Here are the steps to answer this SPARQL query against the given RDF base:
1. The query asks for all ?name values where there is a triple with predicate "name" and another triple with the same subject and predicate "email".
2. In the base, _:b is the only resource that has both a "name" and "email" triple.
3. _:b has the name "Thomas".
Therefore, the only result of the query is ?name = "Thomas".
So the result of the SPARQL query is:
?name
"Thomas"
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
Apache Spark Data Source V2 with Wenchen Fan and Gengliang WangDatabricks
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1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
LOD , Linked Open Data 에 대한 소개 자료 입니다. LOD는 공공 데이터를 제공, 공유, 재활용하기 위한 또 하나의 방법이며 오픈 데이터(Open Data) 를 위한 하나의 방법으로 웹을 기반으로 데이터를 공유하여 재활용하고자 방법이며 기술이고 데이터입니다.
AWS Public Data Sets: How to Stage Petabytes of Data for Analysis in AWS (WPS...Amazon Web Services
AWS hosts a variety of public data sets that anyone can access for free. Previously, large data sets such as satellite imagery or genomic data have required hours or days to locate, download, customize, and analyze. When data is made publicly available on AWS, anyone can analyze any volume of data without downloading or storing it themselves. In this session, the AWS Open Data Team shares tips and tricks, patterns and anti-patterns, and tools to help you effectively stage your data for analysis in the cloud.
RDF is a general method to decompose knowledge into small pieces, with some rules about the semantics or meaning of those pieces. The point is to have a method so simple that it can express any fact, and yet so structured that computer applications can do useful things with knowledge expressed in RDF.
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
Apache Spark Data Source V2 with Wenchen Fan and Gengliang WangDatabricks
As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements.
1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
LOD , Linked Open Data 에 대한 소개 자료 입니다. LOD는 공공 데이터를 제공, 공유, 재활용하기 위한 또 하나의 방법이며 오픈 데이터(Open Data) 를 위한 하나의 방법으로 웹을 기반으로 데이터를 공유하여 재활용하고자 방법이며 기술이고 데이터입니다.
AWS Public Data Sets: How to Stage Petabytes of Data for Analysis in AWS (WPS...Amazon Web Services
AWS hosts a variety of public data sets that anyone can access for free. Previously, large data sets such as satellite imagery or genomic data have required hours or days to locate, download, customize, and analyze. When data is made publicly available on AWS, anyone can analyze any volume of data without downloading or storing it themselves. In this session, the AWS Open Data Team shares tips and tricks, patterns and anti-patterns, and tools to help you effectively stage your data for analysis in the cloud.
RDF is a general method to decompose knowledge into small pieces, with some rules about the semantics or meaning of those pieces. The point is to have a method so simple that it can express any fact, and yet so structured that computer applications can do useful things with knowledge expressed in RDF.
Like this slidedeck? I write books too! Please check out my newest book Face2face - more info here - http://www.davidleeking.com/face2face
Want to know what trends web designers are thinking about for 2014? Here you go! This presentation has a list of 15 hot web design trends that designers should consider for 2014.
Here's a blog post that goes along with this presentation - http://www.davidleeking.com/2013/10/31/web-design-trends-for-2014/
Video Tools for Teachers - Digital VideoNik Peachey
This presentation is based on my book - Digital Video - A manual for language teachers. It covers a range of uses for video in language learning as both communication and content. A number of tools are presented and a range of activities.
What we carry with us in our everyday lives and interactions is just as important for our success as our technical skills and achievements.
This is what I carry with me. What do YOU carry?
Slides designed and produced with Haiku Deck for iPad. Set your story free with Haiku Deck at http://www.haikudeck.com/
You can learn more about Jonathon Colman at http://www.jonathoncolman.org/
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
How to Make Awesome SlideShares: Tips & TricksSlideShare
Turbocharge your online presence with SlideShare. We provide the best tips and tricks for succeeding on SlideShare. Get ideas for what to upload, tips for designing your deck and more.
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From videos to infographics, I’m constantly leveraging visual media.
Can you guess why?
