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  1. eXtended Metadata Registries (XMDR) NKOS Workshop June 11, 2005 Bruce Bargmeyer bebargmeyer@lbl.gov Chair: ISO/IEC JTC1/SC32-Data Mgmt & Interchange PI: XMDR Project Lawrence Berkley National Laboratory University of California WWW.XMDR.ORG

  2. Users CONCEPT Metadata Registry Refers To Symbolizes TerminologyThesaurus Taxonomy “Rose”, “ClipArt” Ontology Stands For Referent Data Standards Structured Metadata 11179 Metadata Registry XMDR Project Draws Together ISO/IEC 11179 Metadata Registries Terminology ISO TC 37 & … ISO/IEC JTC 1/SC 32

  3. Users CONCEPT Metadata Registry Refers To Symbolizes TerminologyThesaurus Taxonomy “Rose”, “ClipArt” Ontology Stands For Referent Data Standards Structured Metadata 11179 Metadata Registry Align, Coordinate, Integrate Standards/Recommendations ISO/IEC 11179 Metadata Registries Terminology Semantic Web W3C ISO TC 37 & … ISO/IEC JTC 1/SC 32

  4. XMDR Metadata RegistriesExtensions • Register (and manage) any semantics that are useful in managing data. • Enable users to find and register correspondences between the content (concepts & relationships) of multiple KOSs • Enable registration of links between concepts and data in databases • Provide Semantic Services -- lay foundation for semantics based computing: Semantics Service Oriented Architecture, Semantic Grids, Semantics based workflows, Semantic Web … • Prepare standards proposals, especially for ISO/IEC 11179 Parts 2 & 3 • Develop a reference implementation

  5. Name: Country Identifiers Context: Definition: Unique ID: 5769 Conceptual Domain: Maintenance Org.: Steward: Classification: Registration Authority: Others DataElementConcept Algeria Belgium China Denmark Egypt France . . . Zimbabwe Current 11179 Metadata Registry ContentE.g., Country Identifier Data Elements Algeria Belgium China Denmark Egypt France . . . Zimbabwe L`Algérie Belgique Chine Danemark Egypte La France . . . Zimbabwe DZ BE CN DK EG FR . . . ZW DZA BEL CHN DNK EGY FRA . . . ZWE 012 056 156 208 818 250 . . . 716 Name: Context: Definition: Unique ID: 4572 Value Domain: Maintenance Org. Steward: Classification: Registration Authority: Others ISO 3166 3-Alpha Code ISO 3166 English Name ISO 3166 French Name ISO 3166 2-Alpha Code ISO 3166 3-Numeric Code

  6. Data Element List – Address Group 33c Name Street Address City, State Postal Code Country Use ISO/IEC 11179 MDR to CreateXML Schemas, DBMS Schemas <?xml version="1.0"?> <shipTo > <name>Alice Wilson</name> <street>161 North Street</street> <city>Happy Valley</city> <state>MO</state> <zip>63105</zip> <country code>USA</country code> </shipTo>

  7. Concept Concept Concept Concept Geographic Area Geographic Sub-Area Country Country Identifier Country Name Country Code ISO 3166 2-Character Code ISO 3166 3- Character Code Short Name Long Name Mailing Address Country Name ISO 3166 3-Numeric Code FIPS Code Distributor Country Name XMDR: Register Ontologies

  8. XMDR: Register AnyKnowledge Organization System (KOS) • Keywords • Glossaries • Gazetteers • Thesauri • Taxonomies • Concept system (ISO TC 37) • Ontologies • Axiomititized Ontologies

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

  10. 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.

  11. 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.

  12. 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

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

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

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

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

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

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

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

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

  21. Example Compound Graph Colin Powell claimed had WMDs Iraq

  22. 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?)

  23. ISO/IEC 11179 Metadata Registries+ XMDR • Register and manage semantics that are or can be harmonized and vetted by some Community of Interest (COI) • Provide Semantic Services • E.g., the semantics can be referenced by RDF statements (subjects, predicates, objects) • The semantics can be used to bootstrap the Semantic Web and Semantic Computing • A “vocabulary” that is grounded for some COI • Enable registration using formal logic as well as natural language

  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. WordNet is usually considered a terminological ontology. 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. XMDR: Vocabularies – Vetted with a Community of Interest to Boostrap the Semantic Web and Semantic Computing dbA:e0139 ai: MailingAddress dbA:ma344 ai: StateUSPSCode “AB”^^ai:StateCode @prefix dbA: “http:/www.epa.gov/databaseA” @prefix ai: “http://www.epa/gov/edr/sw/AdministeredItem#”

  27. ISO/IEC 11179 Part 3 Metamodel (Metadata Registry Schema) Expressed in: • UML model XMDR: Also express in: • OWL Ontology • Common Logic

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

  29. 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)

  30. 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

  31. 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

  32. Project Status This year: • Building XMDR Core (content & System) Next year: • Extend XMDR-Core (content & system) • R&D Semantic Services in a Semantic Service Oriented Architecture Expected to be five-year project

  33. XMDR Project Participants • Collaborative, interagency effort • US Environmental Protection Agency • United States Geological Survey • National Cancer Institute • Mayo Clinic • US Department of Defense • Lawrence Berkeley National Laboratory • & others • Interagency/International Cooperation on Ecoinformatics • EPA, European Environment Agency, UNEP • Ecoterm

  34. Job Posting • Vacancy Announcement--Research position to be posted at LBNL—looking for person with education and experience in semantics, terminology, system development WWW.XMDR.ORG

  35. Acknowledgementsand References • Frank Olken, LBNL • Kevin Keck, LBNL • John McCarthy, LBNL