M.Benno Blumenthal and John del Corral
International Research Institute for Climate and Society
http://iridl.ldeo.columbia.edu/ontologies/
Use of RDF/OWL in Ingrid
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
Standard Metadata
Users
Datasets
Tools
Standard Metadata Schema/Data Services
Many Data Communities
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Super Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Standard metadata schema
Super Schema: direct
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Standard metadata schema/data service
Flaws
• A lot of work• Super Schema/Service is the Lowest-
Common-Denominator• Science keeps evolving, so that standards
either fall behind or constantly change
RDF Standard Data Model Exchange
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Tools
Users
Datasets
Standard Metadata Schema
Standard metadata schema
RDF
RDF
RDF
RDF
RDF
RDF
Standard metadata schema
Tools
Users
Datasets
Standard Metadata Schema
RDF
RDFRDF
Tools
Users
Datasets
Standard Metadata Schema
RDF
RDFRDF
Tools
Users
Datasets
Standard Metadata Schem
RDF
RDFRDF
RDF Data Model Exchange
RDF
Tools
Users
Datasets
Standard Metadata Schema
RDF
RDFRDF
Tools
Users
Datasets
Standard Metadata Schema
RDF
RDFRDF
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
RDF Architecture
RDFRDF RDF
RDFRDF RDF
RDFRDF RDF
RDF
RDFRDF RDF
RDFRDF RDF
Virtual (derived) RDF
queries queries queries
Example: Search Interface
Search Interface
Users
Datasets
Search Ontology
Dataset Ontology
Additional Semantics
Sample Tool: Faceted Searchhttp://iridl.ldeo.columbia.edu/ontologies/query2.pl?...
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)
Architectural Features of the search
• Multiple search structures possible• Multiple languages possible• Search structure is kept in the database,
not in the code
http://iridl.ldeo.columbia.edu/ontologies/query2.pl
Triplets of • Subject• Property (or Predicate)• Object
URI’s identify things, i.e. most of the aboveNamespaces are used as a convenient
shorthand for the URI’s
RDF: framework for writing connections
Datatype Properties
{WOA} dc:title “NOAA NODC WOA01”{WOA} dc:description “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”
Object Properties{WOA} iridl:isContainerOf {Grid-1x1},
{Grid-1x1} iridl:isContainerOf {Monthly}
WOA01 diagram
Standard Properties
{WOA} dcterm:hasPart {Grid-1x1},{Grid-1x1} dcterm:hasPart {MONTHLY}
Alternatively
{WOA} iridl:isContainerOf {Grid-1x1},{iridl:isContainerOf} rdfs:subPropertyOf
{dcterm:hasPart}
{SST} rdf:type {cfatt:non_coordinate_variable}, {SST} cfobj:standard_name {cf:sea_surface_temperature}, {SST} netcdf:hasDimension {longitude}
Data Structures in RDF
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
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 semanticsOWL – additional property/class semantics:
more sophisticated (ontological) relationships
SWRL – rules for constructing virtual triples
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
Define terms
• Attribute Ontology• Object Ontology• Term Ontology
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
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
Example: controlled vocabulary
{variable} cfatt:standard_name {“string”}Where string has to belong to a list of
possibilities.
{variable} cfobj:standard_name {stdnam}Where stdnam is an individual of the class
cfobj:StandardName
Example: controlled vocabulary
Bi-direction crosswalk between the two is somewhat trivial, which means all my objects will have both
cfatt:standard_nameand cfobj:standard_name
Example: controlled vocabulary
If I am writing software to read/write netcdf files, I use the cfatt ontology and in particular cfatt:standard_name
If I am making connections/cross-walks to other variable naming standards, I use
cfobj:standard_name
Term Ontology
Concepts as individualsSimple Knowledge Organization System
(SKOS) is a prime exampleThe ontology used here is slightly different:
facets are classes of terms rather than being top_concepts
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 nuancedtagging can proceed in absence of perfect
information
Mapping to Object Oriented Programming
• ActiveRDF• Elmo
Faceted Search Explicated
Search Interface
• Items (datasets/maps)
• Terms• Facets• Taxa
Search Interface Semantic API{item} dc:title dc:description rss:link iridl:icon dcterm:isPartOf {item2} dcterm:isReplacedBy {item2}
{item} trm:isDescribedBy {term}
{term} a {facet} of {taxa} of {trm:Term},{facet} a {trm:Facet}, {taxa} a {trm:Taxa},{term} trm:directlyImplies {term2}
Faceted Search w/Querieshttp://iridl.ldeo.columbia.edu/ontologies/query2.pl?...
RDF Architecture
RDFRDF RDF
RDFRDF RDF
RDFRDF RDF
RDF
RDFRDF RDF
RDFRDF RDF
Virtual (derived) RDF
queries queries queries
Data ServersOntologies
MMI
JPL
StandardsOrganizations
Start Point
RDF Crawler
RDFS SemanticsOwl SemanticsSWRL Rules
SeRQL CONSTRUCT
Search Queries
LocationCanonicalizer
TimeCanonicalizer
Sesame
Search Interface
bibliography
IRI RDF Architecture
Cast of Characters
NC – netcdf data file formatCF – Climate and Forecast metadata
convention for netcdfSWEET - Semantic Web for Earth and
Environmental Terminology (OWL Ontology)
IRIDL – IRI Data Library
CF attributes
SWEET Ontologies(OWL)
Search Terms
CF Standard Names(RDF object)
IRIDL Terms
NC basic attributes
IRIDLattributes/objects
SWEET as Terms
CF Standard NamesAs Terms
Gazetteer Terms
CF data objects
Location
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
Future Work Possibilities
More Usable Search InterfaceTagging Interface that uses tag interrelationships
to simplify choicesData Format translation using semantics“Related Object Browsing” given a dataset, find
related data, papers, imagesDocument/execute/create analysis treesStovepipe conventions/bash-to-fitLess Monolithic IRI Data Library
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.
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
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