Introduction to linked data and the semantic web

Linked data and its role in the semantic web Dave Reynolds, Epimorphics Ltd @der42

Roadmap image: Leo Oosterloo @ flickr.com What is linked data? Examples Modelling Access Strengths and weaknesses other topics

Linked data intro

Linked data ...
publishing data on the web ...
... to enable integration , linking and reuse across silos

Can’t we just publish data as files?
pdf
easy to read and publish 
Excel
allows further processing and analysis 
csv
processing without need for proprietary tools 
But ...
structure of data not explained
no connection between different data sets, silos
static and fixed – can’t retrieve just slices relevant to problem

Linked data
Apply the principles of the web to publication of data
The web :
is a global network of pages
each identified by a URL
fetching a URL gives a document
pages connected by links
open, anyone can say anything about anything else

Linked data
Apply the principles to the web to publication of data
The linked data web :
is a global network of things
each identified by a URI
fetching a URI gives a set of statements
things connected by typed links
open, anyone can say anything about anything else
Linked data is “data you can click on”
 

Example schools information http://education.data.gov.uk/id/school/401874

Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” “ Secondary” “ Cardiff” label phase district

Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” phase district http://statistics.data.gov.uk/id/local-authority-district/00PT “ Cardiff” label school:PhaseOfEducation_Secondary label

Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” phase district http://statistics.data.gov.uk/id/local-authority-district/00PT “ Cardiff” label school:PhaseOfEducation_Secondary http://data.ordnancesurvey.co.uk/id/7000000000025484 label contains ward extent contains parish GML: 310499.4 184176.6 310476.5 ...

Example schools information http://education.data.gov.uk/id/school/401874 “ Cardiff High School” phase district http://statistics.data.gov.uk/id/local-authority-district/00PT “ Cardiff” label school:PhaseOfEducation_Secondary http://data.ordnancesurvey.co.uk/id/7000000000025484 label contains ward extent contains parish GML: 310499.4 184176.6 310476.5 ... same as

Linked data principles
Use URIs as names for things
Use HTTP URIs so that people can look up those names
When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL)
Include links to other URIs, so that they can discover more things
Pattern of application of semantic web stack

Linked open data cloud: 2007 Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

Data.gov.uk – linked datasets and APIs

Data.gov.uk visualizations on top of linked data

Ordnance survey

Environment agency - data, API, visualizations

BBC – integration and site design

E-commerce and rich snippets Overstock.com Peek-cloppenburg.de

Internal use

Open?
Linked open data
=
linked data
+
open data

Modelling

Modelling Thing, entity, concept ... resource
resource being described
abstract concept
real world thing
data item, particular measurement
document
identify by URI
provide information making statements about those resources
identifier NOT a container c.f. UML
open schema
critical to open extensibility and integration
similar to Entity-Attribute-Value modelling

Modelling – RDF – Resource Description Framework Statement, triple, logical assertion Subject Predicate Object

Modelling – RDF Statement, triple, logical assertion Subject Predicate Object some school has a name/label some literal

Modelling – RDF Statement, triple, logical assertion Subject Predicate Object http://education.data.gov.uk/id/school/401874 has a name/label “ Cardiff High School”

Modelling – RDF Statement, triple, logical assertion Subject Predicate Object http://education.data.gov.uk/id/school/401874 http://www.w3.org/2000/01/rdf-schema#label “ Cardiff High School”

Modelling – RDF Statement, triple, logical assertion where school: = http://education.data.gov.uk/id/school/ rdfs: = http://www.w3.org/2000/01/rdf-schema# Subject Predicate Object school:401874 rdfs:label “ Cardiff High School”

Modelling – RDF Statement, triple, logical assertion Subject Predicate Object school:401874 rdfs:label “ Cardiff High School” school:401874 ont:districtAdministrative la: 00PT la: 00PT rdfs:label Cardiff

Modelling – RDF Statement, triple, logical assertion school:401874 “ Cardiff High School” ont:districtAdministrative la:00PT “ Cardiff” rdfs:label rdfs:label Subject Predicate Object school:401874 rdfs:label “ Cardiff High School” school:401874 ont:districtAdministrative la: 00PT la: 00PT rdfs:label “ Cardiff”

