Technical Background

Chapter 2
Technical Background
NIKOLAOS KONSTANTINOU
DIMITRIOS-EMMANUEL SPANOS
Materializing the Web of Linked Data

Outline
Introduction
RDF and RDF Schema
Description Logics
Querying RDF data with SPARQL
Mapping relational data with R2RML
Other technologies
Ontologies
Datasets
Chapter 2 Materializing the Web of Linked Data 2

Introduction
Linked Data
◦ A set of technologies
◦ Focused on the web
◦ Use the Web as a storage and communication layer
◦ Provide meaning to web content
Semantics
◦ Added value when part of a larger context
◦ Data modeled as a graph
Technologies fundamental to the web

HTTP – HyperText Transfer Protocol
An application protocol for the management and transfer of
hypermedia documents in decentralized information systems
Defines a number of request types and the expected actions
that a server should carry out when receiving such requests
Serves as a mechanism to
◦ Serialize resources as a stream of bytes
◦ E.g. a photo of a person
◦ Retrieve descriptions about resources that cannot be sent over network
◦ E.g. the person itself

URI – Uniform Resource Identifier
URL
◦ Identifies a document location
◦ Address of a document or other entity that can be found online
URI
◦ Provides a more generic means to identify anything that exists in the
world
IRI
◦ Internationalized URI
IRI ⊇ URI ⊇ URL

HTML – HyperText Markup Language
A markup language for the composition and presentation of
various types of content into web pages
◦ E.g. text, images, multimedia
Documents delivered through HTTP are usually expressed in
HTML
◦ HTML5
◦ Current recommendation
◦ Additional markup tags
◦ Extends support for multimedia and mathematical content

XML – eXtensible Markup Language
Allows for strict definition of the structure of information
◦ Markup tags
The RDF model also follows an XML syntax

Outline
Introduction
RDF and RDF Schema
Description Logics
Other technologies
Ontologies
Datasets

Modeling Data Using RDF Graphs
Ontologies in the Semantic Web
◦ Model a system’s knowledge
◦ Based on RDF
◦ Model the perception of the world as a graph
◦ OWL builds on top of RDF
RDF
◦ A data model for the Web
◦ A framework that allows representing knowledge
◦ Model knowledge as a directed and labeled graph

RDF (1)
The cornerstone of the Semantic Web
A common representation of web resources
First publication in 1999
Can represent data from other data models
◦ Makes it easy to integrate data from multiple heterogeneous sources
Main idea:
◦ Model every resource with respect to its relations (properties) to
other web resources

RDF (2)
Relations form triples
◦ First term: subject
◦ Second term: property
◦ Third term: object
RDF statements contain triples, in the form:
(resource, property, resource)
or (subject, property, object)
ex:staffMember
ex:hasWorkAddress
ex:hasCity ex:hasAddress ex:hasPostalCode
“London” “23, Houghton str.” “WC2A 2AE”

Namespaces (1)
Initially designed to prevent confusion in XML names
Allow document elements to be uniquely identified
Declared in the beginning of XML documents
xmlns:prefix="location".

Namespaces (2)
Prefix Namespace URI Description
rdf http://www.w3.org/1999/02/22-rdf-syntax-ns# The built-in RDF vocabulary
rdfs http://www.w3.org/2000/01/rdf-schema RDFS puts an order to RDF
owl http://www.w3.org/2002/07/owl OWL terms
xsd http://www.w3.org/2001/XMLSchema# The RDF-compatible XML Schema datatypes
dc http://purl.org/dc/elements/1.1/ The Dublin Core standard for digital object description
foaf http://xmlns.com/foaf/0.1 The FOAF network
skos http://www.w3.org/2004/02/skos/core# Simple Knowledge Organization System
void http://rdfs.org/ns/void# Vocabulary of Interlinked Datasets
sioc http://rdfs.org/sioc/ns# Semantically-Interlinked Online Communities
cc http://creativecommons.org/ns Creative commons helps expressing licensing information
rdfa http://www.w3.org/ns/rdfa RDFa

RDF Serialization
RDF is mainly destined for machine consumption
Several ways to express RDF graphs in machine-readable
(serialization) formats
◦ N-Triples
◦ Turtle
◦ N-Quads
◦ TriG
◦ RDF/XML
◦ JSON-LD
◦ RDFa
Turtle family
RDFa
JSON-LD
Supports Multiple Graphs
RDF/XML
Turtle
N-Triples
TriG
N-Quads

N-Triples Serialization
<http://www.example.org/bradPitt> <http://www.example.org/isFatherOf>
<http://www.example.org/maddoxJoliePitt>.
<http://www.example.org/bradPitt> <http://xmlns.com/foaf/0.1/name> "Brad Pitt".
<http://www.example.org/bradPitt> <http://xmlns.com/foaf/0.1/based_near> :_x.
:_x <http://www.w3.org/2003/01/geo/wgs84_pos#lat> "34.1000".
:_x <http://www.w3.org/2003/01/geo/wgs84_pos#long> "118.3333".

