Exploring Linked Open Data

Exploration, Visualization and
Querying of
Linked Open Data sources
2nd Keystone Training School - Keyword Search in Big Linked Data
Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS), University of Santiago de
Compostela (USC), Spain.
Laura Po
Department of Engineering «Enzo Ferrari»
University of Modena and Reggio Emilia
Italy

Outline
• Introduction to Linked Open Data
• Searching for LOD datasets
• Exploring a dataset
• Visualization tools
• Querying a SPARQL Endpoint
MORNING SESSION
AFTERNOON HANDS-ON SESSION

Searching for LOD datasets
• Portals that collects datasets
• Datahub – a portal that collect datasets
• DataPortals.org- a portal that maintains a list of open data portals in the world
• International or national open data portals
• EU Open Data Portal is the single point of access to a wide range of data held by EU public
administrations at all levels of government, agencies and other bodies, that allows access in all 24 EU
official languages
• European Union Open Data portal – the Open Data portal for the European Commission and other
institutions of the European Union.
• Popular Datasets
• Wikidata - a collaboratively-created linked dataset that acts as central storage for the structured data
of its Wikimedia sister projects
• DBpedia – a dataset containing extracted data from Wikipedia; it contains about 3.4 million concepts
described by 1 billion triples, including abstracts in 11 different languages
• GeoNames provides RDF descriptions of more than 7,500,000 geographical features worldwide.
• FOAF – a dataset describing persons, their properties and relationships

DataHub
• Datahub collects more
than 10.000 datasets
• It is a data management
platform from the Open
Knowledge Foundation,
based on the CKAN data
management system.
• CKAN is a tool for
managing and publishing
collections of data. It is
used by national and local
governments, research
institutions, and other
organisations which
collect a lot of data.

Search on Datahub
• To find datasets, type any combination of search words (e.g. “health”,
“transport”, etc) in the search box on any page. CKAN displays the
first page of results for your search. You can:
• View more pages of results
• Repeat the search, altering some terms
• Restrict the search to datasets with particular tags, data formats, etc using the
filters in the left-hand column
• If datasets are tagged by geographical area, it is also possible to run
CKAN with an extension which allows searching and filtering of
datasets by selecting an area on a map.

Exploring datasets
• When you have found a dataset you are interested and selected it, CKAN will
display the dataset page. This includes
• The name, description, and other information about the dataset
• Links to and brief descriptions of each of the resources
• The resource descriptions link to a dedicated page for each resource. This
resource page includes information about the resource, and enables it to be
downloaded.
• Many types of resource can also be previewed directly on the resource page. .CSV
and .XLS spreadsheets are previewed in a grid view, with map and graph views
also available if the data is suitable. The resource page will also preview resources
if they are common image types, PDF, or HTML.
• The dataset page also has two other tabs:
• Activity stream – see the history of recent changes to the dataset
• Related items – see any links to web pages related to this dataset, or add your own links.

Exercise 1 – Datahub
• Find the dbpedia dataset in Datahub
• Look at the possible way the dataset can be accessed
• Find datasets about Santiago
• Can you find some interesting data source?
• How much information are given on these data?
• How many formats and access points to the datasets are available?

International or national open data portals
• The European Data Portal harvests the metadata of Public Sector
Information available on public data portals across European
countries. Information regarding the provision of data and the
benefits of re-using data is also included.
• Public sector information is information held by the public sector. The Directive
on the re-use of public sector information provides a common legal framework
for a European market for government-held data.

Improving the accessibility and Value of OGD
• The strategic objective of the European Data Portal is to improve
accessibility and increase the value of Open Governament Data:
• Accessibility: How to access this information? Where to find it? How
to make it available in the first place? In domains, across domains,
across countries? In what language?
• Value: For what purpose and what economic gain? Societal gain?
Democratic gain? In what format? What is the critical mass?
• The European Data Portal addresses the whole data value chain: from
data publishing to data re-use.

