eXtended Metadata Registry (XMDR) Interagency/International Cooperation on Ecoinformatics

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: +1 510-495-2905 bebargmeyer@lbl.gov

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 ManyPlayers, Many Interests…Shared Context

3. 11179 Metadata RegistriesExtensions • 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. Users CONCEPT Metadata Registry TerminologyThesaurus Themes Refers To Symbolizes Ontology GEMET “Rose”, “ClipArt” Stands For Referent Data Standards Structured Metadata XMDR Draws Together Terminology Metadata Registries 11179 Metadata Registry

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

6. Metadata Registries 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 ISO 3166 English Name ISO 3166 3-Alpha Code ISO 3166 3-Numeric Code

7. What is Metadata/Terminology? Data Metadata 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? Data Metadata 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. Data Metadata Fuji Variety name Product look-up (PLU) code 4129 Product of Canada Country of origin What is Metadata/Terminology?

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

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

13. CONCEPT Refers To Symbolizes “Rose”, “ClipArt” Stands For Referent What is Terminology? 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. Name: trout species Definition: The names of species of trout. Values: brook trout Salvelinus fontinalisbrown trout Salmo truttacutthroat trout Oncorhynchus clarkii Concept Terms Context Brown trout Salmo trutta truite common name scientific name French name UIN=6349 Concepts into Data DataElements

17. Systems:STORETEnvirofacts . . . W3C RDF Vocabularies XML SchemasEDI Messages DataInterchange Ontology DBMSQuery Concept Terms Context Brown trout Salmo trutta truite common name scientific name French name UIN=6349 DataElements

18. Continuing ChallengeSynonyms, 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 TowardSemantics 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 11179 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. Example of a graph infectious disease is-a is-a measles influenza

30. 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

31. Graph Taxonomy Graph 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

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

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

40. 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

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

42. 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?)

43. 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

44. 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 Architecture: Initial Implemented Modules

45. 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)

46. 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

47. 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

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

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