lecture linked data cloud & sparql

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COMP3725Knowledge Enriched Information

Systems

Lecture 11-12: Linked Data & SPARQL

Dhavalkumar Thakker (Dhaval)School of Computing, University of Leeds

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Reading & Reflections

Bizer, et al. Linked Data – The Story so far• What is Linked Data?

– Is it same as Web of Data?

• What excited you most about linked data while reading this article? OR what did you find most interesting?

• Is Linked Data happening in real life? Have you seen this anywhere?

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Outline

• What is Linked Data?• Why Linked Data?• How to publish as part of Linked Data

– Linked Data Principles– Finding existing sources– Possible software architectures– Query Language: SPARQL

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Web of Documents

http://news.bbc.co.uk/1/hi/world/americas/us_elections_2008/7709978.stm

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About:• United States• Barack Obama• Presidential Election (Past)• Some relevance to currently

held• Democrats & Republicans• Winner & Looser• Chicago• Etc..

Web of Documents

THINGSAbout:• Location, Event, Places,

Persons, Groups, Abstract concepts (winning, losing)

http://news.bbc.co.uk/1/hi/world/americas/us_elections_2008/7709978.stm

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..people can parse documents and extract meaning

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The web of documents

• Analogy– Global file system

• Designed for– Human consumption

• Primary objects– documents

• Links between– documents (or sub-parts of)

• Semantics– implicit

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The web of documents: Issues

• Web of Documents but primarily About Data– But the connection is implicit

• Integration & Querying– Show me all the news stories by US Presidents

coming from Chicago?

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Semantic Web

• We need to help machines to understand the web..so machines can help us to understand things.

• If machines have access to the data about things (i.e. knowledge) then they can do better job while processing documents

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Linked Data

An introduction to Linked Data- Tim Heath, Talis

Linking Things

Thing

relationshiplinks

Thing

Thing

Thing

Thing

Thing Thing

Thing

Thing

Thing

relationshiplinks

relationshiplinks

relationshiplinks

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Linked Data…

• …. is about creating global database of linked things

• …refers to a set of best practices for publishing and interlinking data on the Web…

• ….is a method of publishing data [on the Web], so that it can be interlinked and become more useful.

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The Web of Linked Data

• Analogy– a global database

• Designed for– machines first, Humans later

• Primary objects– things (or descriptions of things)

• Links between– things

• Semantics– explicit

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Linked Data: Technologies

• Pre-requisite– URIs– HTTPs– RDF– (RDFS/OWL)

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Linked Data Technologies : URIs

• Like URLs but not just for Web pages– For things (cars, people, places, organisations,

coursework, etc.)• “A Uniform Resource Identifier (URI) provides a

simple and extensible means for identifying a resource.” -- RFC 3986

• Many different schemes – http://, ftp://, mailto:• Examples:

http://imash.leeds.ac.uk/ontologies/foaf/dhaval/me.rdfhttp://dbpedia.org/resource/University_of_Leeds

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HTTP

• Data access mechanism between web browsers (client) and servers

• HTTP messages consists of requests from client to servers and responses from servers to clients

• HTTP request/response methods: GET, POST, etc.

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RDF

• Data format to describe things and their interrelations

• is based on triples• Subject, predicate, object• <The sky> <has the colour> <blue>

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RDF

dt:dhaval foaf:Personrdf:type

Dhaval Thakkerfoaf:name

dbpedia:Leedsfoaf:based_near

Prefixesdt: < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/ me.rdf#>rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>foaf: <http://xmlns.com/foaf/0.1/>dbpedia: <http://dbpedia.org/resource/>

From my profile in RDF

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Data Merging with RDF




dbp-prop:population

dbp-prop: is part of dbpedia:West_Yorkshire

751,500

dbpedia:Leeds

From Dbpedia


Prefixesdt: < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/ me.rdf#>rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>foaf: <http://xmlns.com/foaf/0.1/>dbpedia: http://dbpedia.org/resource/dbp-prop: <http://dbpedia.org/ontology/>

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Data Merging with RDF





dbp-prop:population


751,500

dbpedia:Leeds

From Dbpedia


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Linked Data Principles

• Use URIs as names for things– anything, not just documents

• Use HTTP URIs– globally unique names, distributed ownership– allows people to look up those names

• Provide useful information in RDF– when someone looks up a URI

• Include RDF links to other URIs– to enable discovery of related information

Tim Berners-Lee 2007http://www.w3.org/DesignIssues/LinkedData.html

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Provide useful information in RDF

dt:me foaf:Personrdf:type



Prefixesdt: < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/ me.rdf#>rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>foaf: <http://xmlns.com/foaf/0.1/>dbpedia: <http://dbpedia.org/resource/>


http://imash.leeds.ac.uk/ontologies/foaf/dhaval/ me.rdf#me

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RDF is Data Model, Not Serialisation Format

