using rdf/owl technologies for discovery and use metadata

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/

Using RDF/OWL Technologies for Discovery and Use Metadata

Definitions

• Resource Description Framework (RDF)

• Web Ontology Language (OWL)

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

The Data Problem

Users

Datasets

The Tool Interface

Users

Datasets

Tools

Standard Metadata

Users

Datasets

Tools

Standard Metadata Schema/Data Services

Many Data Communities

Tools

Users

Datasets

Standard Metadata Schema

Tools

Users

Datasets


Tools

Users

Datasets


Tools

Users

Datasets


Tools

Users

Datasets


Super Schema

Tools

Users

Datasets


Tools

Users

Datasets


Tools

Users

Datasets


Tools

Users

Datasets


Tools

Users

Datasets


Standard metadata schema

Super Schema: direct

Tools

Users

Datasets


Tools

Users

Datasets


Tools

Users

Datasets


Tools

Users

Datasets


Tools

Users

Datasets


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


Tools

Users

Datasets


Tools

Users

Datasets


Tools

Users

Datasets


Tools

Users

Datasets



RDF

RDF

RDF

RDF

RDF

RDF


Tools

Users

Datasets


RDF

RDFRDF

Tools

Users

Datasets


RDF

RDFRDF

Tools

Users

Datasets

Standard Metadata Schem

RDF

RDFRDF

RDF Data Model Exchange

RDF

Tools

Users

Datasets


RDF

RDFRDF

Tools

Users

Datasets


RDF

RDFRDF

RDF Architecture

RDF

RDF RDF

RDF

RDF RDF

RDF

RDF RDF

RDF

RDF

RDF RDF

RDF

RDF RDF

Virtual (derived) RDF

queries queries queries

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

Sample Tool: Faceted Searchhttp://iridl.ldeo.columbia.edu/ontologies/query2.pl?...

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

http://iridl.ldeo.columbia.edu/ontologies/query2.pl

Cast of RDF Characters

Semantic

Layers

Query

Language

Tools and Frameworks

SPARQL Protégé

RDFS SeRQL

OWL Sesame

SKOS Reasoners Redland

SWRL Jena

Triplets of • Subject• Property (or Predicate)• Object

URI’s identify things, i.e. most of the aboveNamespaces 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} 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

Noncontextual Modeling

• “noncontextual modeling make RDF the perfect glue between systems and fixed data models” – The Semantic Web

RDF Level

• Transport/Exchange (RDF/XML)

• Storage

• RDF APIs (Redland,Jena,Sesame)

• Query (SPARQL,SeRQL, …)

• Basic Semantics

RDF SemanticsRDF Primer

Truly useful property rdf:type “a”

Underlying Class rdf:Property

Organizational Classes rdf:Bag rdf:Alt rdf:Seq

rdf:List

Structured values rdf:value

Reification rdf:Statement: rdf:subject rdf:predicate rdf:object

Bag Properties rdf:_1 rdf:_2 …

List Properties rdf:first rdf:rest:rdf:nil

RDF-Schema (RDFS)

Transitive Properties rdfs: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 documentation rdfs:label, rdfs:comment

Gazetteer Classes

Gazetteer Individuals

Search Interface Term

• http://iri.columbia.edu/~benno/sampleterm.pdf

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 statedLikewise:{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}

Subcategories are not subClasses

So carelessly translating existing conceptual organizations can get one into trouble

Domain and Range are inherited

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

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 RDF

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

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

OWL

rdf:Property owl:DatatypeProperty owl:ObjectProperty owl:AnnotationProperty

owl:FunctionalProperty owl:InverseFunctionalProperty owl:TransitiveProperty owl:SymmetricProperty

rdfs:seeAlso owl:imports

owl:ontology

Protégé

Tool for editing/displaying Ontologies

Different “tabs” display different perspectives

http://protege.stanford.edu/

Cast of RDF Characters II

Semantic

Layers

Query

Language


SPARQL Protégé

RDFS SeRQL

OWL Sesame


SWRL Jena

Query Language: SPARQL

• (quick reference at 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

Query Language: SeRQL

• Older than SPARQL

• Implemented on top of Sesame

• Currently more powerful than SPARQL, i.e. has nested queries

SeRQL DetailsCopied from on-line tutorial

• Syntax

• Select

• Construct

• Where

• From

SeRQL: basic syntax

{person} foo:worksFor {Company} rdf:type {foo:ITCompany}

SeRQL: multiple statements

{subj1} pred1 {obj1}; pred2 {obj2}

Or

{subj1} pred1 {obj1} , {subj1} pred2 {obj2}

SeRQL: short cuts

{subj1} pred1 {obj1,obj2,obj3}

(also implies obj1,obj2,obj3 are distinct)

SeRQL: Select

SELECT dataset, dlabel

FROM {dataset} rdf:type {iridl:dataset},

[{dataset} rdfs:label {dlabel}]

USING NAMESPACE

iridl = <http://iridl.ldeo.columbia.edu/ontologies/iridl.owl>

Output as table (XML)

SeRQL:Construct

CONSTRUCT {dataset} rdf:type {foo:LabelledDatasets}

FROM {dataset} rdf:type {iridl:dataset}; rdfs:label {dlabel}

USING NAMESPACE

iridl = <http://iridl.ldeo.columbia.edu/ontologies/iridl.owl>

Output as RDF (RDF/XML)

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/Querieshttp://iridl.ldeo.columbia.edu/ontologies/query2.pl?...

RDF Architecture

RDF

RDF RDF

RDF

RDF RDF

RDF

RDF RDF

RDF

RDF

RDF RDF

RDF

RDF RDF

Virtual (derived) RDF

queries queries queries

Data ServersOntologies

MMI

JPL

StandardsOrganizations

Start Point

RDF Crawler

RDFS SemanticsOwl SemanticsSWRL Rules

SeRQL CONSTRUCT

Search Queries

LocationCanonicalizer

TimeCanonicalizer

Sesame

Search Interface

bibliography

IRI RDF Architecture

Creating Virtual Triples from Semantic Layers

Semantic

Layers

Query

Language


SPARQL Protégé

RDFS SeRQL

OWL Sesame


SWRL Jena

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

Simple Knowledge Organization System (SKOS)

Schema for relating concepts

Simple Knowledge Oranization System (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

RDF Frameworks

Protégé API

Redland Bindings in many languages, supports several triple stores, some with context

Jena Java API, some cmd line utilities, supports inference layers

Sesame HTTP server, Java API, supports inference, version 2 alpha has context

SesameSAIL- 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

RDF No inference

RDFS RDFS inference

OWLIM Some OWL inference

Custom

Jena

Java framework

In-memory and persistent stores

Inference API

Topics/Issues

• OpenDAP and RDF: can we transport data semantics without fixing the entire schema?

• 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

RDF Cast of Characters

Semantic

Layers

Query

Language


SPARQL Protégé

RDFS SeRQL

OWL Sesame


SWRL Jena

using rdf/owl technologies for discovery and use metadata

Documents