Modelling – RDF Statement, triple, logical assertion Subject Predicate Object school:401874 rdfs:label “ Cardiff High School” school:401874 ont:districtAdministrative la: 00PT la: 00PT rdfs:label “ Cardiff” la: 00PT rdfs:label “ Caerdydd”@cy

RDF Syntaxes
RDF/XML
normative
Turtle
more human readable/writable
being standardized
RDFa
embed in (X)HTML
[others omitted]

Modelling – RDF RDF/XML syntax <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:ont="http://education.data.gov.uk/def/school/" xmlns:la="http://statistics.data.gov.uk/id/local-authority-district/" xmlns:school="http://education.data.gov.uk/id/school/" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"> <rdf:Description rdf:about="http://education.data.gov.uk/id/school/401874"> <rdfs:label>Cardiff High School</rdfs:label> <ont:districtAdministrative> <rdf:Description rdf:about="http://statistics.data.gov.uk/id/local-authority-district/00PT"> <rdfs:label>Cardiff</rdfs:label> </rdf:Description> </ont:districtAdministrative> </rdf:Description> </rdf:RDF> Subject Predicate Object school:401874 rdfs:label “ Cardiff High School” school:401874 ont:districtAdministrative la: 00PT la: 00PT rdfs:label “ Cardiff”

Modelling – RDF Turtle syntax @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix school: <http://education.data.gov.uk/id/school/> . @prefix ont: <http://education.data.gov.uk/def/school/> . @prefix la: <http://statistics.data.gov.uk/id/local-authority-district/> . school:401874 rdfs:label "Cardiff High School"; ont:districtAdministrative la:00PT . la:00PT rdfs:label "Cardiff" . Subject Predicate Object school:401874 rdfs:label “ Cardiff High School” school:401874 ont:districtAdministrative la: 00PT la: 00PT rdfs:label “ Cardiff”

Modelling Vocabularies
so far no actual models, let alone semantics
want to define
types of thing : Class
what you can say about them : Property
encode definitions in more RDF and publish at the corresponding URIs
link from data to data model
reuse published vocabularies to enable integration
freely combine different vocabularies or new ones

Modelling – vocabularies Logical modelling
modelling the domain, not a particular data structure
what exists
what is asserted? what can you deduce from that?
not about constraints as such
monotonic, open world
controlled vocabulary taxonomy thesaurus ontology O ntology

Modelling – vocabularies
unfamiliar terminology but related to
information architecture and conceptual modelling
domain-driven design
... and yes knowledge representation

Modelling – RDFS RDF vocabulary description language
classes, types and type hierarchy
ont:School rdfs:Class rdf:type “ School” rdfs:label

ont:WelshEstablishment ont:School rdfs:Class rdf:type rdf:type rdfs:subClassOf “ School” rdfs:label

school:401874 ont:WelshEstablishment ont:WelshEstablishment ont:School rdfs:Class rdf:type rdf:type rdf:type rdfs:subClassOf “ School” rdfs:label

school:401874 ont:WelshEstablishment ont:WelshEstablishment ont:School rdfs:Class rdf:type rdf:type rdf:type rdfs:subClassOf school:401874 ont:WelshEstablishment ont:School rdf:type  “ School” rdfs:label “ School” rdfs:label

properties, property hierarchy
school:401874 person:JoeBloggs ont:staffAt ont:headOf rdf:Property ont:headOf rdf:type rdfs:subPropertyOf  school:401874 person:JoeBloggs ont:staffAt ont:headOf

class/property relations
domain
range
Already have power to do some vocabulary mapping
declare classes or properties from different vocabularies to be equivalent:
A rdfs:subClassOf B
B rdfs:subClassOf A

Modelling - OWL
richer modelling and semantics
axioms on properties
transitive, symmetric, inverseOf, ...
functional, inverse functional
equivalent property
axioms on classes
intersection, union, disjoint, equivalent
restrictions on classes
some value from, all values from, cardinality, has value, one of, keys
axioms on individuals
same as, different from, all different
imports