Turtle Serialization
PREFIX ex: <http://www.example.org/>.
PREFIX foaf: <http://xmlns.com/foaf/0.1/>.
PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#>.
ex:bradPitt ex:isFatherOf ex:maddoxJoliePitt;
foaf:name "Brad Pitt";
foaf:based_near :_x.
:_x geo:lat "34.1000";
geo:long "118.3333".

TriG Serialization
PREFIX ex: <http://www.example.org/>.
PREFIX foaf: <http://xmlns.com/foaf/0.1/>.
PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#>.
GRAPH <http://www.example.org/graphs/brad> {
ex:bradPitt ex:isFatherOf ex:maddoxJoliePitt;
foaf:name "Brad Pitt";
foaf:based_near :_x.
:_x geo:lat "34.1000";
geo:long "118.3333".
}

XML/RDF Serialization
<?xml version="1.0" encoding="utf-8"?>
<rdf:RDF xmlns:ex="http://www.example.org/"
xmlns:foaf="http://xmlns.com/foaf/0.1/"
xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#">
<rdf:Description rdf:about=" http://www.example.org/bradPitt">
<ex:isFatherOf rdf:resource=" http://www.example.org/maddoxJoliePitt"/>
<foaf:name>Brad Pitt</foaf:name>
<foaf:based_near rdf:nodeID="A0"/>
</rdf:Description>
<rdf:Description rdf:nodeID="A0">
<geo:lat>34.1000</geo:lat>
<geo:long>118.3333</geo:long>
</rdf:Description>
</rdf:RDF>

JSON-LD Serialization
{
"@context":{
"foaf": "http://xmlns.com/foaf/0.1/",
"child": {
"@id": "http://www.example.org/isFatherOf",
"@type": "@id"
},
"name": "foaf:name",
"location": "foaf:based_near",
"geo": "http://www.w3.org/2003/01/geo/wgs84_pos#",
"lat": "geo:lat",
"long": "geo:long"
},
"@id": "http://www.example.org/bradPitt",
"child": "http://www.example.org/maddoxJoliePitt",
"name": "Brad Pitt",
"location": {
"lat": "34.1000",
"long": "118.3333"
}
}

RDFa Serialization
<html>
<head>
…
</head>
<body vocab="http://xmlns.com/foaf/0.1/">
<div resource="http://www.example.org/bradPitt">
<p>Famous American actor <span property="name">Brad Pitt</span>
eldest son is
<a property="http://www.example.org/isFatheOf"
href="http://www.example.org/maddoxJoliePitt">Maddox Jolie-Pitt</a>.
</p>
</div>
</body>
</html>

The RDF Schema (1)
RDF
◦ A graph model
◦ Provides basic constructs for defining a graph structure
RDFS
◦ A semantic extension of RDF
◦ Provides mechanisms for assigning meaning to the RDF nodes and
edges
◦ RDF Schema is not to RDF what XML Schema is to XML!

The RDF Schema (2)
Classes
◦ A definition of groups of resources
◦ Members of a class are called instances of this class
Class hierarchy
Property hierarchy
Property domain and range
RDFS specification also refers to the RDF namespace
Prefix Namespace
rdfs http://www.w3.org/2000/01/rdf-schema#
rdf http://www.w3.org/1999/02/22-rdf-syntax-ns#

The RDF Schema (3)
A Class named Actor
◦ Where ex: http://www.example.org/
Class membership example
Containment relationship
Using reasoning, we can infer
◦ In practice, we could store inferred triples
ex:Actor rdf:type rdfs:Class.
ex:bradPitt rdf:type ex:Actor.
ex:Actor rdfs:subClassOf ex:MovieStaff.
ex:bradPitt rdf:type ex:MovieStaff.

The RDF Schema (4)
No restrictions on the form of the class hierarchy that can be defined
in an ontology
◦ No strict tree-like hierarchy as regular taxonomies
◦ More complex graph-like structures are allowed
◦ Classes may have more than one superclass
Allows hierarchies of properties to be defined
◦ Just like class hierarchies
◦ Introduced via the rdfs:subPropertyOf property
ex:participatesIn rdf:type rdf:Property.
ex:starsIn rdf:type rdf:Property.
ex:starsIn rdfs:subPropertyOf ex:participatesIn.

The RDF Schema (5)
◦ Inferred triple:
Define the classes to which RDF properties can be applied
◦ rdfs:domain and rdfs:range
Then, the assertion
Entails
ex:bradPitt ex:starsIn ex:worldWarZ.
ex:bradPitt ex:participatesIn ex:worldWarZ.
ex:georgeClooney ex:starsIn ex:idesOfMarch.
ex:georgeClooney rdf:type ex:Actor.
ex:starsIn rdfs:domain ex:Actor.