It’s because these visual content pieces are generating more backlinks than any other form of content I publish, which—in the long run—helps increase my search engine rankings and overall readership numbers.
So, how do you create these visual masterpieces? Well, this infographic should help you.
SlideShare now has a player specifically designed for infographics. Upload your infographics now and see them take off! Need advice on creating infographics? This presentation includes tips for producing stand-out infographics. Read more about the new SlideShare infographics player here: http://wp.me/p24NNG-2ay
This infographic was designed by Column Five: http://columnfivemedia.com/
Not sure what to share on SlideShare?
SlideShares that inform, inspire and educate attract the most views. Beyond that, ideas for what you can upload are limitless. We’ve selected a few popular examples to get your creative juices flowing.
No need to wonder how the best on SlideShare do it. The Masters of SlideShare provides storytelling, design, customization and promotion tips from 13 experts of the form. Learn what it takes to master this type of content marketing yourself.
Two graph data models : RDF and Property Graphsandyseaborne
Talk given at ApacheConEU Big Data 2015.
This talk describes the two common graph data approaches, RDF and Property Graphs. It concludes with observations about the different emphasis of each and where each is focused.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
Tutorial on RDFa, to be held at ISWC2010 in Shanghai, China. (I was supposed to hold the tutorial but last minute issues made it impossible for me to travel there...)
This presentation is an introduction to RDFa, as the fourth assignment of the IST 681 in iSchool, Syracuse University. The presentation is made by Kai Li, who is a library student in Syracuse University..
Similar to An introduction to Semantic Web and Linked Data (20)
Walking Our Way to the Web - Fabien Gandon
The Web: Scientific Creativity, Technological Innovation and Society
XXVIII Conference on Contemporary Philosophy and Methodology of Science
9 and 10 March 2023
University of A Coruña
The prospect of Walking our Way to the Web may sound strange to contemporary readers of this article for whom the Web is omnipresent. However, the slogan of the World Wide Web Consortium (W3C) has been, for years, and remains today, to lead “the Web to its full potential” meaning we haven’t reached that potential yet, whatever it is. The first architect of the Web himself, Tim Berners-Lee, said in an interview in 2009: “The Web as I envisaged it, we have not seen it yet. The future is still so much bigger than the past”. And he is still very active, together with the W3C members and Web experts world-wide, in proposing evolutions of the Web architecture to improve its growing usages and applications. In this article we will review the path that led us to the actual Web, the shape it is taking now and the possible evolutions, good and bad, we can identify today. This will lead us to consider the distance that we witness between the initial vision and the reality of the Web today, and to reflect on the possible divergence between the potential we see in the Web and the directions it could take. Our goal in this article is to reflect on how we could walk the delicate path to the full potential of the Web, finding the missing links and avoiding the one too many links.
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https://hal.inria.fr/WIMMICS/hal-03633526
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https://op.europa.eu/en/web/endorse
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An introduction to Semantic Web and Linked Data
1. Semantic Web and Linked Data
or how to link data
and schemas on the web
a W3C tutorial by Fabien Gandon, http://fabien.info, @fabien_gandon
WWW 2014
2. semantic web
mentioned by Tim BL
in 1994 at WWW
[Tim Berners-Lee 1994, http://www.w3.org/Talks/WWW94Tim/]
11. RDFstands for
Resource: pages, dogs, ideas...
everything that can have a URI
Description: attributes, features, and
relations of the resources
Framework: model, languages and
syntaxes for these descriptions
12. RDFis a triple model i.e. every
piece of knowledge is broken down into
( subject , predicate , object )
33. writing rules for RDF triples
• the subject is always a resource (never a literal)
• properties are binary relations and their types are
identified by IRIs
• the value is a resource or a literal
34. blank nodes (bnodes)
http://bu.ch/l23.html
author
"My Life"
title
"John"
surname
"Doe"
firstname
handy anonymous nodes (existential quantification)
there exist a resource such that… { r ; …}
<rdf:Description rdf:about="http://bu.ch/123.html ">
<author>
<rdf:Description>
<surname>Doe</surname>
<firstname>John</firstname>
</rdf:Description>
</author>
<title>My Life</title>
</rdf:Description>
<http://bu.ch/123.html>
author
[surname "Doe" ;
firstname "John" . ] ;
title "My Life" .
