M. Benno Blumenthal International Research Institute for Climate and Society Connecting netcdf/CF to a semantic framework

RDF/OWL and earth science metadata The standards underlying the Semantic Web -- Resource Description Framework (RDF) and Web Ontology Language (OWL), among others – show great promise in addressing some of the basic problems in earth science metadata. In particular they provide a single framework that allows us to describe datasets according to multiple standards, creating a more complete description than any single standard can support, and avoiding the difficult problem of creating a super-standard that can describe everything about everything.

RDF is not a killer app Resource Description Framework (RDF) is a framework to write down relationships in a reusable way, a semantic framework A lot of CF is not written down in a usable way, e.g. relationships between different values of standard_names, or how data representations in CF correspond to concepts or other standards. It is past time to fix that Some would prefer Java, some prefer english

Different Representations of The CF Standard CF EnglishJavaRDF/OWL English – gloriously vague and flexible Java – complete implementation makes a great black-box, an API becomes yet-another-standard RDF/OWL – can facilitate creating/enhancing both the English and Java versions (as well as other programming languages)

CF metadata in a semantic framework A literal level which explains which attributes are available to be attached to datasets/variables, A more semantic level, which gives explicit expression to concepts like Coordinate and Non-Coordinate variables, and how a Non-Coordinate Variable can be geo-located.

Semantic interoperability Writing down the CF standard on a semantic level then allows interoperability with other standards, e.g. other ways of marking geolocated Non-Coordinate variables.

additional issues how to less-ambiguously tag metadata in netcdf files so that software can more easily determine which attribute belongs to which metadata standard How to better register netcdf metadata standards in general How to better register CF concepts so that interoperability (or indeed operability) can occur.

Why RDF? Make implicit semantics explicit Web-based system for interoperating semantics Decontextualizes the information, facilitating reuse 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

One take on semantic interoperability `When I use a word,' Humpty Dumpty said in rather a scornful tone, `it means just what I choose it to mean--neither more nor less.' `The question is,' said Alice, `whether you CAN make words mean so many different things.' `The question is,' said Humpty Dumpty, `which is to be master-- that's all.' Through the Looking Glass (And What Alice Found There) Carroll, Lewis Published: 1871 Type(s): Novels, Young Readers, Fantasy Source: Wikisource

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, so you end up saying less-and-less about more-and- more. 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

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 URI’s do not need to resolve 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

Define terms Attribute Ontology Object Ontology Term Ontology These are different ways RDF can be used

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 e.g. longwell from MIT

cf-att – CF transcribed

cf-att with some attributes

RDF helps decontextualizes {sst variable} cfatt:standard_name “sea_surface_temperature” Where cfatt = the cfatt URI prefix, temporarily u/ontologies/cf-att.owlhttp://iridl.ldeo.columbia.ed u/ontologies/cf-att.owl# Put data in netcdf file Set conventions attribute to “CF-1.0” Set standard_name of variable “sst” to “sea_surface_temperat ure” Current system requires data in a netcdf file for CF to be understood

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 objects 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

Some cf-obj classes

Term Ontology Concepts as individuals Simple Knowledge Organization System (SKOS) is a prime example standard_name as object would be such

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 Partial information can be written down

CF standard names.. I would add that standard names alone (in the cases where a standard name is sufficient) have the same kind of role as common concepts. The definitions of standard names allow some vagueness, though some are more precise than others, because their role is to indicate which things should validly be regarded as the same thing by visualisation and processing software Jonathan Gregory 04/22/08 23:22:01 pcmdi.llnl.gov/trac/ticket/24

CF standard regions I don't think the regions can be exactly standardised, because part of the reason for having names is in order to be somewhat "vague". Just as a common standard name is given to quantities from different data sources when those quantities are regarded as comparable, the same standard region name would be given to data which represent the same region in a way which is regarded as comparable. For instance, different GCMs do not have exactly the same shape for the Atlantic Ocean, but Atlantic meridional overturning streamfunctions are calculated from each model, and these are regarded as comparable. Jonathan Gregory dateWed, Aug 27, 2008 at 5:11 AM

I.E. In other words, the broader the standard, the more vague. On the other hand, we can say something. In fact, we can say quite a lot about how these terms interrelate. And how they relate to less broad, less vague systems.

What we can do easily Establish URI’s for the concepts in CF (standard_names, standard_regions, the attributes themselves) so that statements can be written about them in XML and RDF. Establish a machine-readable version of so that we can write code to extract (decontextualize) metadata from netcdf files Start writing down the relationships between the concepts. Agree on a cf-att ontology, and work on cf-obj so that we can connect with other conventions. Set a convention for explicitly labeling netcdf attributes with their convention, i.e. namespace labels so that process of figuring out which convention covers which attribute is purely gramatical Set a convention for referring to a URI-identified concept in a netcdf file

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

Some Thoughts 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