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Publishing and Using
Linked Open Data

            Richard J. Urban, Ph.D.
            School of Library and Information Studies
            Florida State University
            rurban@fsu.edu
            @musebrarian



                         #lod4h
January 8, 2013
Tuesday’s Schedule
•   9:00 am- 10:30 am     Class Session: Information Modeling Fundamentals

•   10:30-10:45 am   break

•   10:45- Noon      Class Session: Linked Data Models

•   Noon- 1:00 pm    Lunch (on your own)

•   1:00- 2:45 pm    Class Session: Searching Linked Data

•   2:45- 3:00 pm    break

•   3:00-5:00 pm     Class Session: Identifying Linked Data for Participant
    Projects

•   5:30-7:00 pm        DHWI Public DH: API Workshop
                        Registered Attendees Only



                                                #lod4h
Humanities Data Models

• What are the models that we currently
  use?
  – Document-based models
  – Database Models
  – Probabilistic/Statistical Models (NLP)




                          #lod4h
How RDF is Different

• Based in knowledge representation
  languages (artificial intelligence)
• Grounded in formal predicate
  logic/description logics
  • 20th Century developments in the philosophy of
    language (Leibnitz, Russell, Wittgenstein, Peirce,
    Frege, Kripke, Tarski, etc.)
• Intended to enable intelligent reasoning


                              #lod4h
http://en.wikipedia.org/wiki/Semantic_Web_Stack

        #lod4h
Model-Theoretic Semantics
1. use formal structures and rules to ensure
   that every legitimate language expression
   has a well-defined meaning;
2. define what is means for a statement in a
   language to be true under a particular
   interpretation;
3. allow us to formalize the intuitive notion of
   logical consequence, that is, of one
   statement 'following logically' from others;
   and…
4. provide a basis for implementing automated
   reasoning via an appropriate proof theory.
                         #lod4h
Interpretations
• The basic intuition of model-theoretic
  semantics is that asserting a sentence makes
  a claim about the world: it is another way of
  saying that the world is, in fact, so arranged
  as to be an interpretation which makes the
  sentence true. In other words, an assertion
  amounts to stating a constraint on the
  possible ways the world might be.

  – Anyone can say anything about anything.
  – But…you need to tell me what your interpretation
    is so I can evaluate it.

                           #lod4h
Entailment

• A entails B
• A is true
• Therefore B is
  true




                   #lod4h
Entailment

A.   Jane is the mother of John.
B.   All mothers are females.
C.    No females are males.
D.   Jane is not a male.

• Entailment enables us to generate valid
  inferences from RDF data.


                       #lod4h
Identity & Constants

• Logical languages, like first-order logic,
  rely on binding constants to referents.

• RDF does this by using URIs as a
  constant.




                         #lod4h
Literal/Non-Literal

• Literal: Text strings that are directly used
  as objects of a statement.
• Typed Literals: strings that conform to a
  datatype
  – XML Datatypes: http://goo.gl/4wQss
  – XMLLiteral
• Non-Literal: URIs that name a resource.



                         #lod4h
Examples

foaf:name “Leonardo da Vinci”                      Plain literal

dcterms:title “La Joconde”@fr
                                                   Plain literal w/
dcterms:title “Mona Lisa”@en                       language



:birthday
“1452-04-15”^^<http://www.w3.org/2001/XMLSchema#date> . Type literal




                                   #lod4h
MODELING LINKED DATA
WITH RDFS

            #lod4h
Classes/subclasses

• Class: types of resources which we wish
  to assign properties and relationships.
• Subclasses inherit all the properties of a
  class.

• RDFs allows a subclass to have multiple
  parents.


                        #lod4h
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>
@prefix xsd: <http://www.w3.org/2001/XMLSchema#>

@prefix ex: <http://www.example.org/vessels#>

ex:vessel rdf:type rdfs:class ;
          rdfs:label “Vessel” ;

ex:ship rdf:type rdfs:class;
       rdfs:subClassOf ex:vessel ;
       rdfs:label “Ship” .




                                  #lod4h
Properties/subproperties

• Properties: attributes of defined classes.
  Establish relationships between resources
  and values (literals, non-literals).




                       #lod4h
ex:length rdf:type rdfs:property ;
      rdfs:label “Length”
ex:loa rdf:type rdfs:property;
         rdfs:subPropertyOf ex:length ;
         rdfs:label “Length over all” ;
ex:lwl rdf:type rdfs:property;
         rdfs:subPropertyOf ex:length ;
         rdfs:label “Length at waterline” .



                           #lod4h
Domain/Range

• Domain: which class may have a property
  (what can be the subject of a triple that
  uses this property)
• Range: what class of objects can be used
  with this property.
  – A class of resources
  – Literals/datatypes, etc.



                           #lod4h
ex:loa rdfs:range xsd:float

ex:vessel_type rdf:type rdfs:property;
     rdfs:domain ex:vessel ;
     rdfs:range skos:concept .




                        #lod4h
Limitations of RDFs

•   Cardinality
•   Transitivity
•   Equivalence (of classes/instances)
•   Constraining range based on domain
    – Domain:basketball teamMembers 5
    – Domain:soccer teamMembers 11




                         #lod4h
An easier way!

