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