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RDFS
A little semantics goes a
long way
Mariano Rodriguez-Muro,
Free University of Bozen-Bolzano

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Disclaimer
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License
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A few examples from these slides has been taken from
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This work is licensed under a
Creative Commons Attribution-Share Alike 3.0 License
(http://creativecommons.org/licenses/by-sa/3.0/)
Semantic Web for the working Ontologist. Chapter 6.
Some of the slides on the use of taxonomies are based on:
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http://info.earley.com/webinar-replay-business-value-taxonomy-aug2012

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Reading material
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Semantic Web for the working Ontologist. Chapter 6
http://proquest.safaribooksonline.com/book/-/9780123859655

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Motivation
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Ontologies
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RDFS ontologies
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Overview
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Annotations

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Motivation

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Motivation
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Motivations for semantic
technology
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Making the web machine
understandable
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Expressing knowledge
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However, points 2 and 3 are not
possible with the technologies
seen so far
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RDF doesn’t define
vocabularies, and
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Different datasets may use
different URI’s to represent the
same kind of data
Reasoning with knowledge

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Motivation
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Agreement in RDF graphs concerns only the data model and the use
of URI as identifiers
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No semantics!

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Ontologies and Ontology
Languages

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What is an ontology language?
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Specification of valid “axioms”
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Specifications of vocabularies with “predefined” meaning in axioms
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Informal: Topic Maps, UML diagrams
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Formal examples: Predicate Logic, First Order Logic
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Semantic Web examples:
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RDFS
SWRL,
OWL
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Different languages have different expressive power
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Axioms allow to produce “inferences”
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The more expressive power, more complex and costly the inferences

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What is an ontology?
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Collections of “axioms”
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Describe the meaning of the vocabulary of a domain (e.g., an
area of expertise)
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Expressed in an Ontology Language
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Valuable on their own as knowledge repositories
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In combination with data valuable to implement complex
behavior with little or no coding

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Example: Schema.org
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Schema.org IS a simple ontology
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Organizes terms in hierarchies with
predefined meaning
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The language is a variation of RDFS

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RDFS
Introduction by example

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RDFS
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W3C standard for an ontology language
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RDFS introduces resources (URIs) with a predefined meaning
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Inference engines that support RDFS allow to take that
meaning into account
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RDFS inferences extend the RDF graph by means of inference
and hence, affect query answering
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RDFS is very simple compared to SWRL or OWL, however, it
is very useful in many context, allowing for increased
productivity, easy data integration and interesting AI
applications

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Building blocks
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New namespace rdfs:
<http://www.w3.org/2000/01/rdf-schema#>
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New categories:
Commonly,
Class names
are nouns
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Classes, resources that share something in common, allow us to
group things together. For example, Employee, Company.
Resources that identify classes have rdf:type rdfs:Class
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Instances, resources that are “members” of a class

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Building blocks
Resources can belong to multiple classes

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Building blocks (cont.)
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Properties: Resources used as a predicate in statements
Commonly, Property names are
multiple words, expressing direction
and in camel-casing

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RDFS Ontologies
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RDFS Axioms
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Are RDF triples!
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RDFS ontology is an RDF graph!
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An RDF graph may have a subgraph expressed in RDFS
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We call the RDFS axioms/triples the Tbox of the ontology
(terminological information, predefined meaning)
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The rest is the Abox of the ontology (plain data, no predefined
meaning)

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Type propagation
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RDFS vocabulary:
rdfs:subClassOf
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Key notions
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sub class (on the left)
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super class (on the right)
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Intuitive meaning, if :mariano is
an instance of subclass it is
also an instance of superclass
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Formal meaning: subsets
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Inference: type propagation
Similar to inheritance
in Object Oriented
formalisms

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Type propagation
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RDFS vocabulary:
rdfs:subClassOf
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Key notions
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sub class (on the left)
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super class (on the right)
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Intuitive meaning, if :mariano is
an instance of subclass it is
also an instance of superclass
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Formal meaning: subsets
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Inference: type propagation
Similar to inheritance
in Object Oriented
formalisms

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Relation propagation
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RDFS vocabulary:
rdfs:subPropertyOf
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Key notions
 sub property(on the left)
 super property(on the right)
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Intuitive meaning, if (x,y) are connected with subproperty
they are also connected with superproperty
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Formal meaning: subsets (of binary tuples)
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Inference: relationship propagation

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Relation propagation
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RDFS vocabulary:
rdfs:subPropertyOf
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Key notions
 sub property(on the left)
 super property(on the right)
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Intuitive meaning, if (x,y) are connected with subproperty
they are also connected with superproperty
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Formal meaning: subsets (of binary tuples)
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Inference: relationship propagation

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Types by usage
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RDFS vocabulary:
rdfs:domain, rdfs:range
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Key notions
 domain of a triple:
the subject
 range of a triple:
the object
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:p rdfs:domain :C > the domain of any
triple where :p is the predicate is an
instance of :C
(similar for rdfs:range)
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Formal meaning:
if (x,y) in P, then x in :C
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Inference: type assignment by property
usage

