eXtended Metadata Registry (XMDR) International Ecoinformatics Technical Collaboration

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eXtended Metadata Registry (XMDR)

International Ecoinformatics Technical CollaborationBerkeley, California

October 24, 2006

Bruce Bargmeyer, Lawrence Berkley National LaboratoryUniversity of CaliforniaTel: +1 510-495-2905bebargmeyer@lbl.gov

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Topics

Challenges to addressA brief tutorial on Semantics and semantic

computingwhere XMDR fits

Semantic computing technologies Traditional Data Administration

XMDR projectTest Bed demonstrations

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The Internet Revolution

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

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Challenge: Find and process non-explicit data

Analgesic Agent

Non-Narcotic Analgesic

AcetominophenNonsteroidal Antiinflammatory Drug

Analgesic and Antipyretic

DatrilAnacin-3 Tylenol

For example…

Patient data on drugs contains brand names (e.g. Tylenol, Anacin-3, Datril,…);

However, want to study patients taking analgesic agents

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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)

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Challenge: Combine Data, Metadata & Concept Systems

ID Date Temp Hg

A 06-09-13 4.4 4

B 06-09-13 9.3 2

X 06-09-13 6.7 78

Name Datatype Definition Units

ID textMonitoring Station Identifier

not applicable

Date date Date yy-mm-dd

Temp numberTemperature (to 0.1 degree C)

degrees Celcius

Hg numberMercury 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:

Biological Radioactive

Contamination

lead cadmiummercury

Chemical

Concept system:

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Challenge: Use data from systems that record the same facts with different terms

Common Content

OASIS/ebXMLRegistries

Common Content

ISO 11179Registries

Common Content

OntologicalRegistries

Common Content

CASE ToolRepositories

Common Content

UDDIRegistries

CountryIdentifier

DataElement

XML Tag

TermHierarchy

Attribute

BusinessSpecification

TableColumn

SoftwareComponentRegistries

Common Content

Common Content

DatabaseCatalogs

BusinessObject

DublinCore

Registries

Common Content

Coverage

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

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Challenge: Gain Common Understanding of meaning between Data Creators and Data Users

Users Information systems

Data Creation

UsersUsers

EEA

USGS

DoD

EPAenvironagricultureclimatehuman healthindustrytourismsoilwaterair

123345445670248591308

123345445670248591308

3268082513485038270800002178

3268082513485038270800002178

text data

environagricultureclimatehuman healthindustrytourismsoilwaterair

123345445670248591308

123345445670248591308

3268082513485038270800002178

3268082513485038270800002178

text

ambienteagriculturatiemposalud hunanoindustriaturismotierraaguaaero

123345445670248591308

123345445670248591308

3268082513485038270800002178

3268082513485038270800002178

text data

data

environagricultureclimatehuman healthindustrytourismsoilwaterair

123345445670248591308

123345445670248591308

3268082513485038270800002178

3268082513485038270800002178

text data

Others . . .

ambienteagriculturatiemposalud hunoindustriaturismotierraaguaaero

123345445670248591308

123345445670248591308

3268082513485038

3268082513485038270800002178

text data

A common interpretation of what the data represents

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Semantic Computing and XMDR

We are laying the foundation to make a quantum leap toward a substantially new way of computing: Semantic Computing

How can we make use of semantic computing for the environment and health?

What do environmental agencies need to do to prepare for and stimulate semantic computing?

What are the ecoinformatics challenges?

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Coming: A Semantic Revolution

Searching and rankingPattern analysisKnowledge discoveryQuestion answeringReasoningSemi-automated decision making

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The Nub of It

Processing that takes “meaning” into account Processing based on the relations between things

not just computing about the things themselves. Processing that takes people out of the

processing, reducing the human toil Data access, extraction, mapping, translation,

formatting, validation, inferencing, …Delivering higher-level results that are more

helpful for the user’s thought and action

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A Brief Tutorial on Semantics

What is meaning?What are concepts?What are relations?What are concept systems?What is “reasoning”?

17C.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

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Semiotic Triangle:Concepts, Definitions and Signs

CONCEPT

Referent

Refers To Symbolizes

Stands For

“Rose”,“ClipArt”

Definition

Sign

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Forms of Definitions

CONCEPT

Referent

Refers To Symbolizes

Stands For

“Rose”,“ClipArt”

Definition - Define by: --Essence & Differentia --Relations --Axioms

Sign

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Definition of Concept - Rose: Dictionary - Essence & Differentia

1. any of the wild or cultivated, usually prickly-stemmed, pinnate-leaved, showy-flowered shrubs of the genus Rosa. Cf. rose family.