35. XML schema datatypes & literals
standard literals are xsd:string
type literals with datatypes from XML Schema
<rdf:Description rdf:about="#Fabien">
<teaching rdf:datatype="http://www.w3.org/2001/XMLSchema#boolean">
true</teaching>
<birth rdf:datatype="http://www.w3.org/2001/XMLSchema#date">
1975-07-31</birth>
</rdf:Description/>
#Fabien teaching "true"^^xsd:boolean ;
birth "1975-07-31"^^xsd:date .
#Fabien "true"^^xsd:boolean
"1975-07-31"^^xsd:date
teaching
birth
37. langue
<Book>
<title xml:lang=‘fr’>Seigneur des anneaux</title>
<title xml:lang=‘en’>Lord of the rings</title>
</Book>
<Book> title "Seigneur des anneaux"@fr ;
title "Lord of the rings"@en .
literals with languages and without are disjoint
“Fabien” “Fabien”@en “Fabien”@fr
38. typing resources
using URIs to identify the types
<urn://~fgandon> rdf:type <http://www.inria.fr/schema#Person>
a resource can have several types
<urn://~fgandon> rdf:type <http://www.inria.fr/schema#Person>
<urn://~fgandon> rdf:type <http://www.inria.fr/schema#Researcher>
<urn://~fgandon> rdf:type <http://www.mit.edu/schema#Lecturer>
<rdf:Description rdf:about="urn://~fgandon">
<rdf:type rdf:resource="http://www.inria.fr/schema#Person" />
<name>Fabien</name>
</rdf:Description>
<in:Person rdf:about="urn://~fgandon">
<name>Fabien</name>
</in:Person>
<urn://~fgandon>
a in:Person ;
name "Fabien" .
43. alternativese.g. title of a book in different languages
<rdf:Description rdf:about="#book">
<title>
<rdf:Alt>
<rdf:li xml:lang="fr">l’homme qui prenait sa femme
pour un chapeau</rdf:li>
<rdf:li xml:lang="en">the man who mistook his wife
for a hat</rdf:li>
</rdf:Alt>
</title>
</rdf:Description>
<#book>
title [
a rdf:Alt ;
rdf:li "l’homme…"@fr ;
rdf:li "the man…"@en .
] .
59. May 2007 April 2008 September 2008
March 2009
September 2010
Linking Open Data
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
September 2011
0
100
200
300
400
10/10/2006 28/04/2007 14/11/2007 01/06/2008 18/12/2008 06/07/2009 22/01/2010 10/08/2010 26/02/2011 14/09/2011 01/04/2012
63. linked data principles Use RDF as data format
Use HTTP URIs as names for things so that
people can look up those names
When someone looks up a URI, provide useful information
(RDF, HTML, etc.) using content negotiation
Include links to other URIs so that related things can be discovered
HTTP URI
GET
HTML,RDF,…
GET
303
64. DNShe who controls the name
controls the access
ex. bit.ly & Libya
.fr
*
.inria
isicil
71. graph mapping / projection
classical three clauses:
– Select: clause to select the values to be returned
– Where: triple/graph pattern to match
– Filter: constraints expressed using test functions
(XPath 2.0 or external)
72. SPARQL triples
• triples and question marks for variables:
?x rdf:type ex:Person
• graph patterns to match:
SELECT ?subject ?proprerty ?value
WHERE {?subject ?proprerty ?value}
• a pattern is, by default, a conjunction of triples
SELECT ?x WHERE
{ ?x rdf:type ex:Person .
?x ex:name ?name . }
73. question:
• Query:
SELECT ?name WHERE {
?x name ?name .
?x email ?email .
}
• Base:
_:a name "Fabien"
_:b name "Thomas"
_:c name "Lincoln"
_:d name "Aline"
_:b email <mailto:thom@chaka.sn>
_:a email <mailto:Fabien.Gandon@inria.fr>
_:d email <mailto:avalandre@pachinko.jp>
_:a email <mailto:bafien@fabien.info>
• Results ?
x2
74. prefixes
to use namespaces:
PREFIX mit: <http://www.mit.edu#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?student
WHERE {
?student mit:registeredAt ?x .