A checklist for using Open Data
Having access to data is a first step. Data is not an end in itself. Data can be used in different ways
and for different purposes. Data can also be available with different licences, formats and quality.
• Define your purpose: You might specify a topic or a service or an application of interest
• Identify data labels: Filter the data labels and metadata
• Check Openness: Take a look at the licence information. Make sure a licence is available which
allows you to make use of the data in the way that you intend (e.g. that commercial re-use is
allowed if you develop a commercial application).
After you have decided that a specific data set is exactly what you are looking for
• Select the useful file format - you are probably able to choose to download the datasets in
different file formats. Depending on your computer skills, you can choose the file type that is
most appropriate. Most datasets are available in an open file format.
• Check the data quality – check the last date the file was modified, check whether information
about the time period is provided.

A checklist for using Open Data
Having access to data is a first step. Data is not an end in itself. Data can be used in different ways
and for different purposes. Data can also be available with different licences, formats and quality.
• Define your purpose: You might specify a topic or a service or an application of interest
• Identify data labels: Filter the data labels and metadata
• Check Openness: Take a look at the licence information. Make sure a licence is available which
allows you to make use of the data in the way that you intend (e.g. that commercial re-use is
allowed if you develop a commercial application).
After you have decided that a specific data set is exactly what you are looking for
• Select the useful file format - you are probably able to choose to download the datasets in
different file formats. Depending on your computer skills, you can choose the file type that is
most appropriate. Most datasets are available in an open file format.
• Check the data quality – check the last date the file was modified, check whether information
about the time period is provided.
• Form
• how has the data been processed?
• is it in raw or summary form?
• how will its form affect your
analysis/product/application?
• what syntactic (language) and semantic (meaning)
transformations will you need to make?
• is this compatible with other datasets you have?
• Quality
• how current is the data?
• how regularly is it updated?
• do you understand all the fields and their context?
• for how long will it be published? what is the
commitment by the publisher?
• what do you know about the accuracy of the data?
• how are missing data handled?

Exercise 2 – EDP
• Choose one category on the EDP and
find datasets describing one specific
topic (for example in the category of
transport the topic could be cycling
routes)
General analysis
• How many datasets are available?
• What are the main datasets?
Local analysis
• How many information about your
country are available?
• How many formats and access points
to the datasets are available?

National open data portals
• Notable examples of Open Data portals maintained by public administrations in Europe
are:
• France
• opendata.paris.fr
• www.data.gouv.fr
• Italy
• www.dati.piemonte.it
• www.dati.gov.it
• Netherland
• www.data.overheid.nl
• UK
• data.gov.uk

Open data websites in Europe
International
publicdata.eu
data.un.org
data.worldbank.org
EU Member States
data.gov.be
opendata.government.bg
opendata.cz
portal.opendata.dk
govdata.de
opendata.ee
data.gov.ie
data.gov.gr
datos.gob.es
data.gouv.fr
data.gov.hr
dati.gov.it
data.gov.cy
opendata.gov.lt
data.public.lu
data.gov.mt
data.overheid.nl
data.gv.at
danepubliczne.gov.pl
dados.gov.pt
data.gov.ro
nio.gov.si/nio/
data.gov.sk
avoindata.fi
oppnadata.se
data.gov.uk

Exercise 3 – Find national Open Data portals
• Find the government open data portal from your member state
…some suggestions:
• Search in the list of EU member states open data websites
• Search in DataCatalogs
• Search in Google
• How many portals that collect open data are available in your member
state?
• How many datasets are collected in the portals?
• Have you already used some of these data?

Ranking of the national open data datasets
Global Open Data Index – is an annual report to measure the state of open
government data around the world. The goal is to provide a civil society audit of
how governments actually publish data - with input and review from citizens and
organisations around the world.
• Topical experts review datasets from different country, establish a baseline and track changes and
trends in the open data world over time as the field evolves.
Open Data Barometer - aims to uncover the impact of open data initiatives around
the world. It analyses global trends, and provides comparative data on countries
and regions via an in-depth methodology combining contextual data, technical
assessments and secondary indicators to explore multiple dimensions of open data
readiness, implementation and impact.
• This is the second edition of the Open Data Barometer. The Open Data Barometer forms part of
the World Wide Web Foundation’s work on common assessment methods for open data.