The RDF Schema (6)
Similarly
Produces
Domain and range axioms
◦ Are not constraints that data need to follow
◦ Function as rules that lead to the production of new triples and knowledge
ex:starsIn rdfs:range ex:Movie.
ex:worldWarZ rdf:type ex:Movie.
ex:starsIn rdfs:range ex:Movie.

The RDF Schema (7)
Example
◦ Infers:
An individual entity can be a member of both ex:Actor and ex:Movie
classes
Such restrictions are met in more expressive ontology languages,
such as OWL
ex:georgeClooney ex:starsIn ex:bradPitt.
ex:bradPitt rdf:type ex:Movie.

Reification (1)
Statements about other RDF statements
Ability to treat an RDF statement as an RDF resource
◦ Make assertions about that statement
Becomes:
<http://www.example.org/person/1> foaf:name "Brad Pitt " .
<http://www.example.org/statement/5> a rdf:Statement;
rdf:subject <http://www.example.org/person/1>;
rdf:predicate foaf:name;
rdf:object "Brad Pitt".

Reification (2)
Useful when referring to an RDF triple in order to
◦ Describe properties that apply to it
◦ e.g. provenance or trust
◦ E.g. assign a trust level of 0.8
<http://www.example.org/statement/5> ex:hasTrust "0.8"^^xsd:float.

Classes (1)
rdfs:Resource
◦ All things described by RDF are instances of the class rdfs:Resource
◦ All classes are subclasses of this class, which is an instance of
rdfs:Class
rdfs:Literal
◦ The class of all literals
◦ An instance of rdfs:Class
◦ Literals are represented as strings but they can be of any XSD
datatype

Classes (2)
rdfs:langString
◦ The class of language-tagged string values
◦ It is a subclass of rdfs:Literal and an instance of rdfs:Datatype
◦ Example: "foo"@en
rdfs:Class
◦ The class of all classes, i.e. the class of all resources that are RDF
classes

Classes (3)
rdfs:Datatype
◦ The class of all the data types
◦ An instance but also a subclass of rdfs:Class
◦ Every instance of rdfs:Datatype is also a subclass of rdfs:Literal
rdf:HTML
◦ The class of HTML literal values
◦ An instance of rdfs:Datatype
◦ A subclass of rdfs:Literal

Classes (4)
rdf:XMLLiteral
◦ The class of XML literal values
◦ An instance of rdfs:Datatype
◦ A subclass of rdfs:Literal
rdf:Property
◦ The class of all RDF properties

Properties (1)
rdfs:domain
◦ Declares the domain of a property P
◦ The class of all the resources that can appear as S in a triple (S, P, O)
rdfs:range
◦ Declares the range of a property P
◦ The class of all the resources that can appear as O in a triple (S, P, O)

Properties (2)
rdf:type
◦ A property that is used to state that a resource is an instance of a
class
rdfs:label
◦ A property that provides a human-readable version of a resource’s
name

Properties (3)
rdfs:comment
◦ A property that provides a human-readable description of a
resource
rdfs:subClassOf
◦ Corresponds a class to one of its superclasses
◦ A class can have more than one superclasses
rdfs:subPropertyOf
◦ Corresponds a property to one of its superproperties
◦ A property can have more than one superproperties

Container Classes and Properties (1)
rdfs:Container
◦ Superclass of all classes that can contain instances such as rdf:Bag, rdf:Seq and
rdf:Alt
rdfs:member
◦ Superproperty to all the properties that declare that a resource belongs to a
class that can contain instances (container)
rdfs:ContainerMembershipProperty
◦ A property that is used in declaring that a resource is a member of a container
◦ Every instance is a subproperty of rdfs:member

Container Classes and Properties (2)
rdf:Bag
◦ The class of unordered containers
◦ A subclass of rdfs:Container
rdf:Seq
◦ The class of ordered containers
rdf:Alt
◦ The class of containers of alternatives

Collections (1)
rdf:List
◦ The class of RDF Lists
◦ Can be used to build descriptions of lists and other list-like
structures
rdf:nil
◦ An instance of rdf:List that is an empty rdf:List

Collections (2)
rdf:first
◦ The first item in the subject RDF list
◦ An instance of rdf:Property
rdf:rest
◦ The rest of the subject RDF list after the first item
rdf:_1, rdf:_2, rdf:_3, etc.
◦ A sub-property of rdfs:member
◦ An instance of the class rdfs:ContainerMembershipProperty

Reification (1)
rdf:Statement
◦ The class of RDF statements
rdf:subject
◦ The subject of the subject RDF statement

Reification (2)
rdf:predicate
◦ The predicate of the subject RDF statement
rdf:object
◦ The object of the subject RDF statement

