Opportunities for earth science data interoperability through coordinated

semantic development, using a shared model for observations and

measurementsMark Schildhauer

Director of Computing, NCEAS

Logan Utah: CUAHSI Conference on Hydrologic Data and Information Systems

June 2011

SONet

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Integrative Environmental Research

Analyses require a wide range of data– Broad scales: geospatial, temporal, biological (micro-macro)– Diverse topics: abiotic and biotic phenomena

• Predicting impact of invasive insect species on crop production

• Documenting effects of climate change on forest composition

• Large amounts of relevant data…– E.g., over 25,000 data sets are available in the

Knowledge Network for Biocomplexity repository (KNB– http://knb.ecoinormatic.org)

• But researchers struggle to …– Discover relevant datasets for a study– And combine these into an integrated product to analyze

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for Discovery, Access, Interpretation, Re-use:

SHARED KNOWLEDGE MODELS

• Need consistency and rigor in terminology• Standardized protocols, methods when

possible• Interoperability (syntax)• Comparability (semantics)

• Minimally, need a “shared community vocabulary”

• For hydrologists--- WATERML?• For broader, integrative environmental science--- ?

• metadata and keywords are good

start, but not enough: ambiguous, idiosyncratic, hard to parse

• controlled vocabularies: an improvement, but can do more with today’s technology

SHARED KNOWLEDGE MODELS

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SHARED KNOWLEDGE MODELS– Ontologies provide a “shared vocabulary”

• Common “external” definitions (namespaces)• for explicating relationships among terms• describing data schemas (observations)• for machine-assisted discovery, reasoning, integration

– Standard technologies for creating and operating on ontologies:

– Syntaxes: RDF, SKOS, OWL– FOSS applications and frameworks: Jena, Protégé– Standard Reasoners: Pellet, FaCT++, Racer

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Another Opportunity: Observational data

Environmental and earth science data often consists of “observations”

• Data sets are often stored in tables (e.g., flat files, spreadsheets)

• Represent collections of associated measurements

• Highly heterogeneous (format, content, semantics)

• (cell) Values represents measurements

Examples of “raw” observational data

Several prospective observation models…

Project Domain Observational data model

VSTO Atmospheric sciences

Ontologies for interoperability among different meteorological metadata standards and other atmospheric measurements

SERONTO Socioecological research

Ontology for integrating socio-ecological data

OGC’s O&M Geospatial Observations and Measurements standard for enhancing sensor data interoperability

SEEK’s OBOE Ecology Extensible Observation Ontology for describing data as observations and measurements

PATO’s EQ Phenotype/Evolution Underlying model for describing phenotypic traits to link with genomic data

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Observational Data Models

• High degree of similarity across independently derived models

• Opportunity to enable enhanced data interoperability and uniform access– Domain-neutral “foundational” template– Abstracts away underlying format issues – Domain ontologies “extend” core concepts, to formalize

semantics of terms used to describe measurements

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Observational Data Model

• Implemented as an OWL-DL ontology– Provides basic concepts for describing

observations– Specific “extension points” for domain-specific

terms

Entity

Characteristic

Observation

Measurement

Protocol Standard

+ precision : decimal + method : anyType

1..1*

1..1

*

*

*

0..1 0..1

1..1

**

Value

1..1

*

*

Context ObservedEntity

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Observational Data ModelObservations are of entities (e.g., River,

Water, Sample, …)– An observation can have multiple

measurements– Each measurement is taken of the observed

entityEntity

Characteristic

Observation

Measurement

Protocol Standard

+ precision : decimal + method : anyType

1..1*

1..1

*

*

*

0..1 0..1

1..1

**

Value

1..1

*

*

Context ObservedEntity

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Observational Data ModelA measurement consists of

– The characteristic measured (e.g., Ammonium concentration)

– The standard used (e.g., unit, coding scheme)– The measurement protocol– The measurement value

Entity

Characteristic

Observation

Measurement

Protocol Standard

+ precision : decimal + method : anyType

1..1*

1..1

*

*

*

0..1 0..1

1..1

**

Value

1..1

*

*

Context ObservedEntity

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Observational Data ModelObservations can have context

– E.g. geographic, temporal, or biotic/abiotic environment in which some measurement was taken

– Context is an observation too (entity + characteristic)

– Context is transitive Entity

Characteristic

Observation

Measurement

Protocol Standard

+ precision : decimal + method : anyType

1..1*

1..1

*

*

*

0..1 0..1

1..1

**

Value

1..1

*

*

Context ObservedEntity

Similarities among Observational Data Models

FeatureOfInterest

ObservationContext

ObservedProperty

OM_Observation

Result

carrierOfCharacteristic

forProperty

relatedContextObservation

hasResult

OM_Process

usesProcedure

OGC’s Observations and Measurements (O&M)

ofFeature

Similarities among Observational Data Models

Entity

Context (other Observation)

