An Introduction to the Semantic Webbenno/Talks/MBBSemanticsIntro... · 2011-11-03 · An...
Transcript of An Introduction to the Semantic Webbenno/Talks/MBBSemanticsIntro... · 2011-11-03 · An...
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An Introduction to the Semantic Web
M. Benno BlumenthalInternational Research Institute for Climate
and SocietyColumbia University
2 November 2011
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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)
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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
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Standard Metadata
Users
Datasets
Tools
Standard Metadata Schema/Data Services
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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
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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
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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
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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
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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
RDF
RDF
RDF
RDF
RDF
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Standard metadata schema
Tools
Users
Datasets
Standard Metadata Schema
RDF
RDFRDF
Tools
Users
Datasets
Standard Metadata Schema
RDF
RDFRDF
Tools
Users
Datasets
Standard Metadata Schem
RDF
RDFRDF
RDF Data Model Exchange
RDF
Tools
Users
Datasets
Standard Metadata Schema
RDF
RDFRDF
Tools
Users
Datasets
Standard Metadata Schema
RDF
RDFRDF
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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
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RDF Architecture
RDF
RDFRDF
RDF
RDFRDF
RDF
RDFRDF
RDF
RDF
RDFRDF
RDF
RDFRDF
Virtual (derived) RDF
queries queries queries
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Example: Search Interface
Search Interface
Users
Datasets
Search Ontology
Dataset Ontology
Additional Semantics
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Sample Tool: Faceted Searchhttp://iridl.ldeo.columbia.edu/ontologies/query2.pl?...
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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)
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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
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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
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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”
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Object Properties
{WOA} iridl:isContainerOf {Grid-1x1},
{Grid-1x1} iridl:isContainerOf {Monthly}
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WOA01 diagram
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Standard Properties
{WOA} dcterm:hasPart {Grid-1x1},{Grid-1x1} dcterm:hasPart {MONTHLY}
Alternatively
{WOA} iridl:isContainerOf {Grid-1x1},{iridl:isContainerOf} rdfs:subPropertyOf
{dcterm:hasPart}
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{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
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Search Interface Term
• http://iri.columbia.edu/~benno/sampleterm.pdf
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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
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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
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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
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Faceted Search Explicated
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Search Interface
● Items (datasets/maps)
● Terms● Facets● Taxa
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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}
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RDF Architecture
RDF
RDFRDF
RDF
RDFRDF
RDF
RDFRDF
RDF
RDF
RDFRDF
RDF
RDFRDF
Virtual (derived) RDF
queries queries queries
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Data Servers
Ontologies
MMI
JPL
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
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Models, Crosswalks, and Objects
Structure of the RDF information that we are using to represent data objects in multiple frameworks (see full figure)
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Semantic Crosswalk for metadata translation
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Semantic Web
● Universal ids (URIs)
● Multiple partial representations adding to be a more complete picture
John Godfrey Saxe (1816-1887)
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Define terms
● Attribute Ontology● Object Ontology● Term Ontology
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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
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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
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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
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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
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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
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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