Utility Properties
rdf:value
◦ Idiomatic property used for describing structured values
rdfs:seeAlso
◦ A property that indicates a resource that might provide additional information
about the subject resource
rdfs:isDefinedBy
◦ A property that indicates a resource defining the subject resource
◦ The defining resource may be an RDF vocabulary in which the subject
resource is described

Outline
Introduction
RDF and RDF Schema
Description Logics
Other technologies
Ontologies
Datasets

Ontologies Based on Description Logics
Language roots are in Description Logics (DL)
DAML, OIL, and DAML + OIL
OWL and OWL 2 latest results in this direction
Formal semantics
Syntactically compatible to the RDF serializations
◦ Ontologies in OWL can be queried in the same approach as in RDF
graphs

Description Logics (1)
Offers the language for the description and manipulation of
independent individuals, roles, and concepts
There can be many DL languages
◦ Describe the world using formulas
Formulas constructed using
◦ Sets of concepts, roles, individuals
◦ Constructors, e.g. intersection ∧, union ∨, exists ∃, for each ∀

Variables in DL can represent arbitrary world objects
E.g. a DL formula can declare that a number x, greater than
zero exists:
◦ ∃x:greaterThan(x; 0)
Adding more constructors to the basic DL language increases
expressiveness
◦ Possible to describe more complex concepts

E.g. concept conjunction (C ∪ D)
◦ Parent = Father ∪ Mother
Expressiveness used in describing the world varies according
to the subset of the language that is used
Differentiation affects
◦ World description capabilities
◦ Behavior and performance of processing algorithms

The Web Ontology Language (1)
OWL language is directly related to DL
◦ Different “flavors” correspond to different DL language subsets
Successor of DAML+OIL
◦ Creation of the OWL language officially begins with the initiation of the DAML
project
◦ DAML, combined to OIL led to the creation of DAML + OIL
◦ An extension of RDFS
◦ OWL is the successor of DAML + OIL
W3C recommendation, currently in version 2

Designed in order to allow applications to process the
information content itself instead of simply presenting the
information
The goal is to provide a schema that will be compatible both
to the Semantic Web and the World Wide Web architecture
Makes information more machine- and human- processable

First version comprised three flavors
◦ Lite, DL, Full
OWL Lite
◦ Designed keeping in mind that it had to resemble RDFS
OWL DL
◦ Guaranteed that all reasoning procedures are finite and return a result
OWL Full
◦ The whole wealth and expressiveness of the language
◦ Reasoning is not guaranteed to be finite
◦ Even for small declaration sets

An OWL ontology may also comprise declarations from RDF
and RDFS
◦ E.g. rdfs:subClassOf, rdfs:range, rdf:resource
OWL uses them and relies on them in order to model its
concepts
rdfs:Resource
owl:Class
rdfs:Class rdf:Property
owl:DataProperty owl:ObjectProperty owl:AnnotationProperty

OWL 2
Very similar overall structure to the first version of OWL
Backwards compatible
◦ Every OWL 1 ontology is also an OWL 2 ontology

OWL 2 Additional Features (1)
Additional property and qualified cardinality constructors
◦ Minimum, maximum or exact qualified cardinality restrictions,
object and data properties
◦ E.g. ObjectMinCardinality, DataMaxCardinality
Property chains
◦ Properties as a composition of other properties
◦ E.g. ObjectPropertyChain in SubObjectPropertyOf

Extended datatype support
◦ Support the definition of subsets of datatypes (e.g. integers and
strings)
◦ E.g. state that every person has an age, which is of type integer, and restrict
the range of that datatype value
Simple metamodeling
◦ Relaxed separation between the names of, e.g. classes and
individuals
◦ Allow different uses of the same term

Extended annotations
◦ Allow annotations of axioms
◦ E.g. who asserted an axiom or when
Extra syntactic sugar
◦ Easier to write common patterns
◦ Without changing language expressiveness, semantics, or complexity

OWL 2 Profiles (1)
OWL 2 EL
◦ Polynomial time algorithms for all standard reasoning tasks
◦ Suitable for very large ontologies
◦ Trades expressive power for performance guarantees

OWL 2 Profiles (2)
OWL 2 QL
◦ Conjunctive queries are answered in LOGSPACE using standard
relational database technology
◦ Suitable for
◦ Relatively lightweight ontologies with large numbers of individuals
◦ Useful or necessary to access the data via relational queries (e.g. SQL)

OWL 2 Profiles (3)
OWL 2 RL
◦ Enables polynomial time reasoning algorithms using rule-extended
database technologies operating directly on RDF triples
◦ Suitable for
◦ Relatively lightweight ontologies with large numbers of individuals
◦ Necessary to operate directly on data in the form of RDF triples

Manchester OWL syntax
A user-friendly syntax for OWL 2 descriptions
Class: VegetarianPizza
EquivalentTo:
Pizza and
not (hasTopping some FishTopping) and
not (hasTopping some MeatTopping)
DisjointWith:
NonVegetarianPizza