2. any of various related or similar plants. 3. the flower of any such shrub, of a red,

pink, white, or yellow color.--Random House Webster’s Unabridged

Dictionary (2003)

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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:

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Definition of Concept - Rose: Relations to Other Concepts

CONCEPT

Referent

Refers To Symbolizes

Stands For

“Rose”,“ClipArt”

LoveRomanceMarriage

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SNOMED – Terms Defined by Relations

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Definition of Concept - Rose:Defined by Axioms in OWL

CONCEPT

Referent

Refers To Symbolizes

Stands For

“Rose”,“ClipArt”

rdfs:subClassOf owl:equivalentClass owl:disjointWith

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Class Axiom (Definitions)Class Description is Building Block of Class

Axiom 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.

OWL distinguishes six types of class descriptions: a class identifier (a URI reference) an exhaustive enumeration of individuals that together form the instances of a class a property restriction the intersection of two or more class descriptions the union of two or more class descriptions the complement of a class description 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.

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.

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Class Descriptions -> Class Axiom

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.

For example, the following class axiom declares the URI reference #Human to be the name of an OWL class: <owl:Class rdf:ID="Human"/> 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:

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.

owl:equivalentClass allows one to say that a class description has exactly the same class extension as another class description.

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.

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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 anassertion is invalid, if it states that a rose is also a daffodil (e.g., in a knowledgebase).

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Fletcher CreekMerced

Lake

WaterBody

What are Relations?

Relation

Merced Lake

Fletcher CreekMerced River

isA isA

Concepts and relations can be represented as nodes and edges in formal graph structures, e.g., “is-a” hierarchies.

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A

2

b a c d

1

Nodes represent concepts

Lines (arcs) represent relations

Concept Systems have Nodes and may have Relations

Concept systems can be represented & queried as graphs

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A More Complex Concept Graph

From Supervaluation Semantics for an Inland Water Feature OntologyPaulo Santos and Brandon Bennett http://ijcai.org/papers/1187.pdf#search=%22terminology%20water%20ontology%22

Concept lattice of inland water features

Linear LargeNon-linear

Non-linear

Large linear Small linear Small non- linear

Deep Natural

Artificial

River Stream Canal Reservoir Lake Marsh Pond

Flowing Shallow Stagnant

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Types of Concept System Graph Structures

Trees Partially Ordered Trees Ordered Trees Faceted Classifications Directed Acyclic Graphs Partially Ordered Graphs Lattices Bipartite Graphs Directed Graphs Cliques Compound Graphs

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Tree

Partial Order Tree

Ordered Tree

Faceted ClassificationDirected Acyclic GraphPartial Order Graph

Powerset of 3 element setBipartite Graph Clique

Compound Graph

Types of Concept System Graph Structures

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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 graphsare undirected.

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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.

acquaintanceOf - A person having more than slight or superficial knowledge of this person but short of friendship.

ambivalentOf - A person towards whom this person has mixed feelings or emotions.

ancestorOf - A person who is a descendant of this person. antagonistOf - A person who opposes and contends against this person. apprenticeTo - A person to whom this person serves as a trusted counselor or

teacher. childOf - A person who was given birth to or nurtured and raised by this person. closeFriendOf - A person who shares a close mutual friendship with this person. collaboratesWith - A person who works towards a common goal with this person.

…

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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)

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Ontologies are a type of Concept System

Ontology: explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)

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.

Why would someone want to develop an ontology? Some of the reasons are: To share common understanding of the structure of information

among people or software agents To enable reuse of domain knowledge To make domain assumptions explicit To separate domain knowledge from the operational knowledge To analyze domain knowledge

http://www.ksl.stanford.edu/people/dlm/papers/ontology101/ontology101-noy-mcguinness.html

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What is Reasoning?Inference

Polio Smallpox

Infectious Disease

Disease

is-a

is-a is-a

is-a

is-a

Diabetes Heart disease

Chronic Disease

is-a

Signifies inferred is-a relationship

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Reasoning: Taxonomies & partonomies can be used to support inference queries

Oakland Berkeley

Alameda County

California

part-of

part-of part-of

part-of

part-of

Santa Clara San Jose

Santa Clara County

part-of

E.g., if a database containsinformation 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.

