1. 1 eXtended Metadata Registry (XMDR) International Ecoinformatics Technical Collaboration Berkeley, California October 24, 2006 Bruce Bargmeyer, Lawrence Berkley National Laboratory University of California Tel: +1 510-495-2905 bebargmeyer@lbl.gov

2. 2 Topics F Challenges to address F A brief tutorial on Semantics and semantic computing F where XMDR fits u Semantic computing technologies u Traditional Data Administration F XMDR project F Test Bed demonstrations

3. 3 The Internet Revolution A world wide web of diverse content: The information glut is nothing new. The access to it is astonishing.

4. 4 Challenge: Find and process non- explicit data Analgesic Agent Non-Narcotic Analgesic AcetominophenNonsteroidal Antiinflammatory Drug Analgesic and Antipyretic Datril Anacin-3Tylenol For example… Patient data on drugs contains brand names (e.g. Tylenol, Anacin-3, Datril,…); However, want to study patients taking analgesic agents

5. 5 Challenge: Specify and compute across Relations, e.g., within a food web in an Arctic ecosystem An organism is connected to another organism for which it is a source of food energy and material by an arrow representing the direction of biomass transfer. Source: http://en.wikipedia.org/wiki/Food_web#Food_web (from SPIRE)http://en.wikipedia.org/wiki/Food_web#Food_web

6. 6 Challenge: Combine Data, Metadata & Concept Systems IDDateTempHg A06-09-134.44 B06-09-139.32 X06-09-136.778 NameDatatypeDefinitionUnits IDtext Monitoring Station Identifier not applicable DatedateDateyy-mm-dd Tempnumber Temperature (to 0.1 degree C) degrees Celcius Hgnumber Mercury contamination micrograms per liter Inference Search Query: “find water bodies downstream from Fletcher Creek where chemical contamination was over 10 micrograms per liter between December 2001 and March 2003” Data: Metadata: BiologicalRadioactive Contamination leadcadmium mercury Chemical Concept system:

7. 7 Challenge: Use data from systems that record the same facts with different terms F Reduce the human toil of drawing information together and performing analysis -> shift to computer processing.

8. 8 Challenge: Use data from systems that record the same facts with different terms Common Content OASIS/ebXML Registries Common Content ISO 11179 Registries Common Content Ontological Registries Common Content CASE Tool Repositories Common Content UDDI Registries Country Identifier Data Element XML Tag Term Hierarchy Attribute Business Specification Table Column Software Component Registries Common Content Database Catalogs Business Object Dublin Core Registries Common Content Coverage

9. 9 Data Elements DZ BE CN DK EG FR... ZW ISO 3166 English Name ISO 3166 3-Numeric Code 012 056 156 208 818 250... 716 ISO 3166 2-Alpha Code Algeria Belgium China Denmark Egypt France... Zimbabwe Name: Context: Definition: Unique ID: 4572 Value Domain: Maintenance Org. Steward: Classification: Registration Authority: Others ISO 3166 French Name L`Algérie Belgique Chine Danemark Egypte La France... Zimbabwe DZA BEL CHN DNK EGY FRA... ZWE ISO 3166 3-Alpha Code Same Fact, Different Terms Algeria Belgium China Denmark Egypt France... Zimbabwe Name: Country Identifiers Context: Definition: Unique ID: 5769 Conceptual Domain: Maintenance Org.: Steward: Classification: Registration Authority: Others Data Element Concept

10. 10 Challenge: Draw information together from a broad range of studies, databases, reports, etc.

