1. M.Benno Blumenthal, Michael Bell, John del Corral, and Emily Grover-Kopec International Research Institute for Climate and Society Columbia University http://iridl.ldeo.columbia.edu/ http://iridl.ldeo.columbia.edu/ Using RDF/OWL Technologies for Discovery and Use Metadata

2. Definitions Resource Description Framework (RDF) Web Ontology Language (OWL)

3. Why RDF? Web-based system for interoperating semantics A key part of the Semantic Web RDF/OWL is an interesting technology, but it is even more interesting when it is clear that it can help solve our problems

4. The Data Problem Users Datasets

5. The Tool Interface Users Datasets Tools

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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. RDF Architecture RDF Virtual (derived) RDF queries

14. 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 uses enhanced mappings between models that are close EASIER – these are tools to enhance the mapping process

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

16. 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)

17. 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

18. Cast of RDF Characters Semantic Layers Query Language Tools and Frameworks SPARQLProtégé RDFSSeRQL OWLSesame SKOSReasonersRedland SWRLJena

19. 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

20. 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”

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

22. WOA01 diagram

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

24. {SST} rdf:type {cfatt:non_coordinate_variable}, {SST} cfatt:standard_name {cf:sea_surface_temperature}, {SST} netcdf:hasDimension {longitude} netcdf/CF 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

25. Noncontextual Modeling “noncontextual modeling make RDF the perfect glue between systems and fixed data models” – The Semantic Web

26. RDF Level Transport/Exchange (RDF/XML) Storage RDF APIs (Redland,Jena,Sesame) Query (SPARQL,SeRQL, …) Basic Semantics

27. RDF Semantics RDF Primer RDF Primer Truly useful propertyrdf:type “a” Underlying Classrdf:Property Organizational Classesrdf:Bag rdf:Alt rdf:Seq rdf:List Structured valuesrdf:value Reificationrdf:Statement: rdf:subject rdf:predicate rdf:object Bag Propertiesrdf:_1 rdf:_2 … List Propertiesrdf:first rdf:rest:rdf:nil

28. RDF-Schema (RDFS)(RDFS) Transitive Propertiesrdfs:subClassOf (“is a”), rdfs:subPropertyOf rdfs:Class, rdfs:Resource rdfs:member rdfs:domain, rdfs:range rdfs:Datatype, rdfs:Literal, rdfs:Container Refering to other RDF documents rdfs:seeAlso, rdfs:isDefinedBy Basic documentationrdfs:label, rdfs:comment

29. Gazetteer Classes

30. Gazetteer Individuals

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

32. Semantics lead to Virtual Triples Transitive: {a} rdfs:subClassOf {b} rdfs:subClassOf {c} implies {a} rdfs:subClassOf {c} i.e. semantics of rdfs:subClassOf imply additional triples not explicitly stated Likewise: {a} rdfs:subPropertyOf{b} rdfs:subPropertyOf {c} implies {a} rdfs:subPropertyOf {c} More interestingly, {a} myprop {b}, {myprop} rdfs:subPropertyOf {prop2} implies {a} prop2 {b}

33. Subcategories are not subClasses So carelessly translating existing conceptual organizations can get one into trouble

34. Domain and Range are inherited Since the domain and range of a property are classes, then subclasses “inherit” properties (in this sense)

35. UML/RDFS Unified Modeling Language Base concepts are the same (RDFS lacks methods), so one can export the underlying structure of the code as the underlying structure for the metadata See Representing UML in RDFRepresenting UML in RDF

36. Ontologies 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

37. 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 is a serious split in world view from what we have been talking about: concepts as classes vs concepts as individuals

38. OWL rdf:Propertyowl:DatatypeProperty owl:ObjectProperty owl:AnnotationProperty owl:FunctionalProperty owl:InverseFunctionalProperty owl:TransitiveProperty owl:SymmetricProperty rdfs:seeAlsoowl:imports owl:ontology

39. Protégé Tool for editing/displaying Ontologies Different “tabs” display different perspectives http://protege.stanford.edu/

