an introduction to the semantic webbenno/talks/mbbsemanticsintro... · 2011-11-03 · an...

An Introduction to the Semantic Web

M. Benno BlumenthalInternational Research Institute for Climate

and SocietyColumbia University

2 November 2011

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)

Semantic Web Stack

T. Berners-Lee

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

Standard Metadata

Datasets

Standard Metadata Schema/Data Services

Many Data Communities

● Semantic walls● Exchange walls

Datasets

Standard Metadata Schema

Datasets

Super Schema

Datasets

Standard metadata schema

Super Schema: direct

Datasets

Standard metadata schema/data service

● 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

Datasets

Standard metadata schema

Datasets

RDFRDF

Datasets

RDFRDF

Datasets

Standard Metadata Schem

RDFRDF

RDF Data Model Exchange

Datasets

RDFRDF

Datasets

RDFRDF

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

RDFRDF

Virtual (derived) RDF

queries queries queries

Example: Search Interface

Search Interface

Datasets

Search Ontology

Dataset Ontology

Additional Semantics

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

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} 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

Search Interface Term

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

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 semanticsOWL – additional property/class semantics: more

sophisticated (ontological) relationshipsSWRL – rules for constructing virtual triples

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

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 nuancedtagging can proceed in absence of perfect

information

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}

RDF Architecture

RDFRDF

Virtual (derived) RDF

queries queries queries

Data Servers

Ontologies

StandardsOrganizations

Start Point

RDF/XML-Schema CrawlerXSLT/GRDDL ingest

XML Schema to OWL translationOwl SemanticsSWRL Rules

SeRQL CONSTRUCT

Search Queries

LocationCanonicalizer

TimeCanonicalizer

Sesame

Search Interface

bibliography

IRI RDF Architecture

Models, Crosswalks, and Objects

Structure of the RDF information that we are using to represent data objects in multiple frameworks (see full figure)

Semantic Crosswalk for metadata translation

Semantic Web

● Universal ids (URIs)

● Multiple partial representations adding to be a more complete picture

John Godfrey Saxe (1816-1887)

Semantic Web Stack

T. Berners-Lee

Define terms

● Attribute Ontology● Object Ontology● Term Ontology

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

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

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

Bi-direction crosswalk between the two is somewhat trivial, which means all my objects will have both

cfatt:standard_name

cfobj:standard_name

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

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

an introduction to the semantic webbenno/talks/mbbsemanticsintro... · 2011-11-03 · an...

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