11. 11 Challenge: Gain Common Understanding of meaning between Data Creators and Data Users Users Information systems Data Creation Users EEA USGS DoD EPA environ agriculture climate human health industry tourism soil water air 123 345 445 670 248 591 308 123 345 445 670 248 591 308 3268 0825 1348 5038 2708 0000 2178 3268 0825 1348 5038 2708 0000 2178 textdata environ agriculture climate human health industry tourism soil water air 123 345 445 670 248 591 308 123 345 445 670 248 591 308 3268 0825 1348 5038 2708 0000 2178 3268 0825 1348 5038 2708 0000 2178 text ambiente agricultura tiempo salud hunano industria turismo tierra agua aero 123 345 445 670 248 591 308 123 345 445 670 248 591 308 3268 0825 1348 5038 2708 0000 2178 3268 0825 1348 5038 2708 0000 2178 textdata environ agriculture climate human health industry tourism soil water air 123 345 445 670 248 591 308 123 345 445 670 248 591 308 3268 0825 1348 5038 2708 0000 2178 3268 0825 1348 5038 2708 0000 2178 textdata Others... ambiente agricultura tiempo salud huno industria turismo tierra agua aero 123 345 445 670 248 591 308 123 345 445 670 248 591 308 3268 0825 1348 5038 3268 0825 1348 5038 2708 0000 2178 textdata A common interpretation of what the data represents

12. 12 Semantic Computing and XMDR F We are laying the foundation to make a quantum leap toward a substantially new way of computing: Semantic Computing F How can we make use of semantic computing for the environment and health? F What do environmental agencies need to do to prepare for and stimulate semantic computing? F What are the ecoinformatics challenges?

13. 13 Coming: A Semantic Revolution Searching and ranking Pattern analysis Knowledge discovery Question answering Reasoning Semi-automated decision making

14. 14 The Nub of It F Processing that takes “meaning” into account F Processing based on the relations between things not just computing about the things themselves. F Processing that takes people out of the processing, reducing the human toil u Data access, extraction, mapping, translation, formatting, validation, inferencing, … F Delivering higher-level results that are more helpful for the user’s thought and action

15. 15 XMDR & ISO/IEC 11179 F Managing, harmonizing, and vetting semantics is essential to enable semantic computing F Managing, harmonizing and vetting semantics is important for traditional data management. u In the past we just covered the basics u We want to maintain compatibility with previous MDR purposes (data administration, data provenance, data design, …) F Ecoinformatics Test Bed demonstrations of XMDR should show more than incremental improvements of current applications for metadata registries

16. 16 A Brief Tutorial on Semantics F What is meaning? F What are concepts? F What are relations? F What are concept systems? F What is “reasoning”?

17. 17 C.K Ogden and I. A. Richards. The Meaning of Meaning. Thought or Reference (Concept) Referent Symbol SymbolisesRefers to Stands for “Rose”, “ClipArt” Meaning: The Semiotic Triangle

18. 18 Semiotic Triangle: Concepts, Definitions and Signs CONCEPT Referent Refers To Symbolizes Stands For “Rose”, “ClipArt” Definition Sign

19. 19 Semiotic Triangle: Concepts, Definitions, Signs, & Designations Definition CONCEPT Referent Refers To Symbolizes Stands For “Rose”, “ClipArt” Sign Designation

20. 20 Forms of Definitions CONCEPT Referent Refers To Symbolizes Stands For “Rose”, “ClipArt” Definition - Define by: --Essence & Differentia --Relations --Axioms Sign

21. 21 Definition of Concept - Rose: Dictionary - Essence & Differentia F 1.any of the wild or cultivated, usually prickly-stemmed, pinnate-leaved, showy- flowered shrubs of the genus Rosa. Cf. rose family. F 2.any of various related or similar plants. F 3.the flower of any such shrub, of a red, pink, white, or yellow color. --Random House Webster’s Unabridged Dictionary (2003)

22. 22 Definitions in the EPA Environmental Data Registry http://www.epa/gov/edr/sw/AdministeredItem#MailingAddress The exact address where a mail piece is intended to be delivered, including urban-style address, rural route, and PO Box http://www.epa/gov/edr/sw/AdministeredItem#StateUSPSCode The U.S. Postal Service (USPS) abbreviation that represents a state or state equivalent for the U.S. or Canada http://www.epa/gov/edr/sw/AdministeredItem#StateName The name of the state where mail is delivered Mailing Address: State USPS Code: Mailing Address State Name:

