-a
is-a
is-a
is-a
is-a 2
wheeled 3
wheeled 4
wheeled
Human
Powered
Internal
Combustion
is-a
is-a
is-a
is-a
is-a
is-a
is-a
is-a
is-a
Bicycle
Tricycle
Auto
Motorcycle

42. Faceted Classifications and Multi-dimensional Data Model
MDM – a.k.a. OLAP data model
Online Analytical Processing data model
Star / Snowflake schemas
Fact Tables
fact = function over Cartesian product of dimensions
dimensions = facets
geographic region, product category, year, ...

43. Directed Acylic Graphs
Directed edges
No cycles
No assumptions about transitivity (e.g., mixed edge types, some partonomies)
Nodes may have multiple parents
Examples:
Partonomies (“part-of”) - transitivity is not always true

44. Example: Directed Acyclic Graph
Vehicle
Wheeled
Vehicle
is-a
is-a
Propelled
Vehicle
is-a
is-a
is-a
is-a
is-a 3
Wheeled
Vehicle 4
Wheeled
Vehicle
Human
Powered
Vehicle 2
Wheeled
Vehicle
Internal
Combustion
Vehicle
is-a
is-a
is-a
is-a
is-a
is-a
is-a
is-a
is-a
Bicycle
Tricycle
Auto
Motorcycle

45. Partial Order Graphs Directed acyclic graphs + inferred transitivity
Nodes may have multiple parents
Most taxonomies drawn as transitive reduction, transitive closure edges are implied.
Examples:
all taxonomies
most partonomies
multiple inheritance
POGs can be described in Description Logic, e.g., OWL-DL

46. Example: Partial Order Graph
Vehicle
Wheeled
Vehicle
is-a
is-a
Propelled
Vehicle
is-a
is-a
is-a
is-a
is-a 2
Wheeled
Vehicle 3
Wheeled
Vehicle 4
Wheeled
Vehicle
Human
Powered
Vehicle
Internal
Combustion
Vehicle
is-a
is-a
is-a
is-a
is-a
is-a
is-a
is-a
is-a
Bicycle
Tricycle
Auto
Motorcycle
Dashed line = inferred is-a (transitive closure)

47. Directed Graph Generalization of DAG (directed acyclic graph)
Cycles are allowed
Arises when many edge types allowed
Example: UMLS

48. Lattices A partial order For every pair of elements A and B Example:
There exists a least upper bound
There exists a greatest lower bound
Example:
The power set (all possible subsets) of a finite set
LUB(A,B) = union of two sets A, B
GLB(A,B) = intersect of two sets A,B

49. Example Lattice: Powerset of 3 element set
{a,b,c}
{a,b}
{a,c}
{b,c}
{c}
{a}
{b}
Empty Set
Denotes subset

50. Lattices - Applications
Formal Concept Analysis
synthesizing taxonomies
Machine Learning
concept learning

51. Bipartite Graphs Vertices = two disjoint sets, V and W
All edges connect one vertex from V and one vertex from W
Examples:
mappings among value representations
mappings among schemas
(entity/attribute, relationship) nodes in Conceptual Graphs

52. Example Bipartite Graph
California CA
Massachusetts MA
Oregon OR
States
Two-letter state codes

53. Clique Clique = complete graph (or subgraph)
all possible edges are present
Used to represent equivalence classes
Typically, on undirected graphs

54. Example of Clique
California
Calif. CA
CAL
Here edges denote synonymy.

55. Compound Graphs
Edges can point to/from subgraphs, not just simple nodes
Used in conceptual graphs
CG is isomorphic to First Order Logic
Could be used to specify contexts for subgraphs

56. Example Compound Graph
Colin
Powell
claimed
had
Iraq
WMDs

57. Conclusions
We can characterize metadata structure in terms of graph structures
Partial Order Graphs are the most common structure:
used for taxonomies, partonomies
support multiple inheritance, faceted classification
implicit inclusion of inferred transitive closure edges

58. Challenges How to register & manage the various graph structures?
DBMS, File systems ….
How to query the graph structures?
XQuery for XML
Poor to non-existent graph query languages
How to get adequate performance, even in high performance computing environment
User interface complexity
How to manage semantic drift
Versions
How to interrelate graphs with other graphs and with data
Granularity at which to register metadata (then point to greater detail elsewhere?)

