Geoscience Knowledge Representation Using the SWEET Ontologies

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Geoscience Knowledge Representation Using the
SWEET Ontologies

• Rob Raskin
• Jet Propulsion Laboratory

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Transforming Data into Knowledge
Data Information Knowledge
Basic Elements Bytes Numbers Models Facts Serv
ices Ingest Archive Visualize
Infer Understand Predict Storage File
Database HDF-EOS GIS MIS Ontology
Mind Interoperability Syntactic OPeNDAP
WMS/WCS Semantic Volume/Density High/Low Low/H
igh Statistics Checksum Moments
Descriptive Inferential Analysis
Fourier Wavelet EOF
SSA Methodology Exploratory-analysis
Model-based-mining
Syntax Semantics
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What is Knowledge?

• Facts, relations, meanings, contexts
• Organized information
• Core ingredient in common sense
• Common understanding
• In a form to apply reasoning/inference
• Dynamic
• Expandable

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Semantic Understanding is Difficult!
Sea surface temperature measured 3 m above
surface Sea surface temperature measured at
surface
Data quality 5
Variable t temperature Variable t time
Lets eat, Grandma. Lets eat Grandma.
Time flies like an arrow. Fruit flies like a pie.
LA Times headline
Mission accomplished. Major combat operations in
Iraq have ended
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Database vs Knowledge Base

• Database
• Entities and Relations
• Closed world
• All facts included
• Knowledge base
• Classes and Properties
• Collection of facts
• Captures corporate memory
• Open world
• Facts not stated may be either true or untrue

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PO.DAAC Knowledge Bases
Public access
People
Documents
Roles/Tasks
Data Processing
(Docushare)
Data Products
Metadata
Tools/ Services
Web Pages
Science Concepts
Missions
Instruments
Organiza- tions
Applications
Announce- ments
Inquiries
Computers
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Relations

• People have roles
• Instruments measure science parameters
• Inquiries relate to data products
• etc.

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Example of Knowledge-Assisted Service

• Yellow Page Lookup
• cars vs automobiles
• Hotels vs motels vs resorts

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Semantic-based Service Example Google

• Type into Google gymnasiums in Seattle
• Generates map of Seattle with dots locating gyms
• Google understands that
• Seattle is a place
• Gymnasiums is a place-based service
• Google understands semantics so that the search
  results also could include
• locations near Seattle
• Similar services (e.g., health club)

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Assertion of Facts as Triples

• Subject-Verb-Object representation
• Flood subClassOf WeatherPhenomena
• HDF subClassOf FileFormat
• Pressure subClassOf PhysicalProperty
• Ocean hasSubstance Water
• AIRS measures Temperature

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Applications

• Software tools can find meaning in resources
  for
• Discovery
• Fusion
• Lineage
• Requirements
• Data products associated with objects in science
  concept space
• Richer descriptions than DIFs
• Data services associated with objects in service
  concept space
• Richer descriptions than SERFs
• Search/fusion tools that exploit ontologies

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Semantic Web Vision

• Web page creators place XML tags around technical
  terms on web pages
• XML tags point to knowledge base where term is
  defined
• Search tools use this information to provide
  value-added services
• Common search engines (Google) use these
  capabilities only minimally, at present

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Ontologies

• Current preferred method to store facts
• General definition all that is known
• Computer science definition Machine-readable
  definition of terms and how they relate to one
  another
• As with a dictionary, terms are defined in terms
  of other terms
• Provide shared understanding of concepts
• Support knowledge reuse
• Support machine-to-machine communications with
  deeper semantics than controlled vocabulary

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XML-based Ontology Languages

• XML satisfies desired properties for language
  syntax
• Readable by both humans and machines
• However, there are too many possible ways that
  XML tags can be named and used
• No standardization of XML tag meanings as in HTML
  (ltbgt lt/bgt pair gt renders in bold)
• Additional standardized semantics needed to
  exploit shared understanding of concepts

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RDF and OWL

• W3C has adopted languages that specialize XML
  Resource Description Formulation (RDF)
• Ontology Web Language (OWL)
• Languages predefine specific tags
• RDF Class, subclass, property, subproperty,
• RDF and OWL form a nested collection of
  languages, each roughly a specialization of the
  preceding language with further shared
  understanding
• XML
• RDF
• RDFS
• OWL Lite
• OWL DL
• OWL Full

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Semantic Web for Earth and Environmental
Terminology (SWEET)

• SWEET is a concept space
• Enables scalable classification of Earth system
  science concepts
• Currently being expanded to Space science
• Anybody can import, expand, and specialize the
  work of others
• No need to regenerate a physics, chemistry, or
  math ontology
• Concept space is translatable into other
  languages/cultures using sameAs notions

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SWEET Ontologies and Their Interrelationships
Faceted Ontologies
Non-Living Substances
Living Substances
Integrative Ontologies
Physical Processes
Natural Phenomena
Earth Realm
Human Activities
Physical Properties
Data
Time
Space
Units
Numerics
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SWEET as an Upper Level Earth Science Ontology
Math
Physics
Chemistry
Space
import
Property EarthRealm Process, Phenomena Substance
Data
SWEET
Time
import
Stratospheric Chemistry
Biogeochemistry
Specialized domains
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Why an Upper-Level Ontology for Earth System
Science?