Outline
Introduction
RDF and RDF Schema
Description Logics
Other technologies
Ontologies
Datasets

Querying the Semantic Web with SPARQL
SPARQL
◦ Is for RDF what SQL is for relational databases
SPARQL family of recommendations
◦ SPARQL Update
◦ SPARQL 1.1 Protocol
◦ Graph Store HTTP Protocol
◦ SPARQL 1.1 entailment regimes
◦ Specifications about the serialization of query results
◦ In JSON, CSV and TSV, and XML

SELECT Queries (1)
Return a set of bindings of variables to RDF terms
Binding
◦ A mapping from a variable to a URI or an RDF literal
Triple pattern
◦ Resembles an RDF triple
◦ May also contain variables in one or more positions

SELECT Queries (2)
An RDF triple matches a triple pattern
◦ There is an appropriate substitution of variables with RDF terms
that makes the triple pattern and the RDF triple equivalent
◦ Example:
matches
ex:bradPitt rdf:type ex:Actor.
?x rdf:type ex:Actor

SELECT Queries (3)
A set of triple patterns build a basic graph pattern
◦ The heart of a SPARQL SELECT query
SELECT ?x ?date
WHERE {
?x rdf:type ex:Actor .
?x ex:bornIn ?date
}

SELECT Queries (4)
Graph example
SPARQL query solution
?x ?date
ex:bradPitt "1963"^^xsd:gYear
ex:angelinaJolie "1975"^^xsd:gYear
ex:bradPitt rdf:type ex:Actor;
ex:bornIn "1963"^^xsd:gYear.
ex:angelinaJolie rdf:type ex:Actor;
ex:jenniferAniston rdf:type ex:Actor.

The OPTIONAL keyword
Resource ex:jenniferAniston not included in the results
◦ No RDF triple matches the second triple pattern
Use of the OPTIONAL keyword
◦ Retrieves all actors and actresses and get their birthdate only if it is specified
in the RDF graph
?x ?date
ex:bradPitt "1963"^^xsd:gYear
ex:angelinaJolie "1975"^^xsd:gYear
ex:jenniferAniston
SELECT ?x ?date
WHERE {
OPTIONAL {?x ex:bornIn ?date}
}

The UNION Keyword
Specify alternative graph patterns
Search for resources that are of type ex:Actor or ex:Director,
including cases where a resource may belong to both
classes
SELECT ?x
WHERE {
{?x rdf:type ex:Actor}
UNION
{?x rdf:type ex:Director}
}

The FILTER Keyword
Set a condition that limits the result of a SPARQL query
Return all actors that were known to be born before 1975
and their corresponding birth date
SELECT ?x ?date
WHERE{
?x ex:bornIn ?date .
FILTER(?date < "1975"^^xsd:gYear)
}

ORDER BY and LIMIT Keywords
SELECT ?x ?date
WHERE {
?x ex:bornIn ?date .
}
ORDER BY DESC(?date)
LIMIT 10

CONSTRUCT Queries
Generate an RDF graph based on a graph template using the
solutions of a SELECT query
CONSTRUCT {?x rdf:type ex:HighlyPaidActor}
WHERE {
?x ex:hasSalary ?salary .
FILTER (?salary > 1000000)
}

ASK Queries
Respond with a boolean
Whether a graph pattern is satisfied by a subgraph of the
considered RDF graph
Return true if the RDF graph contains an actor named "Cate
Blanchett"
ASK {
?x foaf:name "Cate Blanchett" .
}

DESCRIBE Queries
Return a set of RDF statements that contain data about a
given resource
Exact structure of the returned RDF graph depends on the
specific SPARQL engine
DESCRIBE <http://www.example.org/bradPitt> .

Outline
Introduction
RDF and RDF Schema
Description Logics
Other technologies
Ontologies
Datasets

Mapping Relational Data to RDF (1)
Lack of RDF data volumes → Slow uptake of the Semantic Web vision
Large part of available data is stored in relational databases
Several methods and tools were developed for the translation of
relational data to RDF
◦ Each one with its own set of features and, unfortunately, its own mapping
language
The need for the reuse of RDB-to-RDF mappings led to the creation
of R2RML
◦ A standard formalism by W3C for expressing such mappings

Mapping Relational Data to RDF (2)
R2RML: RDB to RDF Mapping Language
◦ Provides a vocabulary for the definition of RDF views over
relational schemas
◦ Is database vendor-agnostic
An R2RML mapping is an RDF graph: an R2RML mapping
graph
◦ In Turtle syntax

R2RML Overview
More features
◦ Organization of generated triples in named graphs
◦ Definition of blank nodes
◦ Specification of a generated literal’s language
RefObjectMap
TriplesMap
PredicateObjectMap
SubjectMap
Generated Triples
Generated Output Dataset
GraphMap
Join
ObjectMap
PredicateMap
LogicalTable