23. 23 Definition of Concept - Rose: Relations to Other Concepts CONCEPT Referent Refers To Symbolizes Stands For “Rose”, “ClipArt” Love Romance Marriage

24. 24 SNOMED – Terms Defined by Relations

25. 25 Definition of Concept - Rose: Defined by Axioms in OWL CONCEPT Referent Refers To Symbolizes Stands For “Rose”, “ClipArt” rdfs:subClassOf owl:equivalentClass owl:disjointWith

26. 26 Class Axiom (Definitions) Class Description is Building Block of Class Axiom F A class description is the term used in this document (and in the OWL Semantics and Abstract Syntax) for the basic building blocks of class axioms (informally called class definitions in the Overview and Guide documents). A class description describes an OWL class, either by a class name or by specifying the class extension of an unnamed anonymous class. F OWL distinguishes six types of class descriptions: F a class identifier (a URI reference) F an exhaustive enumeration of individuals that together form the instances of a classenumeration F a property restrictionproperty restriction F the intersection of two or more class descriptionsintersection F the union of two or more class descriptionsunion F the complement of a class descriptioncomplement F The first type is special in the sense that it describes a class through a class name (syntactically represented as a URI reference). The other five types of class descriptions describe an anonymous class by placing constraints on the class extension. F Class descriptions of type 2-6 describe, respectively, a class that contains exactly the enumerated individuals (2nd type), a class of all individuals which satisfy a particular property restriction (3rd type), or a class that satisfies boolean combinations of class descriptions (4th, 5th and 6th type). Intersection, union and complement can be respectively seen as the logical AND, OR and NOT operators. The four latter types of class descriptions lead to nested class descriptions and can thus in theory lead to arbitrarily complex class descriptions. In practice, the level of nesting is usually limited.

27. 27 Class Descriptions -> Class Axiom F Class descriptions form the building blocks for defining classes through class axioms. The simplest form of a class axiom is a class description of type 1, It just states the existence of a class, using owl:Class with a class identifier. F For example, the following class axiom declares the URI reference #Human to be the name of an OWL class: u This is correct OWL, but does not tell us very much about the class Human. Class axioms typically contain additional components that state necessary and/or sufficient characteristics of a class. OWL contains three language constructs for combining class descriptions into class axioms: F rdfs:subClassOf allows one to say that the class extension of a class description is a subset of the class extension of another class description. rdfs:subClassOf F owl:equivalentClass allows one to say that a class description has exactly the same class extension as another class description. owl:equivalentClass F owl:disjointWith allows one to say that the class extension of a class description has no members in common with the class extension of another class description. owl:disjointWith

28. 28 Computable Meaning CONCEPT Referent Refers To Symbolizes Stands For “Rose”, “ClipArt” rdfs:subClassOf owl:equivalentClass owl:disjointWith If “rose” is owl:disjointWith “daffodil”, then a computer can determine that an assertion is invalid, if it states that a rose is also a daffodil (e.g., in a knowledgebase).

29. 29 Fletcher Creek Merced Lake WaterBody What are Relations? Relation Merced Lake Fletcher Creek Merced River isA Concepts and relations can be represented as nodes and edges in formal graph structures, e.g., “is-a” hierarchies.