Exercise 4 – Find the ranking
• By using the information on Open Data Barometer and Global Open
Data index, find the ranking for the Open Data Portals/Initiatives of
your member state
Some questions
• Does the National Maps have an open lincense?
• Is the Government Budget publicly available?
• How are the Government policies of your country compared to the
mean of Europe and Central Asia?
• How high/low is the impact of open data in your country?

Exploring the Web of Data
• Linked Data Browsers - generic Linked Data browsers which allow users to start browsing in one data source
and then navigate along links into related data sources
• OpenLink Data Explorer a Web browser extension, and a server-side component of the OpenLink Ajax
Toolkit.
• Marbles tabular Linked Data browser supporting Fresnel.
• Sigma, Live views on the Web of Data
• Quick & Dirty RDF Browser Simple RDF browser. Useful for checking RDF or RDFa says what you intended.
• Graphity Client Generic Linked Data browser and platform for building declarative SPARQL triplestore-
backed Web applications. Apache license.
• Linked Data mashups
• Revyu by Tom Heath. Uses Linked Data from DBpedia to augment reviews, for instance with information
about a director for a film.
• DBpedia Mobile by Christian Becker and Chris Bizer. Combines Linked Data from DBpedia, the flickr
wrapper, and Revyu.
• Music Mashup by Yves Raimond. Combines Linked Data from various music related data sources.
• Linked Data Search engines - crawl the Web of Data by following links between data sources and provide
expressive query capabilities over aggregated data

Linked Data Browsers
http://marbles.sourceforge.net
Marbles

Linked Data Mashup
http://revyu.com
Revyu.com

Linked Data Mashup
DBPedia
Mobile Pictures from revyu.com
http://wiki.dbpedia.org/DBPediaMobile

Linked Data Mashup
http://sig.ma
SIGMA

Linked Data Search Engines
http://data.nytimes.com/schools/schools.html
NYTimes

Some Application Scenarios
BBC Music

Some Application Scenarios
LinkedGeoData.org
LinkedGeoData adds a spatial dimension to the Web of Data /
Semantic Web.
LinkedGeoData uses the information collected by the
OpenStreetMap project and makes it available as an
RDF knowledge base according to the Linked Data principles.
It interlinks this data with other knowledge bases in the Linking
Open Data initiative.

Exercise 5 – OpenLink exploration - Facebook to Linked
Data Transformation Examples
• Install the OpenLink Data Explorer (ODE) extension for your browser (currently available for Firefox, Safari,
Chrome, Opera, and Internet Explorer)
• This extension will allow you to explore the raw data and entity relationships that underlay the Web resources it
processes.
• Select your Facebook Profile Page (or another person Facebook Profile page)
• Right-Click (or Ctrl-Click on Mac) on the page and then click on "View Page Description" to obtain a
descriptions of the resources available on the linked page
• A description of the resource Metadata available on the page is displayed.
• More example: If you what to perform some other researches look at the example page

Visualization of Linked Data
• Why is it important?
• Actually the consumption of LOD is restricted to the Semantic Web
community
• Visual tools that provide a coherent and legible picture of the data
allow also non-technical audience
• to obtain a good understanding of the data structure,
• and to compose query,
• identify links between resources
• and intuitively discover new pieces of information

What is visualization
• The visualization of information
• Goals:
• Effective communication of information
• Clarity
• Integrity (all the information)
• Stimulate viewer engagment
• Focus on effectiveness

Why is visualization important?
• With lage datasets we need an efficint way to understand a vast
amount of data
• The human visual system is the highest- bandwith channel to the
human brain

Why visualize data instead of provide statistic
analysis?
http://en.wikipedia.org/wiki/Anscombe's_quartet
• Anscombe's
quartet of datasets
having similar
statistical
properties but
appearing very
different when
plotted