• RDF Serialisation Formats : RDF/XML, Turtle, N-Triples

– RDF/XML

<rdf:RDF

xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-ns#

xmlns:foaf=http://xmlns.com/foaf/0.1 />

<foaf:Person rdf:ID="me">

<foaf:name>Dhavalkumar Thakker</foaf:name>

<foaf:title>Dr</foaf:title>

<foaf:based_near rdf:resource="http://dbpedia.org/resource/Leeds"/>

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– Turtle

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix foaf: <http://xmlns.com/foaf/0.1/> . @prefix dt: < http://imash.leeds.ac.uk/ontologies/foaf/dhaval/me.rdf#>

dt:me rdf:type foaf:Person ;

foaf:name “Dhavalkumar Thakker" ; foaf:title “Dr" .

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– N-Triples

< http://imash.leeds.ac.uk/ontologies/foaf/dhaval/me.rdf#me> <xmlns:foaf=http://xmlns.com/foaf/0.1#name> “Dhavalkumar Thakker”.

< http://imash.leeds.ac.uk/ontologies/foaf/dhaval/me.rdf#me>

< http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <xmlns:foaf=http://xmlns.com/foaf/0.1#Person>.

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Including Links to other Things: Relationship Links

• Relationship Links point at related things in other data sources, for instance, other people, places or genes.

• For example, relationship links enable people to point to background information about the place they live, or to bibliographic data about the publications they have written.

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Including Links to other Things: Relationship Links





dbp-prop:population


751,500

dbpedia:Leeds

From Dbpedia


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Including Links to other Things: Identity Links

• Different URIs may refer to the same object

<URI1> in one dataset

is same as

<URI2> defined somewhere else

<http://dbpedia.org/resource/Kirkgate_Markets> <owl:sameAs> <http://rdf.freebase.com/ns/guid.9202a8c04000641f8000000000c5f680>

• Such a need exists due to:– Different opinions. – Traceability. – No central points of failure.

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Including Links to other Things: Vocabulary Links

• Reusing existing Vocabularies to further specify yours

<htttp://mydomain.co.uk/myvocab/enterprise#SmallMediumEnterprise>

rdfs:subClassOf

<http://dbpedia.org/ontology/Company>;

rdfs:subClassOf

<http://umbel.org/umbel/sc/Business> ;

rdfs:subClassOf

<http://rdf.freebase.com/ns/m/0qb7t>.

http://dbpedia.org/ontology/Company

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Linked Data Principles: Summary





RDF serialisation formats: RDF/XML, N-Triples & Turtle

Include Links:Relationship, Vocabulary & Identity Links

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Finding Existing Datasets or Vocabularies

• All of the scenarios about including links to other things assume some sort of knowledge of existing vocabularies/datasets

• Where to Find such datasets?• How to Find such datasets?

– Two steps:• Find datasets/vocabularies that contain certain

Things or Concepts• Once found, how to inspect the coverage and

suitability

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Where to Find: Web of Data

• A significant number of individuals and organisations have adopted Linked Data as a way to publish their data

• The result is a global data space we call the Web of Data

• The Web of Data forms a giant global graph consisting of billions of RDF triples from numerous sources covering all sorts of topics

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Web of Data

http://richard.cyganiak.de/2007/10/lod/

http://richard.cyganiak.de/2007/10/lod/lod-datasets_2011-09-19_colored.html

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Statistics about Web of Data (2011)Domain

Number of datasets

Triples (Out-)Links %

Media 25 1,841,852,061 50,440,705 10.01 %

Geographic 31 6,145,532,484 35,812,328 7.11 %

Government 49 13,315,009,400 19,343,519 3.84 %

Publications 87 2,950,720,693 139,925,218 27.76 %

Cross-domain 41 4,184,635,715 63,183,065 12.54 %

Life sciences 41 3,036,336,004 191,844,090 38.06 %

User-generated content

20 134,127,413 3,449,143 0.68 %

295 31,634,213,770 503,998,829

More statistics from: http://www4.wiwiss.fu-berlin.de/lodcloud/state/

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Step1: Finding existing datasets and vocabularies: publishing sites-> Data Hub

Available from: http://datahub.io/

http://datahub.io/

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Step 1: Finding existing datasets and vocabularies: search engines-> Sindice

Available from: http://sindice.com/

http://sindice.com/

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Step 1: Finding existing datasets and vocabularies: search engines-> Falcon