59. How can Terminologies and Ontologies help Manage Metadata?
At the level of metadata instances in a registry, connect metadata entities via shared terms
via automatic indexing of metadata words
via text values from specific metadata elements
At the level of the (or other) metamodel, ontologies can help specify formal relationships
is-a and part-of hierarchies, etc.
Inheritance, aggregation, …
for automatic searching of sub-classes & inverses
to specify semantic pathways for indexing

60. Major Tasks, Deliverables & Milestones
Initial Architecture Design
Present Proposed Part 2 Revisions to SC32
WG2 mtg in DC
Research and Development
Task/Deliverable
Test Implementation (External Users)
Prepare Draft Revision of Part 2 for SC32
mtg in Berlin
System Test & Evaluation (Internal Participants)
Identify, Select Metadata Sources
Identify, Select Technologies
Develop Project Plan
System TestDevelop Project Plan - LBNL – Jul 04
Develop and Test RI – Extend (Revise) Part 2 Standard April 05 – LBNL
Identify, Select Appropriate Technologies – DoD – Dec 04
Identify, Select Metadata Sources – LBNL – Dec 04
Content Assessment and Selection – LBNL – Aug – Dec 04
Install Contents into Test and Demo System – LBNL – Dec 04 to End of Project
Test and Demo System Metadata Content Selection and Prioritization
Test and Demo System Content Acquisition
Test and Demo System Content Installation
System Test and Feedback – All – NLT Feb 05
Technical Assistance – All – As Required
General
Limited User Interface
Registration of Content will be Prioritized to Ensure Optimal Coverage (esp. Across versions, domains)
Help Functions will be Limited to Loading Metadata Sources and Testing
Query Optimization will be Limited
Most Deliverable Documents will be to Recommend Choices During RDT&E of the RI.
They will be Working Documents and Not Necessarily Publication Quality
Project Duration is Expected to be July 04 – May 05; and may be extended if additional resources are available
Gantt Chart Forthcoming

61. General Tasks/Intentions
Limited Query Optimization
Brief ISO/IEC L8
Documents will Recommend Choices
Task/Intention
Brief DOD Metadata Working Group
Brief IC Metadata Working Group
Limited Help Functions
Prioritized Content Registration
Limited User Interface - Initially
General
Limited User Interface
Registration of Content will be Prioritized to Ensure Optimal Coverage (esp. Across versions, domains)
Help Functions will be Limited to Loading Metadata Sources and Testing
Query Optimization will be Limited
Most Deliverable Documents will be to Recommend Choices During RDT&E of the RI.
They will be Working Documents and Not Necessarily Publication Quality
Project Duration is Expected to be July 04 – May 05; and may be extended if additional resources are available
Will Seek to Promote Awareness

62. Potential Standards/Technologies
DBMS
Object, XML, Relational, RDF/Graph, Logic, Text, Document, Multimedia
Knowledge Representation
Web Ontology Language (OWL)
Common Logic (CL)
Middleware/Messaging
Cocoon 2, Jini, CoABS, JMS, XMLBlaster, SOAP
XML [Semantic] Web Services
Axis, JWSDP
Agent Development
ABLE, JADE
Engines/Servers
OMS (IBM), Federator/OMS (OWI)
Jess
Open Source and Risk Tolerant

63. Architecture Approach
Fully modular approach
Exemplars:
Apache Web Server
Eclipse IDE
Protégé Ontology Editor
Benefits:
numerous modules are relatively easy to implement
clean separation of concerns and high reusability and portability
tooling support required is minimal
Collaborative, Interagency effort to Extend the Semantics Management Capabilities of ISO/IEC 11179
Test and Demonstrate Extended Capabilities with a Reference Implementation
Serve as a Design for Operational Registries
Focuses on Extending Classification and Terminology Aspects Specified in Part 2
Adapting and Adopting Useful Emerging Technologies
Result of Work will be Draft Documents that Propose Revisions for ISO/IEC Parts 2 and 3 as Version 3
Additionally, the Reference Implementation (RI) Prototype will Help Resolve Issues of How to Register, Manage, and Interrelate Complex Metadata Standards