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Reasoning: Relationship metadata can be used to infer non-explicit data

Analgesic Agent

Non-Narcotic Analgesic

AcetominophenNonsteroidal Antiinflammatory Drug

Analgesic and Antipyretic

DatrilAnacin-3 Tylenol

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

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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)

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Reasoning: Example “sibling” queries: concepts that share a common ancestor

Environmental:"siblings" of Wetland (in NASA SWEET ontology)

HealthSiblings of ERK1 finds all 700+ other kinase enzymesSiblings of Novastatin finds all other statins

11179 MetadataSibling values in an enumerated value

domain

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HealthFind all the siblings of

Breast Neoplasm

EnvironmentalFind all chemicals that are acarcinogen (cause cancer) andtoxin (are poisonous) andterratogenic (cause birth

defects)

Reasoning: More complex “sibling” queries: concepts with multiple ancestors

site neoplasms breast disorders

Breast neoplasm

RespiratorySystem

neoplasm

Non-Neoplastic

Breast Disorder

Eye neoplasm

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End of Tutorial about concept systems

Where does ISO/IEC 11179 fit?

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Data Generation and UseCost vs. Coordination

Autonomous

Reporting

Community of Interest

Full Control

$

Coordination

DataCreation

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Data Generation and UseCost vs. Coordination

Autonomous

Reporting

Community of Interest

Full Control

$

Coordination

DataCreation

DataUse

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ISO/IEC 11179 Metadata Registries Reduce Cost of Data Creation and Use

Autonomous

Reporting

Community of Interest

Full Control

$

Coordination

DataCreation

DataUse

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Metadata Registries Increase the Benefitfrom Data (Strategic Effectiveness)

Autonomous Reporting

Community of Interest Full Control

Benefit

MDR

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What Can ISO/IEC 11179 MDR Do?

Traditional Data Management (11179 Edition 2) Register metadata which describes data—in databases,

applications, XML Schemas, data models, flat files, paper Assist in harmonizing, standardizing, and vetting metadata Assist data engineering Provide a source of well formed data designs for system designers Record reporting requirements Assist data generation, by describing the meaning of data entry

fields and the potential valid values Register provenance information that can be provided to end

users of data Assist with information discovery by pointing to systems where

particular data is maintained.

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Data Elements

DZ

BE

CN

DK

EG

FR

. . .

ZW

ISO 3166English Name

ISO 31663-Numeric Code

012

056

156

208

818

250

. . .

716

ISO 31662-Alpha Code

Algeria

Belgium

China

Denmark

Egypt

France

. . .

Zimbabwe

Name:Context:Definition:Unique ID: 4572Value Domain:Maintenance Org.Steward:Classification:Registration Authority:Others

ISO 3166French Name

L`Algérie

Belgique

Chine

Danemark

Egypte

La France

. . .

Zimbabwe

DZA

BEL

CHN

DNK

EGY

FRA

. . .

ZWE

ISO 31663-Alpha Code

Traditional MDR:Manage Code Sets

Algeria

Belgium

China

Denmark

Egypt

France

. . .

Zimbabwe

Name: Country IdentifiersContext:Definition:Unique ID: 5769Conceptual Domain:Maintenance Org.:Steward:Classification:Registration Authority:Others

DataElementConcept

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What Can XMDR Do?

Support a new generation of semantic computing Concept system management Harmonizing and vetting concept systems Linkage of concept systems to data Interrelation of multiple concept systems Grounding ontologies and RDF in agreed upon

semantics Reasoning across XMDR content Provision of Semantic Services

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Coming: A Semantic Revolution

Autonomous

Reporting

Community of Interest

Full ControlSearching and rankingPattern analysisKnowledge discoveryQuestion answeringReasoningSemi-automated decision making

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We are trying to manage semantics in an increasingly complex content space

Structured dataSemi-structured dataUnstructured dataTextPictographicGraphicsMultimediaVoice video

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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.

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Example

Combining Concept Systems, Data, and Metadata to answer queries.

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Linking Concepts: Text Document

§ 141.62 Maximum contaminant levelsfor inorganic contaminants.(a) [Reserved](b) The maximum contaminant levelsfor inorganic contaminants specified inparagraphs (b) (2)–(6), (b)(10), and (b)(11)–(16) of this section apply to communitywater systems and non-transient,non-community water systems.The maximum contaminant level specifiedin paragraph (b)(1) of this sectiononly applies to community water systems.The maximum contaminant levelsspecified in (b)(7), (b)(8), and (b)(9)of this section apply to communitywater systems; non-transient, noncommunitywater systems; and transientnon-community water systems.Contaminant MCL (mg/l)(1) Fluoride ............................ 4.0(2) Asbestos .......................... 7 Million Fibers/liter (longerthan 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

