M.Benno Blumenthal and John del Corral International Research Institute for Climate and Society 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

Standard metadata schema Tools Users Datasets Standard Metadata Schema RDF Tools Users Datasets Standard Metadata Schema RDF Tools Users Datasets Standard Metadata Schem RDF RDF Data Model Exchange RDF Tools Users Datasets Standard Metadata Schema RDF Tools Users Datasets Standard Metadata Schema RDF

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 RDF Virtual (derived) RDF queries

Example: Search Interface Search Interface Users Datasets Search Ontology Dataset Ontology Additional Semantics

Sample Tool: Faceted Search

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

Triplets of Subject Property (or Predicate) Object URI’s identify things, i.e. most of the above Namespaces 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 semantics OWL – 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_name and 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 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

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

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/Queries

RDF Architecture RDF Virtual (derived) RDF queries

Data Servers Ontologies MMI JPL Standards Organizations Start Point RDF Crawler RDFS Semantics Owl Semantics SWRL Rules SeRQL CONSTRUCT Search Queries Location Canonicalizer Time Canonicalizer Sesame Search Interface bibliography IRI RDF Architecture

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

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

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