30. 30 A 2 bacd 1 Nodes represent concepts Lines (arcs) represent relations Concept Systems have Nodes and may have Relations Concept systems can be represented & queried as graphs

31. 31 A More Complex Concept Graph From Supervaluation Semantics for an Inland Water Feature Ontology Paulo Santos and Brandon Bennett http://ijcai.org/papers/1187.pdf#search=%22terminology%20water%20ontology%22http://ijcai.org/papers/1187.pdf#search=%22terminology%20water%20ontology%22 Concept lattice of inland water features LinearLarge Non-linear Large linearSmall linearSmall non- linear DeepNatural Artificial RiverStreamCanalReservoirLakeMarshPond FlowingShallowStagnant

32. 32 Types of Concept System Graph Structures F Trees F Partially Ordered Trees F Ordered Trees F Faceted Classifications F Directed Acyclic Graphs F Partially Ordered Graphs F Lattices F Bipartite Graphs F Directed Graphs F Cliques F Compound Graphs

33. 33 Tree Partial Order Tree Ordered Tree Faceted Classification Directed Acyclic Graph Partial Order Graph Powerset of 3 element set Bipartite Graph Clique Compound Graph Types of Concept System Graph Structures

34. 34 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.

35. 35 What Kind of Relations are There? Lots! Relationship class: A particular type of connection existing between people related to or having dealings with each other. F acquaintanceOf - A person having more than slight or superficial knowledge of this person but short of friendship. F ambivalentOf - A person towards whom this person has mixed feelings or emotions. F ancestorOf - A person who is a descendant of this person. F antagonistOf - A person who opposes and contends against this person. F apprenticeTo - A person to whom this person serves as a trusted counselor or teacher. F childOf - A person who was given birth to or nurtured and raised by this person. F closeFriendOf - A person who shares a close mutual friendship with this person. F collaboratesWith - A person who works towards a common goal with this person. F …

36. 36 Example of relations in a food web in an Arctic ecosystem An organism is connected to another organism for which it is a source of food energy and material by an arrow representing the direction of biomass transfer. Source: http://en.wikipedia.org/wiki/Food_web#Food_web (from SPIRE)http://en.wikipedia.org/wiki/Food_web#Food_web

37. 37 Ontologies are a type of Concept System F Ontology: explicit formal specifications of the terms in the domain and relations among them (Gruber 1993) F An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. F Why would someone want to develop an ontology? Some of the reasons are: u To share common understanding of the structure of information among people or software agents u To enable reuse of domain knowledge u To make domain assumptions explicit u To separate domain knowledge from the operational knowledge u To analyze domain knowledge http://www.ksl.stanford.edu/people/dlm/papers/ontology101/ontology101-noy-mcguinness.html

38. 38 What is Reasoning? Inference PolioSmallpox Infectious Disease Disease is-a Diabetes Heart disease Chronic Disease is-a Signifies inferred is-a relationship

39. 39 Reasoning: Taxonomies & partonomies can be used to support inference queries Oakland Berkeley Alameda County California part-of Santa Clara San Jose Santa Clara County part-of E.g., if a database contains information on events by city, we could query that database for events that happened in a particular county or state, even though the event data does not contain explicit state or county codes.

40. 40 Reasoning: Relationship metadata can be used to infer non-explicit data Analgesic Agent Non-Narcotic Analgesic AcetominophenNonsteroidal Antiinflammatory Drug Analgesic and Antipyretic Datril Anacin-3Tylenol For example… (1)patient data on drugs currently being taken contains brand names (e.g. Tylenol, Anacin-3, Datril,…); (2) concept system connects different drug types and names with one another (via is-a, part-of, etc. relationships); (3) so… patient data can be linked and searched by inferred terms like “acetominophen” and “analgesic” as well as trade names explicitly stored as text strings in the database

41. 41 Reasoning: Least Common Ancestor Query Analgesic and Antipyretic Analgesic Agent Non-Narcotic Analgesic Acetominophen Opioid Opiate Morphine Sulfate Codeine Phosphate Nonsteroidal Antiinflammatory Drug What is the least common ancestor concept in the NCI Thesaurus for Acetominophen and Morphine Sulfate ? (answer = Analgesic Agent)