R2RML (1)
An R2RML mapping
◦ Associates relational views with functions that generate RDF terms
Logical tables
◦ Relations or (custom) views defined in the relational schema
Term maps
◦ RDF generating functions
◦ Distinguished according to the position of the RDF term in the
generated triple
◦ Subject, predicate, and object maps

R2RML (2)
Triples maps
◦ Functions that map relational data to a set of RDF triples
◦ Groups of term maps
◦ R2RML mappings contain one or more triples maps
Graph maps
◦ Generated RDF triples can be organized into named graphs

R2RML Example (1)
A relational instance
FILM
ID Title Year Director
1 The Hunger Games 2012 1
ACTOR
ID Name BirthYear BirthLocation
1 Jennifer Lawrence 1990 Louisville, KY
2 Josh Hutcherson 1992 Union, KY
FILM2ACTOR
FilmID ActorID
1 1
1 2
DIRECTOR
ID Name BirthYear
1 Gary Ross 1956

R2RML Example (2)
An R2RML
triples map
@prefix rr: <http://www.w3.org/ns/r2rml#>.
@prefix ex: <http://www.example.org/>.
@prefix dc: <http://purl.org/dc/terms/>.
<#TriplesMap1>
rr:logicalTable [ rr:tableName "FILM" ];
rr:subjectMap [
rr:template "http://data.example.org/film/{ID}";
rr:class ex:Movie;
];
rr:predicateObjectMap [
rr:predicate dc:title;
rr:objectMap [ rr:column "Title" ];
];
rr:predicate ex:releasedIn;
rr:objectMap [ rr:column "Year";
rr:datatype xsd:gYear;];
].

R2RML Example (3)
The result (in Turtle)
Note the reuse of terms from external ontologies
◦ E.g. dc:title from Dublin Core
<http://data.example.org/film/1> a ex:Movie;
dc:title "The Hunger Games";
ex:releasedIn "2012"^^xsd:gYear.

R2RML Example (4)
The R2RML mapping graph of the example
◦ Specifies the generation of a set of RDF triples for every row of the
FILM relation
◦ Is the simplest possible
◦ Contains only one triples map
Every triples map must have exactly
◦ One logical table
◦ One subject map
◦ One or more predicate-object maps

R2RML Example (5)
A logical table represents an SQL result set
◦ Each row gives rise to an RDF triple
◦ Or quad in the general case, when named graphs are used
A subject map is simply a term map that produces the
subject of an RDF triple

R2RML Example (6)
Term maps
◦ Template-valued (rr:template)
◦ The subject map of the example
◦ Constant-valued (rr:predicate)
◦ The predicate map of the example
◦ Column-valued (rr:column)
◦ The object map of the example
Every generated resource will be an instance of ex:Movie
◦ Use of rr:class

R2RML Example (7)
Subject maps
◦ Generate subjects
Predicate-object maps
◦ Generate pairs of predicates and objects
◦ Contain at least one predicate map
◦ Contain at least one object map
Typed Literals
◦ Use of rr:datatype
◦ The second object map of the example specifies that the datatype of the RDF
literal that will be generated will be xsd:gYear

Foreign Keys (1)
Add another predicate-
object map that operates
on the DIRECTOR relation
Specify the generation of
three RDF triples per row
of the DIRECTOR relation
@prefix foaf: <http://xmlns.com/foaf/0.1/>.
<#TriplesMap2>
rr:logicalTable [ rr:tableName "DIRECTOR" ];
rr:subjectMap [
rr:template "http://data.example.org/director/{ID}";
rr:class ex:Director;
];
rr:predicate foaf:name;
rr:objectMap [ rr:column "Name" ];
];
rr:predicate ex:bornIn;
rr:objectMap [ rr:column "BirthYear";
].

Foreign Keys (2)
Add another predicate-object
map, the referencing object
map
Link entities described in
separate tables
Exploit the foreign key
relationship of FILM.Director
and DIRECTOR.ID
<#TriplesMap1>
rr:logicalTable [ rr:tableName "FILM" ];
rr:subjectMap [
rr:template "http://data.example.org/film/{ID}";
rr:class ex:Movie;
];
rr:predicate dc:title;
rr:objectMap [ rr:column "Title" ];
];
rr:predicate ex:releasedIn;
rr:objectMap [ rr:column "Year";
rr:predicateObjectMap[
rr:predicate ex:directedBy;
rr:objectMap[
rr:parentTriplesMap <#TriplesMap2>;
rr:joinCondition[
rr:child "Director";
rr:parent "ID";
];
];
].