Example of the Linked Data visualization process

Heatmap visualization of Beatles releases

Visualization, exploration and query tools

LOD live
LodLive project provides a demonstration
of the use of Linked Data standards (RDF,
SPARQL) to browse RDF resources. The
application aims to spread linked data
principles using a simple and friendly
interface with reusable techniques.
http://en.lodlive.it/
http://en.lodlive.it/?http://dbpedia.org/resource/Jules_Verne

Exercise 6 - LODLive
By using LodLive online to explore dbpedia resources, search for Serena
Williams http://en.lodlive.it/
- who is she?
- where does she live?
- where does she is list as a champion actually (find and explore the
"currentChampion" relation)?
- find the statistics and records associated to her, navigate to the wikipedia
page, and discover what is the total win rate of Serena in Single disciplines

Visualbox
Visualbox allows you to create visualizations based on Linked Open Data. The
goal of Visualbox is to facilitate the creation of visualization without the need
to learn Javascript libraries. You do need to know a bit of SPARQL and some
notions of HTML though.
Visualbox is a simplified version of LODSPeaKr, a framework to create Linked
Data-based applications.
http://orion.tw.rpi.edu/~agraves/mozfest/index.html

VisualBox – some example
http://orion.tw.rpi.edu/~agraves/mozfest/action
http://orion.tw.rpi.edu/~agraves/mozfest/firesock_test

LODEXIt is a tool for producing a representative summary of a Linked open Data (LOD)
source starting from scratch, thus supporting users in exploring and
understanding the contents of a dataset.
LODeX extracts statistical indexes that uses to build the representative summary,
by quering the SPARQL endpoint of a LOD source.
Two online versions:
• LODeX 2.0 (http://www.dbgroup.unimo.it/lodex2 ) includes the possibility to
compose visual queries by selecting objects from the representative summary
of a LOD source
• LODeX Cluster (http://www.dbgroup.unimo.it/lodex2/testCluster ) provides a
more concise schema for huge datasets

LODeX Architecture
Two main modules
• Extraction & Summarization
– Index Extraction (IE)
– Post Processing (PP)
LOD Cloud
SPARQL
Queries
LODeX
Post-
processing
Statistical
Indexes
LODeX
Indexes
Extraction
Endpoint
URLs
Schema
Summary
NoSQL
SPARQL
Queries
Schema
Summary
Query
Orchestrator
Schema
Summary
Visualizzation
Basic
QueryResults
• Visualization & Querying
– Schema Summary
Visualization
– Query Orchestrator

The Schema Summary is a pseudograph composed by:
C - Classes (nodes)
P - Properties (edges)
And additional elements and function:
A - Attributes associated to each class
Each attribute represent the existence of a Datatype property from the
instances of the class
σ 𝒍 -labels
l – labeling function
count - count function
The Schema Summary is inferred by the distribution of the
instances of a dataset
The Schema summary

A running example
ex:Sector foaf:Organization
owl:Class
ex:sector
“sector”
rdf:type rdf:type
rdf:Propertyrdf:type
owl:ObjectProperty
rdf:type
sector1 organization1ex:sector
dc:title
“Energy”
Extensional
Classes
Extensional
Knowledge
Intensional
Knowledge
ex:activity
“Village electrification
in the Pacific”
organization2 “+41331231”
rdfs:label
rdfs:label
rdfs:domain
rdf:type
ex:sector
rdf:type rdf:type
dbpedia:fax
person1
foaf:Person
ex:activity
“Paolo”
rdf:type
ex:ceo
rdf:type foaf:firstName
foaf:lastName “Rossi”
The information contained in the Intensional knowledge can be incomplete
or absent