?x foaf:homepage <http://www.mit.edu> .
}
Base namespace : BASE <…>
76. result formats
• a binding i.e. list of all the selected values
(SELECT) for each answer found;
(stable XML format ; e.g. for XSLT transformations)
• RDF sub-graphs for each answer found
(RDF/XML format ; e.g. for application integration)
• JSON (eg. ajax web applications)
• CSV/TSV (eg. export)
77. example of binding
results for previous query in XML
<?xml version="1.0"?>
<sparql xmlns="http://www.w3.org/2005/sparql-results#">
<head>
<variable name="student"/>
</head>
<results ordered="false" distinct="false">
<result>
<binding name="student">
<uri>http//www.mit.edu/data.rdf#ndieng</uri></binding>
</result>
<result>
<binding name="student">
<uri>http//www.mit.edu/data.rdf#jdoe</uri></binding>
</result>
</sparql>
78. simplified syntax
triples with a common subject:
SELECT ?name ?fname
WHERE {
?x a Person;
name ?name ;
firstname ?fname ;
author ?y . }
list of values
?x firstname "Fabien", "Lucien" .
blank node
[firstname "Fabien"] or [] firstname "Fabien"
SELECT ?name ?fname
WHERE {
?x rdf:type Person .
?x name ?name .
?x firstname ?fname .
?x author ?y .
}
81. union
alternative graph patterns
PREFIX mit: <http://www.mit.edu#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?student ?name
WHERE {
?student mit:registeredAt ?x .
{
{
?x foaf:homepage <http://www.mit.edu> .
}
UNION
{
?x foaf:homepage <www.stanford.edu/> .
}
}
}
82. sort, filter and limit answers
PREFIX mit: <http://www.mit.edu#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?student ?name
WHERE {
?student mit:registeredAt ?x .
?x foaf:homepage <http://www.mit.edu> .
?student foaf:name ?name .
? student foaf:age ?age .
FILTER (?age > 22)
}
ORDER BY ?name
LIMIT 20
OFFSET 20
students older than 22 years sorted by name
results from number #21 to #40
83. operators
• Inside the FILTER:
– Comparators: <, >, =, <=, >=, !=
– Tests on variables : isURI(?x), isBlank(?x),
isLiteral(?x), bound(?x)
– Regular expression regex(?x, "A.*")
– Attributes and values: lang(), datatype(), str()
– Casting: xsd:integer(?x)
– External functions and extensions
– Boolean combinations: &&, ||
• In the where WHERE: @fr , ^^xsd:integer
• In the SELECT: distinct
84. other functions (v 1.1)
isNumeric(Val) test it is a numeric value
coalesce(val,…, val) first valid value
IRI(Str)/URI(Str) to build an iri/uri from a string
BNODE(ID) to build a blank node
RAND() random value between 0 and 1
ABS(Val) absolute value
CEIL(Val), FLOOR(Val), ROUND(Val)
NOW() today’s date
DAY(Date), HOURS(Date), MINUTES(Date),
MONTH(Date), SECONDS(Date),
TIMEZONE(Date), TZ(Date), YEAR(Date)
to access different parts of a date
MD5(Val), SHA1(Val), SHA256(Val),
SHA384(Val), SHA512(Val) hash functions
85. string / literal functions (v1.1)
STRDT(value, type) build a typed literal
STRLANG(value, lang) build a literal with a language
CONCAT(lit1,…,litn) concatenate a list of literal
CONTAINS(lit1,lit2), STRSTARTS(lit1,lit2),
STRENDS(lit1,lit2)
to test string inclusion
SUBSTR(lit, start [,length]) extract a sub string
ENCODE_FOR_URI (Str) encodes a string as URI
UCASE (Str), LCASE (Str) uppercase and lowercase
STRLEN (Str) length of the string
86. aggregates
group by + count, sum, min, max,
avg, group_concat, or sample
ex. average scores, grouped by the subject, but
only where the mean is greater than 10
SELECT (AVG(?score) AS ?average)
WHERE { ?student score ?score . }
GROUP BY ?student
HAVING(AVG(?score) > 10)
91. test a value is in / not in a list
prefix foaf: <http://xmlns.com/foaf/0.1/>
select * where {
?x foaf:name ?n .
