1. M.Benno Blumenthal and John del Corral International Research Institute for Climate and Society http://iridl.ldeo.columbia.edu/ontologies/ Using a Resource Description Framework (RDF) to carry metadata for climate datasets

2. 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

3. Standard Metadata Users Datasets Tools Standard Metadata Schema/Data Services

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. RDF Architecture RDF Virtual (derived) RDF queries

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

13. Sample Tool: Faceted Search http://iridl.ldeo.columbia.edu/ontologies/query2.pl?...

14. 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)

15. 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

16. 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

17. 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”

18. Object Properties {WOA} iridl:isContainerOf {Grid-1x1}, {Grid-1x1} iridl:isContainerOf {Monthly}

19. WOA01 diagram

20. Standard Properties {WOA} dcterm:hasPart {Grid-1x1}, {Grid-1x1} dcterm:hasPart {MONTHLY} Alternatively {WOA} iridl:isContainerOf {Grid-1x1}, {iridl:isContainerOf} rdfs:subPropertyOf {dcterm:hasPart}

21. {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

22. Search Interface Term http://iri.columbia.edu/~benno/sampleterm. pdfhttp://iri.columbia.edu/~benno/sampleterm. pdf

23. 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

24. 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

25. Define terms Attribute Ontology Object Ontology Term Ontology

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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

32. 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

33. Faceted Search Explicated

34. Search Interface Items (datasets/maps) Terms Facets Taxa

35. 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}

36. Faceted Search w/Queries http://iridl.ldeo.columbia.edu/ontologies/query2.pl?...

37. RDF Architecture RDF Virtual (derived) RDF queries

38. 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

39. 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

40. 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

41. 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

42. 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

43. Question: Canonical Objects Given a set of individuals related by owl:sameAs, semantics says predicates that point to one individual should point to all the individuals. But as a programmer I usually want to work with one (the canonical object).

44. Question: Concept Class Membership Seemingly the only way to use a concept which is expressed as an OWL class is rdf:type, i.e. membership. Should I really be putting datasets into conceptual classes? Shouldn’t there be more choice of relationship between a concept and a dataset?

45. Question: OWL/SKOS Crosswalk Given a concept in both OWL and Term (SKOS) frameworks, is it possible to crosswalk between them, in particular preserving the ability of Term to have different predicates with the item being described? I’ve tried dual-defined objects, not sure it works yet …

46. 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

47. Overview IRI Data Collection Generalized Data Tools Specialized Data Tools Dataset Variable ivar multidimensional Data Viewer Data Language Maproom URL/URI for data, calculations, figs, etc

48. IRI Data Collection Dataset Variable ivar multidimensional Economics Public Health “geolocated by entity” GIS “geolocation by vector object or projection metadata” Ocean/Atm “geolocated by lat/lon” multidimensional spectral harmonics equal-area grids GRIB grid codes climate divisions IRI Data Collection

49. IRI Data Collection Dataset Variable ivar Servers OpenDAP THREDDS GRIB netCDF images binary Database Tables queries spreadsheets shapefiles images w/proj IRI Data Collection

50. IRI Data Collection Dataset Variable ivar Calculations “virtual variables” images graphics descriptive and navigational pages OpenGIS WMS v1.3 WCS Data Files netcdf binary Images GeoTiff Clients OpenDAP THREDDS Tables Servers OpenDAP THREDDS GRIB netCDF images binary Database Tables queries spreadsheets shapefiles images w/proj IRI Data Collection

51. IRI General Data Tools Data Page

52. IRI General Data Tools Data Viewer

53. IRI General Data Tools Cut and Paste

54. IRI Map Room

55. Malaria Early Warning System Front page illustrates most recent dekadal rainfall estimates (FEWS RFE) Change dates to view different time periods Administrative and epidemiological overlays available Click and drag box across map to zoom IRI Map Room