1. An Introduction to the Semantic Web M. Benno Blumenthal International Research Institute for Climate and Society Columbia University 2 November 2011

2. Semantic Web ● “a web of data that can be processed directly and indirectly by machines” ● Web 3.0 ● really it is about Explicit Semantics ● URI ● Resource Description Framework (RDF) ● Web Ontology Language (OWL)

3. Semantic Web Stack T. Berners-Lee

4. Why use 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 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

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

6. Many Data Communities ● Semantic walls ● Exchange walls 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. RDF Architecture RDF Virtual (derived) RDF queries

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

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. 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

19. 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”

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

21. WOA01 diagram

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

23. {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

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

25. 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

26. Multiple Ways of Expressing Concepts in RDF Note that there are many world views in how to express concepts: concepts as classes vs concepts as individuals vs concept as predicate values

27. 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

28. Faceted Search Explicated

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

30. 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}

31. RDF Architecture RDF Virtual (derived) RDF queries

32. Data Servers Ontologies MMI JPL Standards Organizations Start Point RDF/XML-Schema Crawler XSLT/GRDDL ingest XML Schema to OWL translation Owl Semantics SWRL Rules SeRQL CONSTRUCT Search Queries Location Canonicalizer Time Canonicalizer Sesame Search Interface bibliography IRI RDF Architecture

33. Models, Crosswalks, and Objects Structure of the RDF information that we are using to represent data objects in multiple frameworks (see full figure)full figure

34. Semantic Crosswalk for metadata translation

36. Semantic Web ● Universal ids (URIs) ● Multiple partial representations adding to be a more complete picture John Godfrey Saxe (1816-1887)

37. Semantic Web Stack T. Berners-Lee

38. Define terms ● Attribute Ontology ● Object Ontology ● Term Ontology

39. 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

40. 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

41. 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

42. 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

43. 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

44. 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