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Types by usage
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RDFS vocabulary:
rdfs:domain, rdfs:range
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Key notions
 domain of a triple:
the subject
 range of a triple:
the object
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:p rdfs:domain :C > the domain of any
triple where :p is the predicate is an
instance of :C
(similar for rdfs:range)
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Formal meaning:
if (x,y) in P, then x in :C
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Inference: type assignment by property
usage

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Interactions
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All inferences interact to allow
complex behavior

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Interactions
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All inferences interact to allow
complex behavior

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Set intersection
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Proper set intersection is not
possible in RDFS
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However, expressing necessary
membership to multiple classes
is possible, i.e., A subset B AND
C
A rdfs:subClassOf B
A rdfs:subClassOf C
consider
x rdf:type A

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Set intersection

Proper set intersection is not
possible in RDFS

However, expressing necessary
membership to multiple classes
is possible, i.e., A subset B AND
C
A rdfs:subClassOf B
A rdfs:subClassOf C
consider
x rdf:type A

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Set intersection

Proper set intersection is not
possible in RDFS

However, expressing necessary
membership to multiple classes
is possible, i.e., A subset B AND
C
A rdfs:subClassOf B
A rdfs:subClassOf C
consider
x rdf:type A
One direction
only!

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Set intersection
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Similar for roles

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Set intersection
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Similar for roles

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Set union
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Proper set union is not possible
in RDFS
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However, A OR B subsetOf C
B rdfs:subClassOf A
C rdfs:subClassOf A
consider
x rdf:type B
or
x rdf:type C

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Set union
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Proper set union is not possible
in RDFS
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However, A OR B subsetOf C
B rdfs:subClassOf A
C rdfs:subClassOf A
consider
x rdf:type B
or
x rdf:type C

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Set union
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For roles. Aligning to a global
vocabulary

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Set union
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For roles. Aligning to a global
vocabulary

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Equivalence
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Merging vocabularies
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To account for same use of
different terms (classes or
properties)
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For classes or proeperties

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Equivalence
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Merging vocabularies
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To account for same use of
different terms (classes or
properties)
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For classes or proeperties

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Last notes on RDFS axioms
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Main new vocabulary:
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rdfs:subClassOf
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rdfs:subPropertyOf
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rdfs:domain
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rdfs:range
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Different from CONSTRAINTS, missing triples are NOT a
violation
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Allow to infer new information
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Allows to implement system behavior!

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Open lists revisited
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RDFS also facilitates access to Lists
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Elements of lists are a possibly
infinite set of elements of the form
rdf:_1, rdf:_2, etc
RDFS facilitates this by enforcing that:
if x rdfs:_1 y
then x rdfs:member b
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Access difficult in practice

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Open lists revisited
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RDFS also facilitates access to Lists
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Elements of lists are a possibly
infinite set of elements of the form
rdf:_1, rdf:_2, etc
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Access difficult in practice
RDFS facilitates this by enforcing that:
if x rdfs:_1 y
then x rdfs:member b
More detail on this on the lecture about
RDFS semantics

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Axiomatic triples
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RDFS enforces certain facts to be always true
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These facts are statements (triples)
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Referred as Axiomatic triples
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Listed in http://www.w3.org/TR/rdf-mt/
More detail on this on the lecture about
RDFS semantics

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RDFS Semantic Conditions
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Every resource x
x rdf:type rdfs:Resource
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Every literal x
x rdf:type rdfs:Literal
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… etc
More detail on this on the lecture about
RDFS semantics

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Last notes on RDFS axioms
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Main new vocabulary (not the only one):
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rdfs:subClassOf
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rdfs:subPropertyOf
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rdfs:domain
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rdfs:range
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Different from CONSTRAINTS, missing triples are NOT a
violation
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Allow to infer new information
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Allows to implement system behavior!

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Hands on examples
From Semantic Web for the Working Ontologist

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Automatic classification of
employees (part 1)
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Transform into an RDF representation
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Automatically catalog objects as Employees, and as Active
employees, Suspended employees and Ex-employees
using a minimal set of “axioms”
<ID>
Project
Assignment
Absent
Until
Termination Date
22
24
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34
24
Dec 23, 2012
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73
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Jun 4, 2010
Employee table. Primary key: 10
Active employees are assigned to projects

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Automatic classification of
employees (part 2)
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Transform into an RDF representation
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Automatically catalog objects as Employees as managers
<ID>
Project Name
<Manager>
24
Project-x
34
25
Project Mayhem
22
Project table. Primary key: ID
Foreign key <Manager> to Employee table

+ Align vocabularies
• Align corresponding properties
using RDFS
• Align with FOAF vocabulary
(when possible) using RDFS (use
foaf:name, foaf:homepage)

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Annotations

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Annotations
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URI’s are not readable
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Readable information (comments, names, etc.) can be stored
using properties, but
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Property names are not standard, however, we could like some
standard names for “human oriented information”
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RDFS defines:
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rdfs:label
A readable name for a resource
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rdfs:comment
Human focused comments
These are properties
So, subPropertyOf can be used
with them

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Redirection
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Redirecting to location of documents (RDF) with additional
information about a subject
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No formal semantics
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RDFS provides:
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rdfs:seeAlso. Additional information
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rdfs:definedBy. Authority information, primary source.
Recall the semantic web
idea, linked databases
These are properties
So, subPropertyOf can be used
with them