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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)

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Concept System (Thesaurus)

Chemical

cadmium lead mercury

Biological Radioactive

chemical pollutant

chemical pollution

Contamination

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Name Acalypha ostryifolia

Mercury Mercury, bis(acetato-.kappa.O)(benzenamine)-

Mercury, (acetato-.kappa.O)phenyl-, mixt. with phenylmercuric propionate

Type Biological Organism

Chemical Chemical Chemical

CAS Number

7439-97-6 63549-47-3 No CAS Number

TSN 28189

ICTV

EPA ID E17113275 E965269

Recent Additions | Contact Us

Environmental Data Registry

Chemicals in EPA Environmental Data Registry

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Data

Monitoring StationsName Latitude Longitude Location

A 41.45 N 125.99 W Merced Lake

B 43.23 N 120.50 WMerced

River

X 39.45 N 118.12 WFletcher

Creek

ID Date Temp Hg

A 2006-09-13 4.4 4

B 2006-09-13 9.3 2

X 2006-09-15 5.2 3

X 2006-09-13 6.7 78

Measurements

A

BX

Merced Lake

Fletcher CreekMerced River

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Metadata

System Data Element Definition Units Precision

Measurements ID Monitoring Station Identifier not applicable not applicable

Measurements Date Date sample was collected not applicable not applicable

Measurements Temp Temperature degrees Celcius 0.1

Measurements Hg Mercury contamination micrograms per liter 0.004

Monitoring Stations Name Monitoring Station Identifier

Monitoring Stations Latitude Latitude where sample was taken

Monitoring Stations LongitudeLongitude where sample was taken

Monitoring Stations Location Body of water monitored

Contaminants Contaminant Name of contaminant

Contaminants Threshold Acceptable threshold value

Metadata

ContaminantsContaminant Threshold

mercury 5

lead 42?

cadmium 250?

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Relations among Inland Bodies of Water

Fletcher Creek

Merced Lake

Merced River

feeds into

feeds intoFletcher Creek Merced Lake

Merced River

fed from feeds into

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Combining Data, Metadata & Concept Systems

ID Date Temp Hg

A 06-09-13 4.4 4

B 06-09-13 9.3 2

X 06-09-13 6.7 78

Name Datatype Definition Units

ID textMonitoring Station Identifier

not applicable

Date date Date yy-mm-dd

Temp numberTemperature (to 0.1 degree C)

degrees Celcius

Hg numberMercury 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

Biological Radioactive

Contamination

lead cadmiummercury

Chemical

Concept system

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Example – Environmental Text Corpus

Idea: Develop an environmental research corpus that could attract R&D efforts. Include the reports and other material from over $1b EPA sponsored research. Prepare the corpus and make it available

Research results from years of ORD R&D Publish associated metadata and concept

systems in XMDR Use open source software for EPA testing

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Extraction Engines

Find concepts and relations between concepts in text, tables, data, audio, video, …

Produce databases (relational tables, graph structures), and other output

Functions: Segment – find text snippets (boundaries important) Classify – determines database field for text

segment Association – which text segments belong together Normalization – put information into standard form Deduplication – collapse redundant information

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Metadata Registries are Useful

Registered semantics For “training” extraction engines The“Normalize” function can make use of standard

code sets that have mapping between representation forms.

The “Classify” function can interact with pre-established concept systems.

Provenance High precision for proper nouns, less precision

(e.g., 70%) for other concepts -> impacts downstream processing, Need to track precision

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Data Elements

DZ

BE

CN

DK

EG

FR

. . .

ZW

ISO 3166English Name

ISO 31663-Numeric Code

012

056

156

208

818

250

. . .

716

ISO 31662-Alpha Code

Algeria

Belgium

China

Denmark

Egypt

France

. . .

Zimbabwe

Name:Context:Definition:Unique ID: 4572Value Domain:Maintenance Org.Steward:Classification:Registration Authority:Others

ISO 3166French Name

L`Algérie

Belgique

Chine

Danemark

Egypte

La France

. . .

Zimbabwe

DZA

BEL

CHN

DNK

EGY

FRA

. . .

ZWE

ISO 31663-Alpha Code

Normalize – Need Registered and Mapped Concepts/Code Sets

Algeria

Belgium

China

Denmark

Egypt

France

. . .