64. XMDR Prototype Architecture: Initial Implemented Modules
Registry
External
Interface
RegistryStore
WritableRegistryStore
MetadataValidator
AuthenticationService
MappingEngine
Java
RetrievalIndex
FullTextIndex
Subversion
Ontology Editor
Protege
11179 OWL Ontology
Jena, Xerces
LogicBasedIndex
Jena, OWI KS
Racer
Lucene
Generalization
Composition (tight ownership)
Aggregation (loose ownership)

65. XMDR Content Priority List
Phase 1
(V.A) National Drug File Reference Terminology (?)
DTIC Thesaurus (Defense Technology Info. Center Thesaurus)
NCI Thesaurus National Cancer Institute Thesaurus
NCI Data Elements (National Cancer Institute Data Standards Registry
UMLS (non-proprietary portions)
GEMET (General Multilingual Environmental Thesaurus)
EDR Data Elements (Environmental Data Registry)
ISO 3166 Country Codes – from EPA EDR
USGS Geographic Names Information System (GNIS)
Collaborative, Interagency effort to Extend the Semantics Management Capabilities of ISO/IEC 11179
Test and Demonstrate Extended Capabilities with a Reference Implementation
Serve as a Design for Operational Registries
Focuses on Extending Classification and Terminology Aspects Specified in Part 2
Adapting and Adopting Useful Emerging Technologies
Result of Work will be Draft Documents that Propose Revisions for ISO/IEC Parts 2 and 3 as Version 3
Additionally, the Reference Implementation (RI) Prototype will Help Resolve Issues of How to Register, Manage, and Interrelate Complex Metadata Standards

66. XMDR Content Priority List
Phase 2
LOINC Logical Observation Identifiers Names and Codes
ITIS Integrated Taxonomic Information System
Getty Thesaurus of Geographic Names (TGN)
SIC (Standard Industrial Classification System)
NAICS (North American Industrial Classification System)
NAIC-SIC mappings
UNSPSC (United Nations Standard Products and Services Codes)
EPA Chemical Substance Registry System
EPA Terminology Reference System
ISO Language Identifiers ISO Part 3
IETF Language Identifiers RFC 1766
Units Ontology
Collaborative, Interagency effort to Extend the Semantics Management Capabilities of ISO/IEC 11179
Test and Demonstrate Extended Capabilities with a Reference Implementation
Serve as a Design for Operational Registries
Focuses on Extending Classification and Terminology Aspects Specified in Part 2
Adapting and Adopting Useful Emerging Technologies
Result of Work will be Draft Documents that Propose Revisions for ISO/IEC Parts 2 and 3 as Version 3
Additionally, the Reference Implementation (RI) Prototype will Help Resolve Issues of How to Register, Manage, and Interrelate Complex Metadata Standards

67. XMDR Content Priority List
Phase 3
HL7 Terminology
HL7 Data Elements
GO (Gene Ontology)
NBII Biocomplexity Thesaurus
EPA Web Registry Controlled Vocabulary
BioPAX Ontology
NASA SWEET Ontologies
NDRTF
Collaborative, Interagency effort to Extend the Semantics Management Capabilities of ISO/IEC 11179
Test and Demonstrate Extended Capabilities with a Reference Implementation
Serve as a Design for Operational Registries
Focuses on Extending Classification and Terminology Aspects Specified in Part 2
Adapting and Adopting Useful Emerging Technologies
Result of Work will be Draft Documents that Propose Revisions for ISO/IEC Parts 2 and 3 as Version 3
Additionally, the Reference Implementation (RI) Prototype will Help Resolve Issues of How to Register, Manage, and Interrelate Complex Metadata Standards

68. Coming Year (Proposed)
Extension of XMDR core – data & system
Semantic Services
Greater interaction with Ecoterm organizations
Interaction with Ecoinformatics Test Bed project

69. Acknowledgements and References
Frank Olken, LBNL
Kevin Keck, LBNL
John McCarthy, LBNL