Modelling – OWL
supports much richer modelling
consistency checking of model
consistency checking of data
some surprises if used to schema languages
open world, no unique name assumption
can extend to closed world checking
inference
classification
inferred relationships

Modelling Spectrum of goals and styles
Lightweight vocabularies
Rich ontological models
simple modelling
just enough agreement to get useful work done
removing boundaries to enable information to be found and connected
global consistency not possible
a little semantics goes a long way
rich domain models
need expressivity
consistency is critical
make complex inferences you can rely on, across data you trust
knowledge is power

Modelling Ontology reuse
invest in complete ontology for a domain
rich but general model, may be modular inside
strong “ontological commitment”
e.g. medical ontologies
reuse small, common, vocabularies
FOAF, SIOC, Dublin Core, Org ...
pick and choose classes and properties you need
fill in a few missing links for your domain
generic reusable vocabularies
Data cube vocabulary

Accessing all this data
link following
HTTP GET, follow links, aggregate relevant statements
query
SPARQL

SPARQL
core idea is pattern matching
graph patterns with variables
any subgraph which matches yields row of bindings
syntax based on Turtle syntax for RDF
web API endpoints
lots of power
rdfs:label ont:districtAdministrative ?school [ ] “ Cardiff”
filters
optionals
named graphs
sub-queries
property chains
aggregation
federated query
update
construct

Accessing all this data
link following
HTTP GET, follow links, aggregate relevant statements
query
SPARQL
linked data API
RESTful API onto linked data resources
simple query, usable without RDF stack, web dev friendly
easy to layer visualizations and UIs on top
third parties
search engines and aggregators e.g. Sindice, sameAs.org

Semantic web layer cake

Strengths and weaknesses image: spcbrass @ flickr.com

Strengths
data integration
use of global identifiers (URIs)
composable – statements v. containers, schemaless
linking, vocabulary mapping
extensible, incremental, decentralized, resilient
no global ontology/schema to develop or maintain
freely add terms from other vocabularies
open world assumption
modelling and data entwined
link data to models, data in context
use same technology to share, manage extend models
supports inference and classification
rich access routes
web linking, download, query, web APIs

Weaknesses
complexity of the stack
alphabet soup – RDF, RDFS, OWL, SPARQL, RIF ..
unfamiliar “ontology”, “logical entailment”
lots of arcane details
RDF/XML syntax
performance of schema-less stores
optimization challenges
limited validation and constraints
cost of modelling,ontology development
no inbuilt notions of time, uncertainty
use the parts you need
tooling e.g. Linked Data API
core ideas not that complex
technology improving steadily
hybrid solutions
closed world checkers
ontology reuse
generic ontologies (data cube)
tools
model on top

Wrapping up image: erika g. @ flickr.com

Things we missed out
RDF nuances
blank nodes, containers and collections
named graphs
linked data nuances
URI for thing v. web page, content negotiation, httprange-14
URI architecture
OWL nuances
OWL species, serializations, lots of details
Other technologies in the stack
SPARQL update, rules (RIF), GRDDL, Powder, Geo SPARQL, RDB mapping, triple/quad stores
Embedding structured data in markup
RDFa, micro formats, micro data, schema.org and all that

Hot topics
Government linked data
identifiers to seed linked data
data publication
transparency, improving services, economic growth
structured data and search engines
rich snippets, structured results, SEO
search => question answering
user interfaces
visualization, exploration, exploiting linking
data as a service

Case study: Local government payments data model publish use

http://steve-dale.net	825
http://www.epimorphics.com	68
http://beta.publicsectorweb.com	25
http://feeds.feedburner.com	13
http://linkeddata.uriburner.com	5
http://localhost	3
http://twitter.com	2
https://twitter.com	1
http://translate.googleusercontent.com	1

Introduction to linked data and the semantic web

by Dave Reynolds on Jul 27, 2011

Accessibility

Categories

Tags

Upload Details

Usage Rights

9 Embeds 943

Statistics

Introduction to linked data and the semantic web — Presentation Transcript