40. Cast of RDF Characters II Semantic Layers Query Language Tools and Frameworks SPARQLProtégé RDFSSeRQL OWLSesame SKOSReasonersRedland SWRLJena

41. Query Language: SPARQLSPARQL (quick reference at http://www.dajobe.org/2005/04-sparql/) http://www.dajobe.org/2005/04-sparql/ Supported by Redland, Jena, Sesame-2.0 (alpha) Jena implementation supports url source of triples, i.e. do not even need a triple store The standard

42. Query Language: SeRQLSeRQL Older than SPARQL Implemented on top of Sesame Currently more powerful than SPARQL, i.e. has nested queries

43. SeRQL Details Copied from on-line tutorialon-line tutorial Syntax Select Construct Where From

44. SeRQLSeRQL: basic syntax {person} foo:worksFor {Company} rdf:type {foo:ITCompany}

45. SeRQLSeRQL: multiple statements {subj1} pred1 {obj1}; pred2 {obj2} Or {subj1} pred1 {obj1}, {subj1} pred2 {obj2}

46. SeRQLSeRQL: short cuts {subj1} pred1 {obj1,obj2,obj3} (also implies obj1,obj2,obj3 are distinct)

47. SeRQLSeRQL: Select SELECT dataset, dlabel FROM {dataset} rdf:type {iridl:dataset}, [{dataset} rdfs:label {dlabel}] USING NAMESPACE iridl = Output as table (XML)

48. SeRQLSeRQL:Construct CONSTRUCT {dataset} rdf:type {foo:LabelledDatasets} FROM {dataset} rdf:type {iridl:dataset}; rdfs:label {dlabel} USING NAMESPACE iridl = Output as RDF (RDF/XML)

49. Faceted Search Explicated

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

51. 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}

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

53. RDF Architecture RDF Virtual (derived) RDF queries

54. 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

55. Creating Virtual Triples from Semantic Layers Semantic Layers Query Language Tools and Frameworks SPARQLProtégé RDFSSeRQL OWLSesame SKOSReasonersRedland SWRLJena

56. SWRL SWRL: A Semantic Web Rule Language Combining OWL and RuleML A language for writing rules in RDF/OWL, i.e. RDF statements that are rules for creating new RDF statements

57. Simple Knowledge Organization System (SKOS)SKOS Schema for relating concepts

58. Simple Knowledge Oranization System (SKOS)SKOS So, for a resource of type skos:Concept, any properties of that resource (such as creator, date of modification, source etc.) should be interpreted as properties of a concept, and not as properties of some 'real world thing' that that resource may be a conceptualisation of. This layer of indirection allows thesaurus-like data to be expressed as an RDF graph. The conceptual content of any thesaurus can of course be remodelled as an RDFS/OWL ontology. However, this remodelling work can be a major undertaking, particularly for large and/or informal thesauri. A SKOS Core representation of a thesaurus maps fairly directly onto the original data structures, and can therefore be created without expensive remodelling and analysis

59. RDF Frameworks Protégé API RedlandBindings in many languages, supports several triple stores, some with context JenaJava API, some cmd line utilities, supports inference layers SesameHTTP server, Java API, supports inference, version 2 alpha has context

60. Sesame SAIL- Storage and Inference Layer i.e. you can write down rules that imply virtual triples so that triples are generated as they are put into the store RDFNo inference RDFSRDFS inference OWLIMSome OWL inference Custom

61. Jena Java framework In-memory and persistent stores Inference API

62. Topics/Issues OpenDAP and RDF: can we transport data semantics without fixing the entire schema?OpenDAP and RDF netcdf/HDF and RDF: do we need non- contextual modeling in our metadata transport/storage? Concepts as classes vs concepts as individuals Sub-classes vs sub-categories OWL in detail Protégé demo

63. RDF Cast of Characters Semantic Layers Query Language Tools and Frameworks SPARQLProtégé RDFSSeRQL OWLSesame SKOSReasonersRedland SWRLJena