Foreign Keys (3)
TriplesMap1 now contains a referencing object map
◦ Responsible for the generation of the object of a triple
◦ Referencing object maps are a special case of object maps
◦ Follows the generation rules of the subject map of another triples
map
◦ Called parent triples map
◦ Join conditions are also specified in order to select the appropriate
row of the logical table of the parent triples map

Foreign Keys (4)
Not necessary for this foreign key relationship to be explicitly defined
as a constraint in the relational schema
R2RML is flexible enough to allow the linkage of any column set
among different relations
TriplesMap1 result
TriplesMap2 result
<http://data.example.org/film/1> ex:directedBy <http://data.example.org/director/1>.
<http://data.example.org/director/1> a ex:Director;
foaf:name "Gary Ross";

Custom Views (1)
R2RML view defined via the
rr:sqlQuery property
Used just as the native logical
tables
Could also have been defined as
an SQL view in the underlying
database system
◦ Not always feasible/desirable
Produced result set must not
contain columns with the same
name
@prefix dc: <http://purl.org/dc/terms/>.
<#TriplesMap3>
rr:logicalTable [ rr:sqlQuery """
SELECT ACTOR.ID AS ActorId, ACTOR.Name AS
ActorName, ACTOR.BirthYear AS ActorBirth, FILM.ID AS FilmId FROM
ACTOR, FILM, FILM2ACTOR WHERE FILM.ID=FILM2ACTOR.FilmID AND
ACTOR.ID=FILM2ACTOR.ActorID; """];
rr:subjectMap [
rr:template "http://data.example.org/actor/{ActorId}";
rr:class ex:Actor;
];
rr:predicate foaf:name;
rr:objectMap [ rr:column "ActorName" ];
];
rr:predicate ex:bornIn;
rr:objectMap [ rr:column "ActorBirth";
].
rr:predicate ex:starsIn;
rr:objectMap [ rr:template
"http://data.example.org/film/{ID}";
];
].

Custom Views (2)
TriplesMap3 generates the following triples
<http://data.example.org/actor/1> a ex:Actor;
foaf:name "Jennifer Lawrence";
ex:bornIn "1990"^^xsd:gYear;
ex:starsIn<http://data.example.org/film/1>.
<http://data.example.org/actor/2> a ex:Actor;
foaf:name "Josh Hutcherson";
ex:bornIn "1992"^^xsd:gYear;
ex:starsIn <http://data.example.org/film/1>.

Direct Mapping (1)
A default strategy for translating a relational instance to an
RDF graph
A trivial transformation strategy
A recommendation by W3C since 2012
Defines a standard URI generation policy for all kinds of
relations

Direct Mapping (2)
Maps every relation to a new ontology class and every relation
row to an instance of the respective class
The generated RDF graph essentially mirrors the relational
structure
Can be useful as a starting point for mapping authors who can
then augment and customize it accordingly using R2RML

Outline
Introduction
RDF and RDF Schema
Description Logics
Other technologies
Ontologies
Datasets

POWDER – Protocol for Web Description Resources (1)
XML-based protocol
Allows provision of information about RDF resources
A POWDER document contains
◦ Its own provenance information
◦ Information about its creator, when it was created, etc.
◦ A list of description resources

POWDER – Protocol for Web Description Resources (2)
Description resource
◦ Contains a set of property-value pairs in RDF
◦ For a given resource or group of resources
Allows for quick annotation of large amounts of content with
metadata
Ideal for scenarios involving personalized delivery of content,
trust control and semantic annotation

RIF – Rule Interchange Format
An extensible framework for the representation of rule dialects
A W3C recommendation
A family of specifications
◦ Three rule dialects
◦ RIF-BLD (Basic Logic Dialect)
◦ RIF-Core and
◦ RIF-PRD (Production Rule Dialect)
◦ RIF-FLD (Framework for Logic Dialects)
◦ Allows the definition of other rule dialects
◦ Specifies the interface of rules expressed in a logic-based RIF dialect with RDF and OWL

SPIN – SPARQL Inferencing Notation
RIF uptake not as massive as expected
◦ Complexity, lack of supporting tools
SPIN
◦ SPARQL-based
◦ Production rules
Links RDFS and OWL classes with constraint checks and
inference rules
◦ Similar to class behavior in object-oriented languages

GRDDL – Gleaning Resource Descriptions from Dialects of Languages
Mechanisms that specify how to construct RDF
representations
◦ Can be applied to XHTML and XML documents
◦ Defines XSLT transformations that generate RDF/XML
Is the standard way of converting an XML document to RDF
Familiarity with XSLT required

LDP – Linked Data Platform
Best practices and guidelines for organizing and accessing LDP resources
◦ Via HTTP RESTful services
◦ Resources can be RDF or non-RDF
◦ Specifies the expected behavior of a Linked Data server when it receives HTTP requests
◦ Creation, deletion, update
◦ Defines the concept of a Linked Data Platform Container
◦ A collection of resources (usually homogeneous)
LDP specification expected to establish a standard behavior for Linked Data
systems
Example: Apache Marmotta

Outline
Introduction
RDF and RDF Schema
Description Logics
Other technologies
Ontologies
Datasets

