Use of RDFOWL in Ingrid

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Use of RDF/OWL in Ingrid
M.Benno Blumenthal and John del
Corral International Research Institute for
Climate and Society http//iridl.ldeo.columbia.
edu/ontologies/
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Why RDF?

• Make implicit semantics explicit
• Web-based system for interoperating semantics
• RDF/OWL is an emerging technology, so tools are
  being built that help solve the semantic problems
  in handling data

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Standard Metadata
Standard Metadata Schema/Data Services
Datasets
Tools
Users
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Many Data Communities
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Super Schema
Standard metadata schema
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Super Schema direct
Standard metadata schema/data service
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Flaws

• A lot of work
• Super Schema/Service is the Lowest-Common-Denomina
  tor
• Science keeps evolving, so that standards either
  fall behind or constantly change

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RDF Standard Data Model Exchange
Standard metadata schema
RDF
RDF
RDF
RDF
RDF
RDF
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RDF Data Model Exchange
Standard metadata schema
RDF
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Why is this better?

• Maps the original dataset metadata into a
  standard format that can be transported and
  manipulated
• Still the same impedance mismatch when mapped to
  the least-common-denominator standard metadata,
  but
• When a better standard comes along, the original
  complete-but-nonstandard metadata is already
  there to be remapped, and late semantic binding
  means everyone can use the new semantic mapping
• Can use enhanced mappings between models that
  have common concepts beyond the
  least-common-denominator
• EASIER tools to enhance the mapping process,
  mappings build on other mappings

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RDF Architecture
Virtual (derived) RDF
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Example Search Interface
Additional Semantics
Dataset Ontology
Search Ontology
Datasets
Search Interface
Users
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Sample Tool Faceted Search
http//iridl.ldeo.columbia.edu/ontologies/query2.p
l?...
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Distinctive Features of the search

• Search terms are interrelated
• terms that describe the set of returns are
  displayed (spanning and not)
• Returned items also have structure (sub-items and
  superseded items are not shown)

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Architectural Features of the search
http//iridl.ldeo.columbia.edu/ontologies/query2.p
l

• Multiple search structures possible
• Multiple languages possible
• Search structure is kept in the database, not in
  the code

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RDF framework for writing connections

• Triplets of
• Subject
• Property (or Predicate)
• Object
• URIs identify things, i.e. most of the above
• Namespaces are used as a convenient shorthand for
  the URIs

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Datatype Properties

• WOA dctitle NOAA NODC WOA01
• WOA dcdescription NOAA NODC WOA01 World
  Ocean Atlas 2001, an atlas of objectively
  analyzed fields of major ocean parameters at
  monthly, seasonal, and annual time scales.
  Resolution 1x1 Longitude global Latitude
  global Depth 0 m,5500 m Time Jan,Dec
  monthly

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Object Properties
WOA iridlisContainerOf Grid-1x1, Grid-1x1
iridlisContainerOf Monthly
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WOA01 diagram
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Standard Properties

• WOA dctermhasPart Grid-1x1,
• Grid-1x1 dctermhasPart MONTHLY
• Alternatively
• WOA iridlisContainerOf Grid-1x1,
• iridlisContainerOf rdfssubPropertyOf
  dctermhasPart

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Data Structures in RDF
SST rdftype cfattnon_coordinate_variable,
SST cfobjstandard_name cfsea_surface_tempera
ture, SST netcdfhasDimension longitude

• Object properties provide a framework for
  explicitly writing down relationships between
  data objects/components, e.g. vague meaning of
  nesting is made explicit
• Properties also can be related, since they are
  objects too

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Virtual Triples

• Use Conventions to connect concepts to
  established sets of concepts
• Generate additional virtual triples from the
  original set and semantics
• RDFS some property/class semantics
• OWL additional property/class semantics more
  sophisticated (ontological) relationships
• SWRL rules for constructing virtual triples

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OWL

• Language for expressing ontologies, i.e. the
  semantics are very important. However, even
  without a reasoner to generate the implied RDF
  statements, OWL classes and properties represent
  a sophistication of the RDF Schema
• However, there are many world views in how to
  express concepts concepts as classes vs concepts
  as individuals vs concept as predicate