Available from: http://ws.nju.edu.cn/falcons/conceptsearch/index.jsp

http://ws.nju.edu.cn/falcons/conceptsearch/index.jsp

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Finding existing datasets and vocabularies: search engines-> Watson

Available from: http://kmi-web05.open.ac.uk/WatsonWUI/

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Finding existing datasets and vocabularies: search engines-> Swoogle

Available from: http://swoogle.umbc.edu/

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Step 1: Finding existing datasets and vocabularies: search engines-> SWSE

Available from: http://swse.deri.org/

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Step 2: Once found, how to inspect further for coverage, suitability

• Linked Data sources usually provides SPARQL endpoint for their dataset(s)

• SPARQL endpoint is an end point to dataset(s) that can receive query, and return results

• If you have used MySQL, you might be familiar with PhPMyAdmin– SPARQL endpoint are in similar in nature and

its functionality

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Web of Data

http://richard.cyganiak.de/2007/10/lod/

http://richard.cyganiak.de/2007/10/lod/lod-datasets_2011-09-19_colored.html

http://en.wikipedia.org/wiki/Calgary

http://dbpedia.org/resource/Calgary

dbpedia:native_name Calgary”;

dbpedia:altitude “1048”;

dbpedia:population_city “988193”;

dbpedia:population_metro “1079310”;

mayor_name

dbpedia:Dave_Bronconnier ;

governing_body

dbpedia:Calgary_City_Council;

...

Dbpedia: Extracting Infobox

Dbpedia: SPARQL EndpointWeb address: dbpedia.org/sparql

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SPARQL

• Query Language for RDF– Based on RDF Data Model

• Possible to write complex joins of disperate datasets

• Implemented by all major RDF databases

See more: http://www.w3.org/TR/rdf-sparql-query/

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Structure of a SPARQL Query

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#prefix declaration

prefix dbp-ont: <http://dbpedia.org/ontology/>

#result clause

SELECT *

#dataset definition

FROM <http://dbpedia.org>

#query pattern

WHERE {

dbp-ont:Person ?p ?o.

}

SELECT query: Find everything about Concept of “Person” as in Dbpedia

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#prefix declaration


#result clause

SELECT *

#dataset definition


#query pattern

WHERE {

dbp-ont:Person ?p ?o.

}

SELECT query: Find everything about Concept of “Person” as in Dbpedia

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#prefix declaration


Prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>

#result clause

SELECT ?o

#dataset definition


#query pattern

WHERE {

dbp-ont:Person rdfs:subClassOf ?o.

}

SELECT query: Find superclasses of Concept of “Person” as in Dbpedia

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#prefix declaration


Prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>

#result clause

SELECT ?s

#dataset definition


#query pattern

WHERE {

?s rdf:type dbp-ont:Person .

}

SELECT query: Find all persons in Dbpedia

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#prefix declaration



#result clause

SELECT ?s

#dataset definition


#query pattern

WHERE {


?s rdf:type dbp-ont:Astronaut.

}

SELECT query: Find specific types of persons in Dbpedia

Some one who is Person & Astronaut

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#prefix declaration



#result clause

SELECT ?s

#dataset definition


#query pattern

WHERE {



?s dbp-ont:status "Retired"@en.

}

SELECT query: Find specific types of persons in Dbpedia

Some one who is Person & Astronaut& Retired

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#prefix declaration



#result clause

SELECT ?s

#dataset definition


#query pattern

WHERE {




}

LIMIT 10

SELECT query: Find 10 of this, LIMIT

Some one who is Person & Astronaut& Retired

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#prefix declaration



#result clause

SELECT *

#dataset definition


#query pattern

WHERE {




?s dbp-ont:birthDate ?date

} ORDER BY ?date,

LIMIT 10

SELECT query: Find 10 of this and order it by date: ORDER BY

Some one who is Person & Astronaut& Retired & youngest first

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Mathematical operations & Filtering results• Find me all landlocked countries with a population greater

than 15 million , with the highest population country first

PREFIX type: <http://dbpedia.org/class/yago/>

PREFIX prop: <http://dbpedia.org/property/>

SELECT ?country_name ?population

WHERE

{ ?country a type:LandlockedCountries .

?country rdfs:label ?country_name .

?country prop:populationEstimate ?population .

FILTER (?population > 15000000 && langMatches(lang(?country_name), "EN")) . }

ORDER BY DESC(?population)

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ASK query: Is India a Landlocked country?