42. 42 Reasoning: Example “sibling” queries: concepts that share a common ancestor F Environmental: u "siblings" of Wetland (in NASA SWEET ontology) F Health u Siblings of ERK1 finds all 700+ other kinase enzymes u Siblings of Novastatin finds all other statins F 11179 Metadata u Sibling values in an enumerated value domain

43. 43 F Health u Find all the siblings of Breast Neoplasm F Environmental u Find all chemicals that are a u carcinogen (cause cancer) and u toxin (are poisonous) and u terratogenic (cause birth defects) Reasoning: More complex “sibling” queries: concepts with multiple ancestors site neoplasmsbreast disorders Breast neoplasm Respiratory System neoplasm Non-Neoplastic Breast Disorder Eye neoplasm

44. 44 End of Tutorial about concept systems Where does ISO/IEC 11179 fit?

45. 45 Data Generation and Use Cost vs. Coordination Autonomous Reporting Community of Interest Full Control $ Coordination Data Creation

46. 46 Data Generation and Use Cost vs. Coordination Autonomous Reporting Community of Interest Full Control $ Coordination Data Creation Data Use

47. 47 ISO/IEC 11179 Metadata Registries Reduce Cost of Data Creation and Use Autonomous Reporting Community of Interest Full Control $ Coordination Data Creation Data Use

48. 48 Metadata Registries Increase the Benefit from Data (Strategic Effectiveness) Autonomous Reporting Community of Interest Full Control Benefit MDR

49. 49 What Can ISO/IEC 11179 MDR Do? Traditional Data Management (11179 Edition 2) F Register metadata which describes data—in databases, applications, XML Schemas, data models, flat files, paper F Assist in harmonizing, standardizing, and vetting metadata F Assist data engineering F Provide a source of well formed data designs for system designers F Record reporting requirements F Assist data generation, by describing the meaning of data entry fields and the potential valid values F Register provenance information that can be provided to end users of data F Assist with information discovery by pointing to systems where particular data is maintained.

50. 50 Data Elements DZ BE CN DK EG FR... ZW ISO 3166 English Name ISO 3166 3-Numeric Code 012 056 156 208 818 250... 716 ISO 3166 2-Alpha Code Algeria Belgium China Denmark Egypt France... Zimbabwe Name: Context: Definition: Unique ID: 4572 Value Domain: Maintenance Org. Steward: Classification: Registration Authority: Others ISO 3166 French Name L`Algérie Belgique Chine Danemark Egypte La France... Zimbabwe DZA BEL CHN DNK EGY FRA... ZWE ISO 3166 3-Alpha Code Traditional MDR: Manage Code Sets Algeria Belgium China Denmark Egypt France... Zimbabwe Name: Country Identifiers Context: Definition: Unique ID: 5769 Conceptual Domain: Maintenance Org.: Steward: Classification: Registration Authority: Others Data Element Concept

51. 51 What Can XMDR Do? Support a new generation of semantic computing F Concept system management F Harmonizing and vetting concept systems F Linkage of concept systems to data F Interrelation of multiple concept systems F Grounding ontologies and RDF in agreed upon semantics F Reasoning across XMDR content F Provision of Semantic Services

52. 52 Coming: A Semantic Revolution Autonomous Reporting Community of Interest Full Control Searching and ranking Pattern analysis Knowledge discovery Question answering Reasoning Semi-automated decision making

53. 53 We are trying to manage semantics in an increasingly complex content space Structured data Semi-structured data Unstructured data Text Pictographic Graphics Multimedia Voice video

54. 54 11179-3 (E3) Increases MDR Benefit Autonomous Reporting Community of Interest Full Control Benefit MDR When communities create information according to a common vocabulary the value of the resulting information increases dramatically.

55. 55 Example F Combining Concept Systems, Data, and Metadata to answer queries.