• Many common concepts used across Earth Science
  disciplines (such as properties of the Earth)
• Provides common definitions for terms used in
  multiple disciplines or communities
• Provides common language in support of community
  and multidisciplinary activities
• Provides common properties (relations) for tool
  developers
• Reduced burden (and barrier to entry) on creators
  of specialized domain ontologies
• Only need to create ontologies for incremental
  knowledge

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How SWEET was Initially Populated

• Initial sources
• GCMD
• Over 10,000 datasets
• Over 1000 keywords
• Data providers submit far more than the 1000
  terms for free-text search
• CF
• Over 500 keywords
• Very long term names
• surface_downwelling_photon_spherical_irradiance_in
  _sea_water
• Decomposed into facets

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Spatial Ontology

• Concepts of 0-D, 1-D, 2-D, and 3-D objects
• Default coordinate system lat/lon/up
• Polygons used to store spatial extents
• Spatial attributes added (population, area, etc.)
• Scientific applications include geology to
  represent 3-D structure

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Numerical Ontologies

• Numerics
• Extents interval, point, 0, positiveIntegers,
• Relations lessThan, greaterThan,
• SpatialEntities
• Extents country, Antarctica, equator, inlet,
• Relations above, northOf,
• TemporalEntities
• Extents duration, century, season,
• Relations after, before,

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Numerical Ontologies (cont.)

• Numeric concepts defined in OWL only through
  standard XML XSD spec
• Intervals defined as restrictions on real line
• Numerical relations defined in SWEET
• lessThan, max,
• Cartesian product (multidimensional spaces) added
  in SWEET
• Numeric ontologies used to define spatial and
  temporal concepts

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Conceptual Ontologies

• Phenomena
• ElNino, Volcano, Thunderstorm, Deforestation)
• Each has associated, spatial/temporal extent,
  EarthRealms, PhysicalProperties etc.
• Specific instances included
• e.g., 1997-98 ElNino
• Human Activities
• Fisheries, IndustrialProcessing, Economics,
  Public Good
• State
• History or state of planet or component

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SWEET Users

• ESML- Earth Science Markup Language
• ESIP - Earth Science Information Partner
  Federation
• GEON- Geosciences Network
• GENESIS- Global Environmental Earth Science
  Information System
• IRI- International Research Institute (Columbia)
• LEAD- Linked Environments for Atmospheric
  Discovery
• MMI- Marine Metadata Initiative
• NOESIS
• PEaCE- Pacific Ecoinformatics and Computational
  Ecology
• SESDI- Semantically Enabled Science Data
  Integration
• VSTO- Virtual Solar-Terrestrial Observatory

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Collaboration Web Site

• Discussion tools
• Blog, wiki, moderated discussion board
• Version Control/ Configuration Management
• Trace dependencies on external ontologies
• Tools to search for existing concepts in
  registered ontologies
• Ontology Validation Procedure
• W3C note is formal submission method
• Registry/discovery of ontologies
• Support workflows/services for ontology
  development

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Community Issues

• Content
• Maintain alignment given expansion of classes and
  properties
• Standards and Conventions
• Agreement on standards for use of OWL
• Fuzzy representation conventions
• Review Board
• Who will oversee and maintain for perpetuity (or
  at least through the next funding cycle)
• ESIP Federation? ESSI?
• Global Support
• Provide tools to visualize and appreciate the big
  picture

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Update/Matching Issues

• No removal of terms except for spelling or
  factual errors
• Subscription service to notify affected
  ontologies when changes made
• Must avoid contradictions
• Additions can create redundancy if sameAs not
  used
• Humans must oversee matching
• CF has established moderator to carry out
  analogous additions
• OWL import imports entire file
• Associate community with ontology terms
• Community tagging

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Best Practices

• Keep ontologies small, modular
• Be careful that OwlImport imports everything
• Use higher level ontologies where possible
• Identify hierarchy of concept spaces
• Model schemas
• Try to keep dependencies unidirectional