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Define terms

• Attribute Ontology
• Object Ontology
• Term Ontology

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Attribute Ontology

• Subjects are the only type-object
• Predicates are attributes
• Objects are datatype
• Isomorphic to simple data tables
• Isomorphic to netcdf attributes of datasets
• Some faceted browsers predicate facet

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Object Ontology

• Objects are object-type
• Isomorphic to belongs to
• Isomorphic to multiple data tables connected by
  keys
• Express the concept behind netcdf attributes
  which name variables
• Concepts as objects can be cross-walked
• Concepts as object can be interrelated

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Example controlled vocabulary

• variable cfattstandard_name string
• Where string has to belong to a list of
  possibilities.
• variable cfobjstandard_name stdnam
• Where stdnam is an individual of the class
  cfobjStandardName

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Example controlled vocabulary

• Bi-direction crosswalk between the two is
  somewhat trivial, which means all my objects will
  have both
• cfattstandard_name
• and
• cfobjstandard_name

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Example controlled vocabulary

• If I am writing software to read/write netcdf
  files, I use the cfatt ontology and in particular
  cfattstandard_name
• If I am making connections/cross-walks to other
  variable naming standards, I use
• cfobjstandard_name

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Term Ontology

• Concepts as individuals
• Simple Knowledge Organization System (SKOS) is a
  prime example
• The ontology used here is slightly different
  facets are classes of terms rather than being
  top_concepts

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Nuanced tagging

• Concepts as objects can be interrelated specific
  terms imply broader terms
• Object ends up being tagging with terms ranging
  from general to specific.
• Search can then be nuanced
• tagging can proceed in absence of perfect
  information

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Mapping to Object Oriented Programming

• ActiveRDF
• Elmo

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Faceted Search Explicated
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Search Interface

• Items (datasets/maps)
• Terms
• Facets
• Taxa

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Search Interface Semantic API

• item dctitle dcdescription rsslink
  iridlicon
• dctermisPartOf item2
• dctermisReplacedBy item2
• item trmisDescribedBy term
• term a facet of taxa of trmTerm,
• facet a trmFacet, taxa a trmTaxa,
• term trmdirectlyImplies term2

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Faceted Search w/Queries
http//iridl.ldeo.columbia.edu/ontologies/query2.p
l?...
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RDF Architecture
Virtual (derived) RDF
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IRI RDF Architecture
Data Servers
MMI
Ontologies
JPL
Start Point
bibliography
Standards Organizations
RDF Crawler
Location Canonicalizer
RDFS Semantics Owl Semantics SWRL Rules SeRQL
CONSTRUCT
Time Canonicalizer
Sesame
Search Queries
Search Interface
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Cast of Characters

• NC netcdf data file format
• CF Climate and Forecast metadata convention for
  netcdf
• SWEET - Semantic Web for Earth and Environmental
  Terminology (OWL Ontology)
• IRIDL IRI Data Library

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CF attributes
NC basic attributes
IRIDL attributes/objects
CF data objects
CF Standard Names (RDF object)
SWEET Ontologies (OWL)
Location
IRIDL Terms
CF Standard Names As Terms
SWEET as Terms
Search Terms
Gazetteer Terms
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Thoughts

• Pure RDF framework seems currently viable for a
  moderate collection of data
• Potential for making a lot of implicit data
  conventions explicit
• Explicit conventions can improve interoperability
• Simple RDF concepts can greatly impact searches

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Future Work Possibilities

• More Usable Search Interface
• Tagging Interface that uses tag
  interrelationships to simplify choices
• Data Format translation using semantics
• Related Object Browsing given a dataset, find
  related data, papers, images
• Document/execute/create analysis trees
• Stovepipe conventions/bash-to-fit
• Less Monolithic IRI Data Library

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Implications for Curator/Metafor

• Reproducibility implies complete metadata
• Non-standard complete metadata just needs to be
  mapped to more standard schemes
• A multiple-scheme system like RDF retains
  reproducibility even with partial mapping to
  standards
• Should be able to measure the misfit find the
  space of the unexplained guidance for
  developing standards.

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Stovepipe Conventions

• Fixed Schema
• Agreed upon metadata domain
• Agreed upon data domain
• Designed to be a partial solution
• General server software needs to decide whether
  data legitimately fits the standard
• User contemplates bash-to-fit