Characteristic

Observation

Standard

hasCharacteristichasMeasurement

ofEntity

hasContext

usesStandard

Protocol

usesProtocol

Precision

hasPrecision

ofCharacteristic

hasValue

SEEK/Semtools Extensible Observation Ontology (OBOE)

Measurement

Seronto basic classes:value_set

physical_thing

parameter_method

parametermethodselection_description

hasParameterMethodhasInvestigationItem

hasValue

hasSample hasMethod hasParameter

scale

hasScale

unithasUnit

hasValue

value_nominalvalue_float

value_nominalvalue_float

Similarities among Observational Data Models

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SHARED KNOWLEDGE MODELSNSF INTEROP program: foster communication among domains to enable greater interoperability• Scientific Observations Network, SONet• Many earth and life science domains

participating• Advanced conceptual modeling• Unifying abstraction of ‘observation’• Semantic web & ontologies• Domain scientists & knowledge engineers

SONet

Developing a core model (SONet project)

Identify the key observational models in the earth and environmental sciences

Are these various observational models easily reconciled and/or harmonized?

Are there special capabilities and features enabled by some observational approaches?

What services should be developed around these observational models?

Similarities among Observational Data Models

Entity FeatureOfInterest

Characteristic ObservedProperty

Measurement OM_Observation

Protocol OM_Process

Result

Standard

Value

Precision

Context ObservationContext

OBOE O&M

SONet/Semtools Semantic Approach

• Data-> metadata-> annotations-> ontologies• Annotations link EML metadata elements to concepts in

ontology thru Observation Ontology• EML metadata describe data and its structures

Linking data values to concepts through observations

• Link data (or metadata) through observational data model to terms from domain-specific ontologies

• Context can inter-relate values in a tuple• Can provide clarification of semantics of data set as a

whole, not just “independent” measurements

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Semantic annotation

Marburg 2011

Attribute mappings

How to use observational data models…

Marburg 2011

linking observational data models to data…

Marburg 2011

Special Mojo of OWL Ontologies

• Class hierarchies– Parent, sibling, child class relationships

• Object properties– to relate instances between classes

• reflexive, symmetric, transitive• Specify domain and ranges Contained in

• Datatype properties– to relate instances to values

• Cardinality

• Polyhierarchies

Special Mojo of OWL Ontologies

• Reasoning offers axioms such as:

– Disjointness: e.g. can’t be both X and Y• inSediment AND inWaterColumn

– Equivalence (classes) or Same_as (instances)• Synonymy across namespaces

– Properties for mereology • Composite of• Contained in• Connected to

– Reasoner can infer relationships, determine inconsistencies in assertions

Special Mojo of Observations

• Enables faceted discovery along entity & characteristic hierarchies

• Economical use of concepts: don’t need to have a “red-colored eye”, or “red-colored wing”– instead re-use concept of “red” with variety of entities

• Express whether observations taken from the same instance or not (tuple explication)– E.g. Multiple chemical concentrations measured from single

water sample• Use of equivalence class (measurement types) to apply

to realized measurements in data

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Ontology Design Pattern

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Ontology Design Pattern

ThesauForm: LaPorte, Huguenot & GarnierTraitNet: Bunker, Ahrestani, Naeem

Acknowledgements

Mark Schildhauer*, Matthew B. Jones, Ben Leinfelder: NCEAS, Santa Barbara CA, USALuis Bermudez:Open Geospatial Consortium Inc., Wayland MA, USAShawn Bowers: Gonzaga University, Spokane WA, USAPhillip C. Dibner: OGCii, Berkeley CA, USACorinna Gries: University of Wisconsin, Madison WI, USA Deborah L. McGuinness: Rensselaer Polytechnic Institute, Troy NY, USAMargaret O’Brien: UCSB, Santa Barbara CA, USAHuiping Cao: New Mexico State University, Las Cruces NM, USASimon J.D. Cox: Earth Science & Resource Engrg, CSIRO, Bentley WA, AUSSteve Kelling, Carl Lagoze: Cornell University, Ithaca NY, USA Hilmar Lapp: NESCent, Durham NC, USAJoshua Madin: Macquarie University, Sydney NSW, AUS

SONet* presenter

This material is based upon work supported by the National Science Foundation under Grant Numbers 0743429, 0753144.

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FIN

“How many fingers, Winston?”

Orwell, 1984