• Is India a Landlocked country?• ASK query:

PREFIX yago: <http://dbpedia.org/class/yago/>

PREFIX prop: <http://dbpedia.org/property/>

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>

ASK

{ <http://dbpedia.org/resource/India> rdf:type yago:LandlockedCountries.}

Replace with Afghanistan

DO NOT HAVE TO SPECIFY “WHERE”

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Exercise: Write a SPARQL query

• Write a SPARQL query to retrieve all the bands that are of genre rock bands from Republic of Ireland.

Prefix dbpedia: <http://dbpedia.org/resource/>


Prefix dbp-onto: <http://dbpedia.org/ontology/>

Use following classes or properties

dbp-onto:Band, dbp-onto : genre. dbpedia:Rock_music, dbpedia:Republic_of_Ireland, dbp-ont:hometown

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Exercise: Write a SPARQL query

• Write a SPARQL query to retrieve all the bands that are of genre rock bands from Republic of Ireland.

Prefix dbpedia: <http://dbpedia.org/resource/>


Prefix dbp-onto: <http://dbpedia.org/ontology/>

Select * where {

?s rdf:type dbp-onto:Band.

?s dbp-onto:genre dbpedia:Rock_music.

?s dbp-onto:hometown dbpedia:Republic_of_Ireland

}

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Summary: Finding existing datasets/vocabularies

• Use of search engines to find a dataset • Use of SPARQL endpoints to inspect the

dataset further• SPARQL queries

– SELECT query for selecting a set of results to display

– ASK query to ask a specific question about something

– Variations in terms of LIMIT, ORDER BY

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Publishing Linked Data: Software Architecture Patterns

• Follow linked data principles– They are good practice principles NOT norms

or rules

• The software architecture needs to support such way of publication– Existing architectures using structured or

unstructured data– doing it from scratch – publishing linked data – different from when working with existing

applications and infrastructure already in place

http://www.w3.org/DesignIssues/LinkedData.html

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Architecture scenarios

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Type of data

• Structured data– Database tables– XML documents

• Unstructured data– Textual documents

• News stories, reports, textual descriptions – as textual files

Name Address Post code Author of

A ---- ------- Book B

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Query-able Structured Data to Linked Data

• Example: A movie business that has movie database in a relational database

• published relatively easily as Linked Data through the use of relational database to RDF wrappers.

• Maps database schemas to RDF schemas• Wrappers

– Virtuoso RDF Views – Triplify

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Static Structured Data to Linked Data

• A UK government department that has performance data of each department in excel sheets

• must undergo a conversion process that outputs static RDF files or loads converted data directly into an RDF store.

• RDFizing tools – http://www.w3.org/wiki/ConverterToRdf– Tools to convert data from various format to

RDF

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RDF store

• Also called “triple store” or “semantic repository” • They are engines similar to the DBMS- they allow

for storage, querying, and management of structured data. Major differences:– they use ontologies as semantic schemata. This allows

them to automatically reason about the data.– they work with flexible and generic physical data

models (e.g. graphs). This allows them to easily interpret and adopt "on the fly" new ontologies or metadata schemata.

• Available RDF stores: OWLIM, Allegrograph, Virtuoso, Sesame, Jena TDB

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From Text Documents to Linked Data

• Example: News publisher with a corpus of news stories produced in the last month

• it is possible to pass these documents through a Linked Data entity extractor such as Open Calais(http://www.opencalais.com/), or DBpedia Spotlight(http://dbpedia-spotlight.github.com/demo/index.html) which annotate documents with the Linked Data URIs of entities referenced in the documents.

http://www.opencalais.com/

http://dbpedia-spotlight.github.com/demo/index.html

http://dbpedia-spotlight.github.com/demo/index.html

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From Text Documents to Linked Data

• Publishing these annotations together with the documents – increases the discoverability of the documents – enables applications to use the referenced Linked Data

sources as background knowledge to display complementary information on web pages

– or to enhance information retrieval tasks, for instance, offer faceted browsing instead of simple full-text search.

• Applications like this to be presented in next lecture(s)

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Summary

• Linked Data is a way of publishing and interlinking structured data on the web

• Linked Data principles to follow to create such data

• How to find existing datasets: Web of Data• How to query existing datasets: SPARQL• Possible software architecture patterns

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Next Lecture

• Consuming Linked Data– Linked Data Applications

• What datasets they use from Web of Data• What software architecture they follow

– Benefits• Integration – for organisations• Browsing and interaction – for users

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References

• Tom Heath, An Introduction to Linked Data, Linked Data Tutorial, Austin, Texas, 2009.

• Raimond et al., A skim-read introduction to linked data

• Tom Heath, Christian Bizer: Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web, Morgan & Claypool Publishers 2011

• Cambridge Semantics, SPARQL by example

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TED talk from Tim Berners Lee on Linked Data

• http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html

http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html

http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html