1. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 1 SC 32 Tutorial Session WG 2 eXtended Metadata Registry (XMDR) April 19, 2005 Bruce Bargmeyer, Lawrence Berkley National Laboratory University of California

2. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 2 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 semantics based computing: Semantics Service Oriented Architecture, Semantic Grids, Semantics based workflows, Semantic Web ….

3. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 3 XMDR Project Background  Collaborative, interagency effort  DOD, EPA, USGS, NCI, Mayo Clinic, LBNL …& others  Draws on and contributes to interagency cooperation on ecoinformatics  Involves international, national, state, local government agencies, other organizations  Recognizes great potential of semantics-based 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 semantics based computing. Many Players, Many Interests…Shared Context

4. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 4 Purposes of XMDR Project 1. Propose revisions to 11179 Parts 2 & 3 (3 rd 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

5. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 5 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, and axioms  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.

6. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 6 11179 Semantics Management Real World Modeling Methods & Terminology Model Artifacts, Data Exchange, Concept Systems Applications Methodologies: EDR, NIAM, O-O, RDF, UML, Ontology CDIF, (UML- MDL, XMI), OWL, CL & SCL (KIF) SQL, Object, RDF, Semantic Web Metamodel constructs: CDIF Core, MOF, XML-Schema, RDFS, ODM, OWL, CL MDR Semantics Management: Data elements, Domains, Concepts, Terms …

7. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 7 SC 32 Standards & Projects Real World Modeling Tools Applications Methodologies: EDR, NIAM, O-O, RDF, UML, Ontology CDIF, (UML- MDL, XMI), OWL, CL & SCL (KIF) SQL, Object, RDF, Semantic Web WG 3 - SQL Metamodel constructs: CDIF Core, MOF, XML-Schema, RDFS, ODM, OWL, CL WG 2 - MMF (19763), CL (24707) & MOF PAS submission WG 2 - MMF (19763), CL (24707) & XMI PAS submission WG 2 – 11179 WG 2 - 20944 MDR Semantics Management: Data elements, Domains, Concepts, Terms … WG2 - 20943 Modeling Methods & Terminology Model Artifacts, Data Exchange, Concept Systems

8. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 8 XMDR Focus Methodologies: EDR, NIAM, O-O, RDF, UML, Ontology CDIF, (UML- MDL, XMI), OWL, CL & SCL (KIF) SQL, Object, RDF, Semantic Web WG 3 - SQL Metamodel constructs: CDIF Core, MOF, XML-Schema, RDFS, ODM, OWL, CL WG 2 - MMF (19763), CL (24707) & MOF PAS submission WG 2 - MMF (19763), CL (24707) & XMI PAS submission XMDR project Real World Modeling Methods & Terminology Applications WG 2 – 11179 Parts 2 & 3 WG 2 - 20944 MDR Semantics Management: Data elements, Domains, Concepts, Terms … WG2 - 20943 Model Artifacts, Data Exchange, Concept Systems

9. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 9 Drawing Together Metadata Registry Terminology Thesaurus Themes Data Standards Ontology GEMET Structured Metadata Users Registries Terminology CONCEPT Referent Refers To Symbolizes Stands For “Rose”, “ClipArt”

10. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 10 Data Element Concept Afghanistan Belgium China Denmark Egypt France Germany ………… Data Elements AFG BEL CHN DNK EGY FRA DEU ………… ISO 3166 English Name ISO 3166 3-Numeric Code 004 056 156 208 818 250 276 ………… ISO 3166 3-Alpha Code Afghanistan Belgium China Denmark Egypt France Germany ………… 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 Name: Country Identifiers Context: Definition: Unique ID: 5769 Conceptual Domain: Maintenance Org.: Steward: Classification: Registration Authority: Others

11. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 11 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

12. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 12 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.formal ontologyterminological ontologies Building, Sharing, and Merging Ontologies John F. Sowa

13. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 13 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)

14. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 14 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.

15. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 15 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.

16. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 16 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

17. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 17 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

18. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 18 Example of a graph infectious disease influenza measles is-a

19. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 19 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

20. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 20 Graph Taxonomy Directed Graph Directed Acyclic Graph Graph Undirected Graph Bipartite Graph Partial Order Graph Faceted Classification Clique Partial Order Tree Tree Lattice Ordered Tree Note: not all bipartite graphs are undirected.

21. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 21 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

22. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 22 Example: Tree Oakland Berkeley Alameda County California part-of Santa Clara San Jose Santa Clara County part-of

23. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 23 Trees - cont.  Uniform vs. non-uniform height subtrees  Uniform height subtrees  fixed number of levels  common in dimensions of multi-dimensional data models  Non-uniform height subtrees  common terminologies

24. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 24 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

25. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 25 Example: Partial Order Tree PolioSmallpox Infectious Disease Disease is-a Diabetes Heart disease Chronic Disease is-a Signifies inferred is-a relationship

26. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 26 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

27. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 27 Example: Ordered Tree part-of Paper Title page Section Bibliography Note: implicit ordering relation among parts of paper.

28. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 28 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 19 th century, as Colon Classification scheme  Faceted classification can be descirbed with Description Logc, e.g., OWL-DL

29. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 29 Example: Faceted Classification Wheeled Vehicle Facet 2 wheeled 4 wheeled 3 wheeled is-a Internal Combustion Human Powered Vehicle Propulsion Facet Bicycle Tricycle Motorcycle Auto is-a

30. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 30 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,...

31. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 31 Directed Acylic Graphs  Graph:  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

32. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 32 Example: Directed Acyclic Graph Wheeled Vehicle 2 Wheeled Vehicle 4 Wheeled Vehicle 3 Wheeled Vehicle is-a Internal Combustion Vehicle Human Powered Vehicle Propelled Vehicle Bicycle Tricycle Motorcycle Auto is-a Vehicle

33. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 33 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

34. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 34 Example: Partial Order Graph Wheeled Vehicle 2 Wheeled Vehicle 4 Wheeled Vehicle 3 Wheeled Vehicle is-a Internal Combustion Vehicle Human Powered Vehicle Propelled Vehicle Bicycle Tricycle Motorcycle Auto is-a Vehicle Dashed line = inferred is-a (transitive closure)

35. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 35 Directed Graph  Generalization of DAG (directed acyclic graph)  Cycles are allowed  Arises when many edge types allowed  Example: UMLS

36. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 36 Lattices  A partial order  For every pair of elements A and B  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

37. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 37 Example Lattice: Powerset of 3 element set {a,b,c} {c} {a} {b} {b,c}{a,b} {a,c} Denotes subset Empty Set

38. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 38 Lattices - Applications  Formal Concept Analysis  synthesizing taxonomies  Machine Learning  concept learning

39. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 39 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

40. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 40 Example Bipartite Graph California Massachusetts Oregon CA MA OR States Two-letter state codes

41. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 41 Clique  Clique = complete graph (or subgraph)  all possible edges are present  Used to represent equivalence classes  Typically, on undirected graphs

42. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 42 Example of Clique California CA Calif. CAL Here edges denote synonymy.

43. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 43 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

44. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 44 Example Compound Graph Colin Powell claimed Iraq had WMDs

45. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 45 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

46. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 46 Axiomitized 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)

47. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 47 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?)

48. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 48 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 11179 (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

49. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 49 Major Tasks, Deliverables & Milestones Initial Architecture Design Present Proposed 11179 Part 2 Revisions to SC32 WG2 mtg in DC Research and Development Task/Deliverable Test Implementation (External Users) Prepare Draft Revision of 11179 Part 2 for SC32 mtg in Berlin System Test & Evaluation (Internal Participants) Identify, Select Metadata Sources Identify, Select Technologies Develop Project Plan Gantt Chart Forthcoming

50. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 50 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 Will Seek to Promote Awareness

51. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 51 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

52. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 52 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

53. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 53 Ontology Editor Protege 11179 OWL Ontology XMDR Prototype Architecture: Initial Implemented Modules MetadataValidator AuthenticationService MappingEngine Registry External Interface Generalization Composition (tight ownership) Aggregation (loose ownership) Jena, Xerces Java RetrievalIndex FullTextIndex Lucene LogicBasedIndex Jena, OWI KS Racer RegistryStore WritableRegistryStore Subversion

54. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 54 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)

55. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 55 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 639-3 Part 3 IETF Language Identifiers RFC 1766 Units Ontology

56. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 56 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

57. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 57 Acknowledgements and References  Frank Olken, LBNL  Kevin Keck, LBNL  John McCarthy, LBNL

58. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 58

59. Open Forum 2003 on Metadata RegistriesOpen Forum 2005 on Metadata Registries 59 11179 Metadata Registries Extensions Register (and manage) any semantics that are useful for managing data. E.g.  Add semantic information beyond definitions  link any concept found in concept systems or in the “wild” (or clusters of concepts and relations) to data  This may include all of the permissible values (concepts) and the full concept systems in which the permissible values are found.  E.g., may want to register keywords, thesauri, taxonomies, ontologies, axiomitized ontologies….  Lay Foundation for semantics based computing: Semantics Service Oriented Architecture, Semantic Grids, Semantics based workflows, Semantic Web …