Zimbabwe

Name: Country IdentifiersContext:Definition:Unique ID: 5769Conceptual Domain:Maintenance Org.:Steward:Classification:Registration Authority:Others

DataElementConcept

68

Information Extraction & Semantic Computing

Segment

Classify

Associate

Normalize

Deduplicate

Discover patterns

Select models

Fit parameters

Inference

Report results

Actionable Information

Decision Support

ExtractionEngine

11179-3(E3)

XMDR

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Example – 11179-3 (E3) Support Semantic Web Applications

The address state code is “AB”. This can be expressed as a directedGraph e.g., an RDF statement:

Address

AB

State Code

Node

Node

Edge

Subject

Predicate

Object

XMDR may be used to “ground” the Semantics of an RDF Statement.

Graph RDF

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

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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:

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Ontologies for Data Mapping

Concept Concept

ConceptConceptGeographic Area

Geographic Sub-Area

Country

Country Identifier

Country Name Country Code

Short Name ISO 31662-Character

Code

ISO 31663- Character

Code

Long Name

DistributorCountry Name

Mailing AddressCountry Name ISO 3166

3-Numeric CodeFIPS Code

Ontologies can help to capture and express semantics

74

Example: Content Mapping Service

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.

XMDR could accept XML files with the data from different code sets and return a result mapped to a single code set.

75

Ecoinformatics: Concept System Store

Metadata Registry

Concept System Thesaurus Themes

DataStandards

Ontology GEMET

StructuredMetadata

UsersUsers

Concept systems:KeywordsControlled VocabulariesThesauriTaxonomiesOntologiesAxiomatized Ontologies

(Essentially graphs: node-relation-node + axioms)

}

76

Ecoinformatics: Management of Concept Systems

Metadata Registry

Concept System Thesaurus Themes

DataStandards

Ontology GEMET

StructuredMetadata

UsersUsers

Concept system:RegistrationHarmonization StandardizationAcceptance (vetting)Mapping (correspondences)

}

77

Ecoinformatics: Life Cycle Management

Metadata Registry

Concept System Thesaurus Themes

DataStandards

Ontology GEMET

StructuredMetadata

UsersUsers

Life cycle management:Data andConcept systems(ontologies)

}

78

Ecoinformatics: Grounding Semantics

Metadata Registry

Concept System Thesaurus Themes

DataStandards

Ontology GEMET

StructuredMetadata

UsersUsers

MetadataRegistries Semantic Web

RDF TriplesSubject (node URI)Verb (relation URI)Object (node URI)

Ontologies

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XMDR Project Collaboration

Collaborative, interagency effort EPA, USGS, NCI, Mayo Clinic, DOD, LBNL …&

othersDraws on and contributes to interagency/International

Cooperation on Ecoinformatics Involves Ecoterm, international, national, state, local

government agencies, other organizations as content providers and potential users

Interacts with many organizations around the world through ISO/IEC standards committees

Only loosely aligned with Ecoinformatics Cooperation

80

XMDR Project

High risk R&D, sponsor expected likelihood of failure Targeted toward leading-edge semantics applications in a

highly strategic environment Conceptualization of new capabilities, creation of

designs (expressed as standards), development of a software architecture and prototype system for demonstrating capabilities and testing designs Reasoning, inference, linkage of concepts to data, ….

Demonstration of fundamental semantic management capabilities for metadata registries, understanding the potential applications that could be built in-house

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Results to Date

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

Developed XMDR Prototype -- available as open source software

Content loaded in prototype: broad range of traditional metadata and concept systems

Designs and prototype being explored and used in several locations. Potential for facilitating development and sharing of content by wide diversity of users.

Starting the next version of designs, taking on more challenging content and capabilities

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Status of Project

NSF has funded a three-year project, providing a funding base Strong emphasis on the computer science R&D results and

collaboration with EU and Asia Limited staffing

Proposing further high risk R&D Developing proposals for collaborative efforts to

demonstrate capabilities, especially in the area of water. Opportunity to collaborate with JRC and projects under the

European Commission 7th Framework Program

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Ecoinformatics Test Bed

Proposed in Brussels in September 2004 Project direction and statement developed

Purpose Research and technical informatics to investigate metadata

management techniques. Practical experiment for testing usability.

Initial Focus Use metadata and semantic technologies for air quality

(transportation) health effects Potential for extension to other areas Need for engaging ongoing operations and/or indicators

Bruce the unready

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Ecoinformatics Test Bed

Extend original charter to Water Use Water as example content

Metadata, concept systems Look for opportunities to coordinate with

EU projects WISE, EC 7th Framework program

Identify and propose possible demonstrations