Indexes needed to generate a Schema Summary
These indexes belong to extensional group of the Statistical Indexes [2]:
SC (Subject Class) contains the pairs (p,c) where p is an object property and c
is its domain class.
SCl (Subject Class to literal) contains the pairs (p,c) where p is a datatype
property and c is its domain class.
OC (Object Class) contains the pairs (p,c) where p is an object property and c is
its range class.
sector1 ex:sector organization1
dc:title
“Energy” organization2
Extensional
Classes
Extensional
Knowledge
in thePacific”
“+41331231”
ex:sector
rdf:type rdf:type
dbpedia:fax
person1
foaf:Person
ex:activity
“Paolo”
rdf:type
ex:ceo

Indexes needed to generate a Schema Summary
These indexes belong to extensional group of the Statistical Indexes [2]:
SC (Subject Class) contains the pairs (p,c) where p is an object property and c
is its domain class.
SCl (Subject Class to literal) contains the pairs (p,c) where p is a datatype
property and c is its domain class.
OC (Object Class) contains the pairs (p,c) where p is an object property and c is
its range class.
sector1 organization1ex:sector
dc:title
“Energy” organization2
Extensional
Classes
Extensional
Knowledge
in thePacific”
“+41331231”
ex:sector
rdf:type rdf:type
dbpedia:fax
person1
foaf:Person
ex:activity
“Paolo”
rdf:type
ex:ceo

Schema Summary generation
We use an algorithm for combining these indexes and produce a Schema
Summary
Name Values
SC
(foaf:Organization,ex:ceo,1),
(foaf:Organization,ex:sector,2)
SCl
(foaf:Person,foaf:firstName,1),
(foaf:Person,foaf:lastName,1),
(foaf:Organization,ex:dbpedia:fax,1),
(ex:Sector,dc:title,1),
(foaf:Organization,ex:activity,1),
(foaf:Organization,dbpedia:fax,1)
OC
(ex:Sector,ex:sector,1)
(ex:Person,ex:ceo,1)

Schema Summary generation
foaf:Organizzation
2
ex:Sector
1
ex:sector 2foaf:Person
1
ex:ceo1
dc:title1foaf:firstName1
foaf:lastName 1
ex:activity1
dbpedia:fax1
We use an algorithm for combining these indexes and produce a Schema
Summary
Name Values
SC
(foaf:Organization,ex:ceo,1),
(foaf:Organization,ex:sector,2)
SCl
(foaf:Person,foaf:firstName,1),
(foaf:Person,foaf:lastName,1),
(foaf:Organization,ex:dbpedia:fax,1),
(ex:Sector,dc:title,1),
(foaf:Organization,ex:activity,1),
(foaf:Organization,dbpedia:fax,1)
OC
(ex:Sector,ex:sector,1)
(ex:Person,ex:ceo,1)

Visualization & Querying
Schema Summary Visualization
Front end of the Web Application composed by three panel:
List of datasets indexed in LODeX
Schema Summary and query building panel
Refinement panel
Query Orchestrator
It manages the interaction between the User and the GUI
It contains a SPARQL compiler able to compile the visual query in a
SPARQL one

Schema Summary – Building a Visual Query

Exercise 7 - LODeX
By using Lodexhttp://www.dbgroup.unimore.it/lodex2/ find the dataset
about World War 1
• What is the name of the dataset?
• How many classes it has? How many properties it has?
Visualize and explore the LODeX schema summary of this dataset
• How many instances does the class Water have?
• What are the incomming properties of the class Municipality?
Define a visual query that select a Dataset and its creator.
• What is the sparql query?