56. 56 Linking Concepts: Text Document § 141.62 Maximum contaminant levels for inorganic contaminants. (a) [Reserved] (b) The maximum contaminant levels for inorganic contaminants specified in paragraphs (b) (2)–(6), (b)(10), and (b) (11)–(16) of this section apply to community water systems and non-transient, non-community water systems. The maximum contaminant level specified in paragraph (b)(1) of this section only applies to community water systems. The maximum contaminant levels specified in (b)(7), (b)(8), and (b)(9) of this section apply to community water systems; non-transient, noncommunity water systems; and transient non-community water systems. Contaminant MCL (mg/l) (1) Fluoride............................ 4.0 (2) Asbestos.......................... 7 Million Fibers/liter (longer than 10 μm). (3) Barium.............................. 2 (4) Cadmium.......................... 0.005 (5) Chromium......................... 0.1 (6) Mercury............................ 0.002 (7) Nitrate............................... 10 (as Nitrogen) § 141.62 40 CFR Ch. I (7–1–02 Edition) Title 40--Protection of Environment CHAPTER I--ENVIRONMENTAL PROTECTION AGENCY PART 141--NATIONAL PRIMARY DRINKING WATER REGULATIONS

57. 57 Thesaurus Concept System (From GEMET) Chemical Contamination Definition The addition or presence of chemicals to, or in, another substance to such a degree as to render it unfit for its intended purpose. Broader Term contamination Narrower Terms cadmium contamination, lead contamination, mercury contamination Related Terms chemical pollutant, chemical pollution Deutsch: Chemische Verunreinigung English (US): chemical contamination Español: contaminación química SOURCE General Multi-Lingual Environmental Thesaurus (GEMET)

58. 58 Concept System (Thesaurus) Chemical cadmiumleadmercury BiologicalRadioactive chemical pollutant chemical pollution Contamination

59. 59 NameAcalypha ostryifolia MercuryMercury, bis(acetato-.kappa.O) (benzenamine)- Mercury, (acetato-.kappa.O) phenyl-, mixt. with phenylmercuric propionate TypeBiological Organism Chemical CAS Number 7439-97-663549-47-3No CAS Number TSN28189 ICTV EPA IDE17113275E965269 Recent AdditionsRecent Additions | Contact UsContact Us Environmental Data Registry Chemicals in EPA Environmental Data Registry

60. 60 Data Monitoring Stations NameLatitudeLongitudeLocation A41.45 N125.99 WMerced Lake B43.23 N120.50 W Merced River X39.45 N118.12 W Fletcher Creek IDDateTemp Hg A2006-09-134.44 B2006-09-139.32 X2006-09-155.23 X2006-09-136.778 Measurements A B X Merced Lake Fletcher Creek Merced River

61. 61 Metadata SystemData ElementDefinitionUnitsPrecision MeasurementsIDMonitoring Station Identifiernot applicable MeasurementsDateDate sample was collectednot applicable MeasurementsTempTemperaturedegrees Celcius0.1 MeasurementsHgMercury contaminationmicrograms per liter0.004 Monitoring StationsNameMonitoring Station Identifier Monitoring StationsLatitudeLatitude where sample was taken Monitoring StationsLongitude Longitude where sample was taken Monitoring StationsLocationBody of water monitored ContaminantsContaminantName of contaminant ContaminantsThresholdAcceptable threshold value Metadata Contaminants ContaminantThreshold mercury5 lead42? cadmium250?

62. 62 Relations among Inland Bodies of Water Fletcher Creek Merced Lake Merced River feeds into Fletcher CreekMerced Lake Merced River fed from feeds into

63. 63 Combining Data, Metadata & Concept Systems IDDateTempHg A06-09-134.44 B06-09-139.32 X06-09-136.778 NameDatatypeDefinitionUnits IDtext Monitoring Station Identifier not applicable DatedateDateyy-mm-dd Tempnumber Temperature (to 0.1 degree C) degrees Celcius Hgnumber Mercury contamination micrograms per liter Inference Search Query: “find water bodies downstream from Fletcher Creek where chemical contamination was over 2 parts per billion between December 2001 and March 2003” Data Metadata BiologicalRadioactive Contamination leadcadmium mercury Chemical Concept system