filter (?n in ("fabien", "olivier",
"catherine") )
}
108. Test on DBpedia
• Connect to:
http://dbpedia.org/snorql/ or
http://fr.dbpedia.org/sparql or …
http://wiki.dbpedia.org/Internationalization/Chapters
• Query:
SELECT * WHERE {
?x rdfs:label "Paris"@fr .
?x ?p ?v .
}
LIMIT 10
127. RDFS to define classes of resources
and organize their hierarchy
Document
Report
128. RDFS to define relations between
resources, their signature
and organize their hierarchy
creator
author
Document Person
129. FO R GF GRmapping modulo an ontology
car
vehicle
car(x)vehicle(x)
GF
GRvehicle
car
O
130. an old schema of RDFS
W3C http://www.w3.org/TR/2000/CR-rdf-schema-20000327/
131. example of RDFS schema
<rdf:RDF xml:base ="http://inria.fr/2005/humans.rdfs"
xmlns:rdf ="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns ="http://www.w3.org/2000/01/rdf-schema#>
<Class rdf:ID="Man">
<subClassOf rdf:resource="#Person"/>
<subClassOf rdf:resource="#Male"/>
<label xml:lang="en">man</label>
<comment xml:lang="en">an adult male person</comment>
</Class>
<Man> a Class ; subClassOf <Person>, <Male> .
132. example of RDFS properties
<rdf:RDF xml:base ="http://inria.fr/2005/humans.rdfs"
xmlns:rdf ="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns ="http://www.w3.org/2000/01/rdf-schema#>
<rdf:Property rdf:ID="hasMother">
<subPropertyOf rdf:resource="#hasParent"/>
<range rdf:resource="#Female"/>
<domain rdf:resource="#Human"/>
<label xml:lang="en">has for mother</label>
<comment xml:lang="en">to have for parent a female.
</comment>
</rdf:Property>
<hasMother> a rdf:Property ;
subPropertyOf <hasParent> ;
range <Female> ; domain <Human> .
133. example of RDF using this schema
<rdf:RDF xmlns:rdf ="http://www.w3.org/1999/02/22-rdf-
syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns="http://inria.fr/2005/humans.rdfs#"
xml:base=" http://inria.fr/2005/humans.rdfs-instances" >
<rdf:Description rdf:ID="Lucas">
<rdf:type
rdf:resource="http://inria.fr/2005/humans.rdfs#Man"/>
<hasMother rdf:resource="#Laura"/>
</rdf:Description>
<Man rdf:ID="Lucas">
<hasMother rdf:resource="#Laura"/>
</Man>
<Luca> a Man; hasMother <Laura> .
134. rdfs:label
a resource may have one or more labels in
one or more natural language
<rdf:Property rdf:ID='name'>
<rdfs:domain rdf:resource='Person'/>
<rdfs:range rdf:resource='&rdfs;Literal'/>
<rdfs:label xml:lang='fr'>nom</rdfs:label>
<rdfs:label xml:lang='fr'>nom de famille</rdfs:label>
<rdfs:label xml:lang='en'>name</rdfs:label>
</rdf:Property>
<name> a rdf:Property ;
range rdfs:Literal ; domain <Person> ;
label "nom"@fr, "nom de famille"@fr, "name"@en .
135. rdfs:comment & rdfs:seeAlso
comments provide definitions and explanations in natural
language
<rdfs:Class rdf:about=‘#Woman’>
<rdfs:subClassOf rdf:resource="#Person"/>
<rdfs:comment xml:lang=‘fr’>une personne adulte du
sexe féminin</rdfs:comment>
<rdfs:comment xml:lang=‘en’>a female adult person
</rdfs:comment>
</rdfs:Class>
see also…
<rdfs:Class rdf:about=‘#Man’>
<rdfs:seeAlso rdf:resource=‘#Woman’/>
</rdfs:Class>
<Woman> a rdfs:Class ; rdfs:subClassOf <Person> ;
rdfs:comment "adult femal person"@en ;
rdfs:comment "une adulte de sexe féminin"@fr .