Exercise 8 – Linked Clean Energy Data
• Search the Linked Clean Energy Data and navigate its schema summary
• (http://www.dbgroup.unimore.it/lodex2/ok#!/schemaSummary/157)
• Create a visual query that select a Document and the Project Output
associated.
• For the Project Output, show the title and reference number.
• Run the query and look at the results and the SPARQL query.
• Try to perform the same query at the sparql endpoint you can find in DataHub
for the Linked Clean Energy Data
• (http://sparql.reeep.org/)

Querying LOD datasets
• SPARQL query
• On a SPARQL endpoint
• On a dump dataset
• Visual tools

Introduction to SPARQL
• SPARQL Query
• Declarative query language for RDF data
• http://www.w3.org/TR/rdf-sparql-query/
• SPARQL Algebra
• Standard for communication between SPARQL services and clients
• http://www.w3.org/2001/sw/DataAccess/rq23/rq24-algebra.html
• SPARQL Update
• Declarative manipulation language for RDF data
• http://www.w3.org/TR/sparql11-update/
• SPARQL Protocol
• Standard for communication between SPARQL services and clients
• http://www.w3.org/TR/sparql11-protocol/

SPARQL Basics
• RDF triple: Basic building block, of the form subject, predicate,
object. Example:
• RDF triple pattern: Contains one or more variables. Examples:
• RDF quad pattern: Contains graph name: URI or variable.
Examples:
dbpedia:The_Beatles foaf:name "The Beatles" .
dbpedia:The_Beatles foaf:made ?album.
?album mo:track ?track .
?album ?p ?o .
GRAPH <:g> {:s :p :o .}
GRAPH ?g {dbpedia:The_Beatles foaf:name ?o.}

SPARQL Basics
• RDF graph: Set of RDF assertions, manipulated as
a labeled directed graph.
• RDF data set: set of RDF triples. It is comprised of:
• One default graph
• Zero or more named graphs
• SPARQL protocol client: HTTP client that sends requests for SPARQL
Protocol operations (queries or updates)
• SPARQL protocol service: HTTP server that services requests for
SPARQL Protocol operations
• SPARQL endpoint: The URI at which a SPARQL Protocol service listens
for requests from SPARQL clients

Querying Linked Data with
SPARQL

SPARQL Query
Main idea: Pattern matching
• Queries describe sub-graphs of the queried graph
• Graph patterns are RDF graphs specified in Turtle syntax, which contain
variables (prefixed by either “?” or “$”)
• Sub-graphs that match the graph patterns yield a result
?albumdbpedia:
The_Beatles
foaf:made

SPARQL Query
?album
dbpedia:
The_Beatles
foaf:made
dbpedia:
The_Beatlesfoaf:made
<http://
musicbrainz.org
/record/...>
<http://
musicbrainz.org
/record/...>
foaf:made
Data:
Graph pattern:
Results:
"Help!" "Let It Be"
dc:title dc:title
<http://
musicbrainz.org
/record/...>
"Abbey Road"
dc:title
foaf:made
?album
<http://musicbrainz.org...>

SPARQL Query
?album
dbpedia:
The_Beatles
dbpedia:
<http://
musicbrainz.org
/record/...>
<http://
musicbrainz.org
/record/...>
foaf:made
Data:
Graph pattern:
Results:
"Help!" "Let It Be"
dc:title dc:title
<http://
musicbrainz.org
/record/...>
"Abbey Road"
dc:title
foaf:made
?album ?title
<http://...> "Help!"
<http://...> "Abbey Road"
<http://...> "Let It Be"
?title
dc:title

SPARQL Query
?album
dbpedia:
The_Beatles
dbpedia:
<http://
musicbrainz.org
/record/...>
<http://
musicbrainz.org
/track/...>
foaf:made
Data:
Graph pattern:
Results:
"Help!" "Help!"
dc:title dc:title
mo:track
a
mo:Record mo:Track
mo:Record
?album

SPARQL Query: Components
PREFIX dbpedia: <http://dbpedia.org/resource/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX dc: <http://purl.org/dc/elements/1.1/>
PREFIX mo: <http://purl.org/ontology/mo/>
SELECT ?album
FROM <http://musicbrainz.org/20130302>
WHERE {
dbpedia:The_Beatles foaf:made ?album .
?album a mo:Record ; dc:title ?title
}
ORDER BY ?title
Prologue:
• Prefix definitions
• Subtly different from Turtle syntax - the final period is not used

Query form:
• ASK, SELECT, DESCRIBE or CONSTRUCT
• SELECT retrieves variables and their bindings as a table
SELECT ?album
WHERE {
}
ORDER BY ?title