64. 64 Example – Environmental Text Corpus F Idea: Develop an environmental research corpus that could attract R&D efforts. Include the reports and other material from over $1b EPA sponsored research. u Prepare the corpus and make it available n Research results from years of ORD R&D u Publish associated metadata and concept systems in XMDR u Use open source software for EPA testing

65. 65 Extraction Engines F Find concepts and relations between concepts in text, tables, data, audio, video, … F Produce databases (relational tables, graph structures), and other output F Functions: u Segment – find text snippets (boundaries important) u Classify – determines database field for text segment u Association – which text segments belong together u Normalization – put information into standard form u Deduplication – collapse redundant information

66. 66 Metadata Registries are Useful Registered semantics F For “training” extraction engines F The“Normalize” function can make use of standard code sets that have mapping between representation forms. F The “Classify” function can interact with pre-established concept systems. Provenance F High precision for proper nouns, less precision (e.g., 70%) for other concepts -> impacts downstream processing, Need to track precision

67. 67 Data Elements DZ BE CN DK EG FR... ZW ISO 3166 English Name ISO 3166 3-Numeric Code 012 056 156 208 818 250... 716 ISO 3166 2-Alpha Code Algeria Belgium China Denmark Egypt France... Zimbabwe Name: Context: Definition: Unique ID: 4572 Value Domain: Maintenance Org. Steward: Classification: Registration Authority: Others ISO 3166 French Name L`Algérie Belgique Chine Danemark Egypte La France... Zimbabwe DZA BEL CHN DNK EGY FRA... ZWE ISO 3166 3-Alpha Code Normalize – Need Registered and Mapped Concepts/Code Sets Algeria Belgium China Denmark Egypt France... Zimbabwe Name: Country Identifiers Context: Definition: Unique ID: 5769 Conceptual Domain: Maintenance Org.: Steward: Classification: Registration Authority: Others Data Element Concept

68. 68 Information Extraction & Semantic Computing Segment Classify Associate Normalize Deduplicate Discover patterns Select models Fit parameters Inference Report results Actionable Information Decision Support Extraction Engine 11179-3 (E3) XMDR

69. 69 Example – 11179-3 (E3) Support Semantic Web Applications The address state code is “AB”. This can be expressed as a directed Graph e.g., an RDF statement: Address AB State Code Node Edge Subject Predicate Object XMDR may be used to “ground” the Semantics of an RDF Statement. Graph RDF

70. 70 Example: Grounding RDF nodes and relations: URIs Reference a Metadata Registry dbA:ma344 “AB”^^ai:StateCode ai: StateUSPSCode @prefix dbA: “http:/www.epa.gov/databaseA” @prefix ai: “http://www.epa.gov/edr/sw/AdministeredItem#” dbA:e0139 ai: MailingAddress

71. 71 Definitions in the EPA Environmental Data Registry http://www.epa/gov/edr/sw/AdministeredItem#MailingAddress The exact address where a mail piece is intended to be delivered, including urban-style address, rural route, and PO Box http://www.epa/gov/edr/sw/AdministeredItem#StateUSPSCode The U.S. Postal Service (USPS) abbreviation that represents a state or state equivalent for the U.S. or Canada http://www.epa/gov/edr/sw/AdministeredItem#StateName The name of the state where mail is delivered Mailing Address: State USPS Code: Mailing Address State Name:

72. 72 Use data from systems that record the same facts with different terms F Avoid a combinatorial explosion of data content, description, and metadata arrangements for information access, exchange, and presentation..