<Man> a rdfs:Class ; rdfs:seeAlso <Woman> .
139. enumerated class
define a class by providing all its members
<owl:Class rdf:id="EyeColor">
<owl:oneOf rdf:parseType="Collection">
<owl:Thing rdf:ID="Blue"/>
<owl:Thing rdf:ID="Green"/>
<owl:Thing rdf:ID="Brown"/>
<owl:Thing rdf:ID="Black"/>
</owl:oneOf>
</owl:Class>
{a,b,c,d,e}
140. classes defined by union
of other classes
<owl:Class>
<owl:unionOf rdf:parseType="Collection">
<owl:Class rdf:about="#Person"/>
<owl:Class rdf:about="#Group"/>
</owl:unionOf>
</owl:Class>
141. classes defined by intersection
of other classes
<owl:Class rdf:ID="Man">
<owl:intersectionOf rdf:parseType="Collection">
<owl:Class rdf:about="#Male"/>
<owl:Class rdf:about="#Person"/>
</owl:intersectionOf>
</owl:Class>
142. complement and disjunction
complement class
<owl:Class rdf:ID="Male">
<owl:complementOf rdf:resource="#Female"/>
</owl:Class>
declare a disjunction
<owl:Class rdf:ID="Square">
<owl:disjointWith rdf:resource="#Round"/>
</owl:Class>
144. restriction on some values
<owl:Class rdf:ID="Sportive">
<owl:equivalentClass>
<owl:Restriction>
<owl:onProperty rdf:resource="#hobby" />
<owl:someValuesFrom rdf:resource="#Sport" />
</owl:Restriction>
</owl:equivalentClass>
</owl:Class>
!
145. restriction to an exact value
<owl:Class rdf:ID="Bike">
<subClassOf>
<owl:Restriction>
<owl:onProperty rdf:resource="#nbWheels" />
<owl:hasValue>2</owl:hasValue>
</owl:Restriction>
</subClassOf>
</owl:Class>
!
146. restriction on cardinality
how many times a property is used for
a same subject but with different values
• Constraints: minimum, maximum, exact number
• Exemple
<owl:Class rdf:ID="Person">
<subClassOf>
<owl:Restriction>
<owl:onProperty rdf:resource="#name" />
<owl:maxCardinality>1</owl:maxCardinality>
</owl:Restriction>
</subClassOf>
</owl:Class>
1..1
147. types of properties
• ObjectProperty are relations between resources only
e.g. hasParent(#thomas,#stephan)
• DatatypeProperty have a literal value possibly typed
ex:hasAge(#thomas,16^^xsd:int)
• AnnotationProperty are ignored in inferences and used
for documentation and extensions
149. equivalencies and alignment
• equivalent classes : owl:equivalentClass
• equivalent properties: owl:equivalentProperty
• identical or different resources:
owl:sameAs, owl:differentFrom
150. document the schemas
description of the ontology
owl:Ontology, owl:imports, owl:versionInfo,
owl:priorVersion, owl:backwardCompatibleWith,
owl:incompatibleWith
versions of classes and properties
owl:DeprecatedClass, owl:DeprecatedProperty
151. OWL profiles
EL: large numbers of properties and/or classes
and polynomial time.
QL: large volumes of instance data, and
conjunctive query answering using
conventional relational database in LOGSPACE
RL: scalable reasoning without sacrificing too
much expressive power using rule-based
reasoning in polynomial time
161. e.g. infer new relations
rule: if a member of a team is interested in a topic then
the team as a whole is interested in that topic
?person interestedBy ?topic
?person member ?team
?team interestedBy ?topic
interestedByPerson
?person
Topic
?topic
member Team
?team
interestedBy
164. RIF Core
monotonic Horn clause on frames
conclusion :- hyp1 and hyp2 and hyp3 …
• IRI as constants
• frames as triplets
• lists
• existential quantification in condition
• class membership and equality in condition
165. RIF BLD (Basic Logic Dialect)
still monotonic : no changes.