Data set specification:
• This clause is optional
• FROM or FROM NAMED
• Indicates the sources for the data against which to find matches
SELECT ?album
WHERE {
}
ORDER BY ?title

Query pattern:
• Defines patterns to match against the data
• Generalises Turtle with variables and keywords – N.B. final period optional
SELECT ?album
WHERE {
}
ORDER BY ?title

Solution modifier:
• Modify the result set
• ORDER BY, LIMIT or OFFSET re-organise rows;
• GROUP BY combines them
SELECT ?album
WHERE {
}
ORDER BY ?title

Query Forms
SPARQL supports different query forms:
• ASK tests whether or not a query pattern has a
solution. Returns yes/no
• SELECT returns variables and their bindings
directly
• CONSTRUCT returns a single RDF graph specified
by a graph template
• DESCRIBE returns a single RDF graph containing
RDF data about resource

Query Form: ASK
• Namespaces are added with the ‘PREFIX’ directive
• Statement patterns that make up the graph are
specified between brackets (“{}”)
PREFIX dbpedia-ont: <http://dbpedia.org/ontology/>
PREFIX mo: http://purl.org/ontology/mo/
ASK WHERE { dbpedia:The_Beatles mo:member
dbpedia:Paul_McCartney.}
Is Paul McCartney member of ‘The Beatles’?Query:
true
Results:
Is Elvis Presley member of ‘The Beatles’?Query:
false
Results:
PREFIX dbpedia-ont: <http://dbpedia.org/ontology/>
PREFIX mo: http://purl.org/ontology/mo/
ASK WHERE { dbpedia:The_Beatles mo:member
dbpedia:Elvis_Presley.}

Query Form: SELECT
• The solution modifier projection nominates which
components of the matches should be returned
• “*” means all components should be returned
SELECT ?album_name ?track_title
WHERE {
?album dc:title ?album_name ;
mo:track ?track .
?track dc:title ?track_title .}
Query: What albums and tracks did ‘The Beatles’ make?

Filter expressions
• Different types of filters and functions may be used
Query Form: SELECT (2)
SELECT ?album_name ?track_title ?date ?duration
WHERE {
?album dc:title ?album_name ;
mo:track ?track .
?track dc:title ?track_title ;
mo:duration ?duration;
FILTER (?duration>300000 && ?duration<400000) }
Query:
Filter: Comparison and logical operators
Retrieve the albums and tracks recorded by ‘The Beatles’, where the
duration of the song is more than 300 secs. and no longer than 400 secs.

Aggregates
• Calculate aggregate values: COUNT, SUM, MIN, MAX, AVG,
GROUP_CONCAT and SAMPLE
• Built around the GROUP BY operator
• Prune at group level (cf. FILTER) using HAVING
Query Form: SELECT (3)
SELECT ?album (SUM(?track_duration) AS ?album_duration)
WHERE {
?album mo:track ?track .
?track mo:duration ?track_duration .
} GROUP BY ?album
HAVING (SUM(?track_duration) > 3600000)
Retrieve the duration of the albums recorded by ‘The Beatles’.Query:

Exercise 9 – British Museum
• Find the British Museum Collection dataset.
• Find the related SPARQL endpoint
• Look at information about "The Rosetta Stone"
• http://collection.britishmuseum.org/sparql

Linked Data Publishing Platforms/Frameworks
• D2R Server: a tool for publishing relational databases as Linked Data
• Talis Platform: the Talis Platform provides Linked Data-compliant hosting
for content and RDF data
• Pubby: a Linked Data frontend for SPARQL Endpoints
• Paget: a framework for building Linked Data applications
• Linked Media Framework: a Linked Data server with updates and
semantic search
• PublishMyData: A Linked Data Publishing Platform run by Swirrl. RDF
data-hosting, Linked Data API, SPARQL endpoint and customisable
visualisations.

Exploring Linked Open Data

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