73. 73 Ontologies for Data Mapping Concept Geographic Area Geographic Sub-Area Country Country Identifier Country NameCountry Code Short Name ISO 3166 2-Character Code ISO 3166 3- Character Code Long Name Distributor Country Name Mailing Address Country Name ISO 3166 3-Numeric Code FIPS Code Ontologies can help to capture and express semantics

74. 74 Example: Content Mapping Service F Collect data from many sources – files contain data that has the same facts represented by different terms. E.g., one system responds with Danemark, DK, another with DNK, another with 208; map all to Denmark. F XMDR could accept XML files with the data from different code sets and return a result mapped to a single code set.

75. 75 Ecoinformatics: Concept System Store Metadata Registry Concept System Thesaurus Themes Data Standards Ontology GEMET Structured Metadata Users Concept systems: Keywords Controlled Vocabularies Thesauri Taxonomies Ontologies Axiomatized Ontologies (Essentially graphs: node-relation-node + axioms) }

76. 76 Ecoinformatics: Management of Concept Systems Metadata Registry Concept System Thesaurus Themes Data Standards Ontology GEMET Structured Metadata Users Concept system: Registration Harmonization Standardization Acceptance (vetting) Mapping (correspondences) }

77. 77 Ecoinformatics: Life Cycle Management Metadata Registry Concept System Thesaurus Themes Data Standards Ontology GEMET Structured Metadata Users Life cycle management: Data and Concept systems (ontologies) }

78. 78 Ecoinformatics: Grounding Semantics Metadata Registry Concept System Thesaurus Themes Data Standards Ontology GEMET Structured Metadata Users Registries Semantic Web RDF Triples Subject (node URI) Verb (relation URI) Object (node URI) Ontologies

79. 79 XMDR Project Collaboration F Collaborative, interagency effort u EPA, USGS, NCI, Mayo Clinic, DOD, LBNL …& others F Draws on and contributes to interagency/International Cooperation on Ecoinformatics F Involves Ecoterm, international, national, state, local government agencies, other organizations as content providers and potential users F Interacts with many organizations around the world through ISO/IEC standards committees F Only loosely aligned with Ecoinformatics Cooperation

80. 80 XMDR Project F High risk R&D, sponsor expected likelihood of failure F Targeted toward leading-edge semantics applications in a highly strategic environment F Conceptualization of new capabilities, creation of designs (expressed as standards), development of a software architecture and prototype system for demonstrating capabilities and testing designs u Reasoning, inference, linkage of concepts to data, …. F Demonstration of fundamental semantic management capabilities for metadata registries, understanding the potential applications that could be built in-house

81. 81 Results to Date F Completed the first version of designs for next generation metadata registries—expressed as figures in a UML model that is proposed for next edition of the ISO/IEC 11179 standard F Developed XMDR Prototype -- available as open source software F Content loaded in prototype: broad range of traditional metadata and concept systems F Designs and prototype being explored and used in several locations. Potential for facilitating development and sharing of content by wide diversity of users. F Starting the next version of designs, taking on more challenging content and capabilities

82. 82 Status of Project F NSF has funded a three-year project, providing a funding base u Strong emphasis on the computer science R&D results and collaboration with EU and Asia u Limited staffing F Proposing further high risk R&D F Developing proposals for collaborative efforts to demonstrate capabilities, especially in the area of water. F Opportunity to collaborate with JRC and projects under the European Commission 7 th Framework Program

83. 83 Ecoinformatics Test Bed F Proposed in Brussels in September 2004 u Project direction and statement developed F Purpose u Research and technical informatics to investigate metadata management techniques. Practical experiment for testing usability. F Initial Focus u Use metadata and semantic technologies for air quality (transportation) health effects u Potential for extension to other areas u Need for engaging ongoing operations and/or indicators Bruce the unready

84. 84 Ecoinformatics Test Bed F Extend original charter to Water F Use Water as example content u Metadata, concept systems F Look for opportunities to coordinate with EU projects u WISE, EC 7 th Framework program F Identify and propose possible demonstrations