• conjunction in conclusion
• fonctions, predicates and named arguments f(?x)
Maganer(?e) :- Exists ?g (manage(?e ?g))
• disjunction in condition
• equality in conclusion
• sub-classes
166. RIF PRD (Production Rules Dialect)
full production rules in forward chaining
• add, delete, modify, run
• instantiate frames (new)
• negation as failure (ineg)
• no longer monotonic
Forall ?customer ?purchasesYTD
(If And( ?customer#ex:Customer
?customer[ex:purchasesYTD->?purchasesYTD]
External(pred:numeric-greater-than(?purchasesYTD 5000)) )
Then Do( Modify(?customer[ex:status->"Gold"]) ) )
(from PRD Rec. Doc.)
167. RIF, RIF, RIF,…
• DTB (Datatypes and Built-Ins) : data types with their
predicates and functions
• FLD: how to specify new dialects extending BLD
• SWC : syntax and semantics to combine RIF, RDF
graphs, RDFS and OWL (RL)
171. inria:CorporateSemanticWeb
skos:scopeNote "only within KM community";
skos:definition "a semantic web on an intranet";
skos:example "Nokia's internal use of RDF gateway";
skos:historyNote "semantic intranet until 2006";
skos:editorialNote "keep wikipedia def. uptodate";
skos:changeNote "acronym added by fabien".
176. direct mapping
• cells of a line triples with a shared subject
• names of columns names of properties
• each value of a cell one object
• links between tables
name fname age
doe john 34
did sandy 45
#s1 :name "doe"
#s1 :fname "john"
#s1 :age "34"
#s2 :name "did"
#s2 :fname "sandy"
#s2 :age "45"
#s3 …
177. example of mapping
ISBN Author Title Year
0006511409X id_xyz The Glass Palace 2000
ID Name Homepage
id_xyz Ghosh, Amitav http://www.amitavghosh.com
http://…isbn/000651409X
Ghosh, Amitav http://www.amitavghosh.com
The Glass Palace
2000
a:name
a:homepage
a:author
178. (1) transforming
table of persons
ISBN Author Title Year
0006511409X id_xyz The Glass Palace 2000
ID Name Homepage
id_xyz Ghosh, Amitav http://www.amitavghosh.com
http://…isbn/000651409X
Ghosh, Amitav http://www.amitavghosh.com
The Glass Palace
2000
a:name
a:homepage
a:author
:P_Table rdf:type rr:TriplesMap ;
rr:subjectMap [
rr:termtype "BlankNode" ;
rr:column "ID" ;
] ;
rr:predicateObjectMap [
rr:predicateMap [
rr:predicate a:name
];
rr:objectMap [
rr:column "Name"
]
] ;
rr:predicateObjectMap [
rr:predicateMap [
rr:predicate a:homepage
];
rr:objectMap [
rr:column "Homepage" ;
rr:termtype "IRI"
]
] ;
179. (2) transforming
table of books
ISBN Author Title Year
0006511409X id_xyz The Glass Palace 2000
ID Name Homepage
id_xyz Ghosh, Amitav http://www.amitavghosh.com
http://…isbn/000651409X
Ghosh, Amitav http://www.amitavghosh.com
The Glass Palace
2000
a:name
a:homepage
a:author
:B_Table rdf:type rr:TriplesMap ;
rr:subjectMap [
rr:template "http://...isbn/{ISBN}";
];
rr:predicateObjectMap [
rr:predicateMap [
rr:predicate a:title
];
rr:objectMap [
rr:column "Title"
]
] ;
rr:predicateObjectMap [
rr:predicateMap [
rr:predicate a:year
];
rr:objectMap [
rr:column "Year" ;
]
] ;
180. (3) linking tables
ISBN Author Title Year
0006511409X id_xyz The Glass Palace 2000
ID Name Homepage
id_xyz Ghosh, Amitav http://www.amitavghosh.com
http://…isbn/000651409X
Ghosh, Amitav http://www.amitavghosh.com
The Glass Palace
2000
a:name
a:homepage
a:author
:B_Table a rr:TriplesMap ;
...
rr:refPredicateObjectMap [
rr:refPredicateMap [
rr:predicate a:author
];
rr:refObjectMap [
rr:parentTriplesMap :P_Table ;
rr:joinCondition
"{child}.Author = {parent}.ID"
]
]
].