Ontologies and Datasets (1)
Standards and technologies (e.g. RDF and OWL)
◦ Machine-consumable serializations
◦ Syntactical groundwork
◦ Assignment of meaning to resources and their relationships
Vocabularies
◦ (Re)used in order for the described information to be commonly,
unambiguously interpreted and understood
◦ Reused by various data producers
◦ Make data semantically interoperable

Ontologies and Datasets (2)
Datasets
◦ Collections of facts involving specific individual entities and
ontologies, which provide an abstract, high-level, terminological
description of a domain, containing axioms that hold for every
individual
A great number of ontologies exist nowadays
◦ Subject domains: e.g. geographical, business, life sciences,
literature, media

Ontology search engines and specialized directories
Schemapedia
Watson
Swoogle
Linked Open Vocabularies (LOV)
◦ The most accurate and comprehensive source of ontologies used
in the Linked Data cloud
◦ Vocabularies described by appropriate metadata and classified to
domain spaces
◦ Full-text search enabled at vocabulary and element level

DC – Dublin Core (1)
A minimal metadata element set
Mainly used for the description of web resources
DC vocabulary
◦ Available as an RDFS ontology
◦ Contains classes and properties
◦ E.g. agent, bibliographic resource, creator, title, publisher, description

DC – Dublin Core (2)
DC ontology partitioned in two sets
◦ Simple DC
◦ Contains 15 core properties
◦ Qualified DC
◦ Contains all the classes and the rest of the properties (specializations of
those in simple DC)

FOAF – Friend-Of-A-Friend
One of the first lightweight ontologies being developed
since the first years of the Semantic Web
Describes human social networks
Classes such as Person, Agent or Organization
Properties denoting personal details and relationships with
other persons and entities

SKOS – Simple Knowledge Organization System (1)
A fundamental vocabulary in the Linked Data ecosystem
A W3C recommendation since 2009
Contains terms for organizing knowledge
◦ In the form of taxonomies, thesauri, and concept hierarchies
Defines concept schemes as sets of concepts
◦ Concept schemes can relate to each other through
“broader/narrower than” or equivalence relationships

SKOS – Simple Knowledge Organization System (2)
Defined as an OWL Full ontology
◦ Has its own semantics
◦ Its own set of entailment rules distinct from those of RDFS and OWL
Widely used in the library and information science domain

VoID – Vocabulary of Interlinked Datasets
Metadata for RDF datasets
◦ General metadata
◦ E.g. Name, description, creator
◦ Information on the ways that this dataset can be accessed
◦ E.g. as a data dump, via a SPARQL endpoint
◦ Structural information
◦ E.g. the set of vocabularies that are used or the pattern of a typical URI
◦ Information on the links
◦ E.g. the number and target datasets
Aid users and machines to decide whether a dataset is suitable for
their needs

SIOC – Semantically-Interlinked Online Communities
An OWL ontology
Describes online communities
◦ E.g. forums, blogs, mailing lists
Main classes
◦ E.g. forum, post, event, group, user
Properties
◦ Attributes of those classes
◦ E.g. the topic or the number of views of a post

Good Relations
Describes online commercial offerings
◦ E.g. Product and business descriptions, pricing and delivery
methods
Great impact in real-life applications
◦ Adoption by several online retailers and search engines
Search engines able to interpret product metadata
expressed in the Good Relations vocabulary
◦ Offer results better tailored to the needs of end users

Outline
Introduction
RDF and RDF Schema
Description Logics
Other technologies
Ontologies
Datasets

Datasets
Several “thematic neighborhoods” in the Linked Data cloud
◦ E.g. government, media, geography, life sciences
Single-domain and cross-domain datasets

DBpedia (1)
The RDF version of Wikipedia
◦ Perhaps the most popular RDF dataset in the LOD cloud
◦ A large number of incoming links
A cross-domain dataset
◦ Assigns a URI to every resource described by a Wikipedia article
◦ Produces structured information by mining information from
Wikipedia infoboxes

DBpedia (2)
A multilingual dataset
◦ Transforms various language editions of Wikipedia
The DBpedia dataset offered as data dump and through a
SPARQL endpoint
A “live” version available, updated whenever a Wikipedia
page is updated

Freebase
Openly-licensed, structured dataset, also available as an RDF
graph
Tens of millions of concepts, types and properties
Can be edited by anyone
Gathers information from several structured sources and free
text
Offers an API for developers
Linked to DBpedia through incoming and outgoing links

GeoNames
An open geographical database
Millions of geographical places
Can be edited by anyone
Geographical information as the context of descriptions and
facts
Omnipresent in the Linked Data cloud
Several incoming links from other datasets

Lexvo
Central dataset for the linguistic Linked Data Cloud
Provides identifiers for thousands of languages
URIs for terms in every language
Identifiers for language characters

Technical Background

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