182. RDFa 1.1: example on schema.org
<div vocab="http://schema.org/" typeof="Product">
<img rel="image" src="dell-30in-lcd.jpg" />
<span property="name">Dell UltraSharp 30" LCD Monitor</span>
<div rel="hasAggregateRating" >
<div typeof="http://schema.org/AggregateRating">
<span property="ratingValue">87</span>
out of <span property="bestRating">100</span>
based on <span property="ratingCount">24</span> user ratings
</div>
</div>
<div rel="offers" >
<div typeof="http://schema.org/AggregateOffer">
<span property="lowPrice">$1250</span>
to <span property="highPrice">$1495</span>
from <span property="offerCount">8</span> sellers
</div>
</div>
(…)
PS: RDFa Lite = vocab + typeof + property + about + prefix.
183. GRDDL opens formats
by allowing us to declare RDF extraction
algorithms inside XML documents
<head profile="http://www.w3.org/2003/g/data-view">
<title>The man who mistook his wife for a hat</title>
<link rel="transformation"
href="http://www.w3.org/2000/06/ dc-extract/dc-extract.xsl" />
<meta name="DC.Subject" content="clinical tales" />
…
207. annotating multimédia elements
• semantic description of multimedia
resources [Media Annotation]
• pointing to internal elements of
multimedia resources [Media Fragment]
208. multimedia fragment
• part of the URL after the #
http://www.example.com/example.ogv#track=audio&t=10,20
• dimensions:
– temporal:
t=10,20 / t=npt:,0:02:01.5 / t=clock:2009-07-26T11:19:01Z
– spatial:
xywh=pixel:160,120,320,240 / xywh=percent:25,25,50,50
– track:
track=1 / track=video&track=subtitle / track=Wide
– named:
id=chapter-1
• fragment are not sent with the URL but encoded in
the HTTP request
209. ontologies for multimedia descriptions
ontology for Media Resources 1.0
<video.ogv> a ma:MediaResource ;
ma:hasTrack <video.ogv#track=audio>,
<video.ogv#track=subtitle>;
ma:hasSubtitling <video.ogv#track=subtitle> ;
ma:hasSigning <video.ogv#xywh=percent:70,70,90,90> .
<video.ogv#track=audio> a ma:AudioTrack ;
ma:hasLanguage [ rdfs:label "en-GB" ] ;
ma:hasFragment <video.ogv#track=audio&t=10,20> .
<video.ogv#track=audio&t=10,20> a ma:MediaFragment ;
ma:hasLanguage [ rdfs:label "fr" ] .
<video.ogv#track=subtitle> a ma:DataTrack ;
ma:hasLanguage [ rdfs:label "es" ] .
<video.ogv#xywh=percent:70,70,90,90> a ma:MediaFragment ;
ma:hasLanguage [ rdfs:label "bfi" ] .
220. 66 FOAF primitives 3 475 908 348 references (2)
x 52 millions
“a small tree ruling a big graph”(1)
(1) Franck Van Harmelen, ISWC 2011
(2) Libby Miller, 2009
222. data
data bases
data models
open data
linked data
closed data
enterprise data
linked enterprise data
linked open data
data schemas
semantic web of data
data structures
linked data schemas
web of data
big data
big data streams
data streams
linked data streams web of sensors, things, …
VELOCITY
big linked data
VOLUME
VARIETY
VVeb data
linked healthcare data
VICINITY
VISIBILITY
personal data
data mining
data type
228. he who controls metadata, controls the web
and through the world-wide web many things in our world.
fabien, gandon, @fabien_gandon, http://fabien.info
WWW 2014