• Protégé Ontology Editor
  http://protege.stanford.edu/




                        #lod4h
Cultural Heritage

• CIDOC – Conceptual Reference Model
  – Lightweight Information Describing Objects
    (LIDO) (XML Schema)
  – Europeana Data Model (EDM)
• Bibliontology
• Open Annotation Collaboration




                         #lod4h
LUNCH


        #lod4h
SPARQL


         #lod4h
Basic SPARQL

Prefix <…>                         Declare what schemas
                                   you are using.
Prefix <…>
SELECT ?displayVariables           Query results

FROM/FROM NAMED                    Specify a dataset

WHERE
  {
      subject object predicate .   Query pattern

  }
                                   Query modifiers
ORDER BY/LIMIT/OFFSET

                          #lod4h
SELECT ?person
WHERE
{
 ?person :givenName "Richard" .
}


http://mith.umd.edu/dhwiwiki/index.php/SPARQL_Examples

                              #lod4h
SELECT ?propertyName ?propertyValue
WHERE
{
<http://chi.cci.fsu.edu/person/rurban#>
?propertyName ?propertyValue .
}



                      #lod4h
SELECT *
WHERE
{

    ?s ?p ?o .

}


                 #lod4h
SELECT *
WHERE
{

    ?s ?p ?o .

}


                 #lod4h
SELECT *
WHERE
{

    ?s ?p ?o .
    FILTER (regex (?o, "edu", "i"))

}

Additional functions:
http://www.w3.org/TR/rdf-sparql-query/#tests

                            #lod4h
• CONSTRUCT: returns results as RDF
  triples (not a web page to browse)

• ASK: returns boolean (true/false)

• DESCRIBE: provide a specified set of
  properties for a resource


                       #lod4h
dbPedia

• SPARQL endpoint
  http://dbpedia.org/snorql/
• Faceted Search
  http://dbpedia.org/fct/
  – View SPARQL




                        #lod4h
LINKED DATA FOR PROJECTS


            #lod4h
Next up:


• 5:30-7:00 pm
  DHWI Public DH: API Workshop
  Registered Attendees Only




                    #lod4h

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Publishing and Using Linked Open Data - Day 2

  • 1. Publishing and Using Linked Open Data Richard J. Urban, Ph.D. School of Library and Information Studies Florida State University rurban@fsu.edu @musebrarian #lod4h
  • 2. January 8, 2013 Tuesday’s Schedule • 9:00 am- 10:30 am Class Session: Information Modeling Fundamentals • 10:30-10:45 am break • 10:45- Noon Class Session: Linked Data Models • Noon- 1:00 pm Lunch (on your own) • 1:00- 2:45 pm Class Session: Searching Linked Data • 2:45- 3:00 pm break • 3:00-5:00 pm Class Session: Identifying Linked Data for Participant Projects • 5:30-7:00 pm DHWI Public DH: API Workshop Registered Attendees Only #lod4h
  • 3. Humanities Data Models • What are the models that we currently use? – Document-based models – Database Models – Probabilistic/Statistical Models (NLP) #lod4h
  • 4. How RDF is Different • Based in knowledge representation languages (artificial intelligence) • Grounded in formal predicate logic/description logics • 20th Century developments in the philosophy of language (Leibnitz, Russell, Wittgenstein, Peirce, Frege, Kripke, Tarski, etc.) • Intended to enable intelligent reasoning #lod4h
  • 6. Model-Theoretic Semantics 1. use formal structures and rules to ensure that every legitimate language expression has a well-defined meaning; 2. define what is means for a statement in a language to be true under a particular interpretation; 3. allow us to formalize the intuitive notion of logical consequence, that is, of one statement 'following logically' from others; and… 4. provide a basis for implementing automated reasoning via an appropriate proof theory. #lod4h
  • 7. Interpretations • The basic intuition of model-theoretic semantics is that asserting a sentence makes a claim about the world: it is another way of saying that the world is, in fact, so arranged as to be an interpretation which makes the sentence true. In other words, an assertion amounts to stating a constraint on the possible ways the world might be. – Anyone can say anything about anything. – But…you need to tell me what your interpretation is so I can evaluate it. #lod4h
  • 8. Entailment • A entails B • A is true • Therefore B is true #lod4h
  • 9. Entailment A. Jane is the mother of John. B. All mothers are females. C. No females are males. D. Jane is not a male. • Entailment enables us to generate valid inferences from RDF data. #lod4h
  • 10. Identity & Constants • Logical languages, like first-order logic, rely on binding constants to referents. • RDF does this by using URIs as a constant. #lod4h
  • 11. Literal/Non-Literal • Literal: Text strings that are directly used as objects of a statement. • Typed Literals: strings that conform to a datatype – XML Datatypes: http://goo.gl/4wQss – XMLLiteral • Non-Literal: URIs that name a resource. #lod4h
  • 12. Examples foaf:name “Leonardo da Vinci” Plain literal dcterms:title “La Joconde”@fr Plain literal w/ dcterms:title “Mona Lisa”@en language :birthday “1452-04-15”^^<http://www.w3.org/2001/XMLSchema#date> . Type literal #lod4h
  • 14. Classes/subclasses • Class: types of resources which we wish to assign properties and relationships. • Subclasses inherit all the properties of a class. • RDFs allows a subclass to have multiple parents. #lod4h
  • 15. @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> @prefix xsd: <http://www.w3.org/2001/XMLSchema#> @prefix ex: <http://www.example.org/vessels#> ex:vessel rdf:type rdfs:class ; rdfs:label “Vessel” ; ex:ship rdf:type rdfs:class; rdfs:subClassOf ex:vessel ; rdfs:label “Ship” . #lod4h
  • 16. Properties/subproperties • Properties: attributes of defined classes. Establish relationships between resources and values (literals, non-literals). #lod4h
  • 17. ex:length rdf:type rdfs:property ; rdfs:label “Length” ex:loa rdf:type rdfs:property; rdfs:subPropertyOf ex:length ; rdfs:label “Length over all” ; ex:lwl rdf:type rdfs:property; rdfs:subPropertyOf ex:length ; rdfs:label “Length at waterline” . #lod4h
  • 18. Domain/Range • Domain: which class may have a property (what can be the subject of a triple that uses this property) • Range: what class of objects can be used with this property. – A class of resources – Literals/datatypes, etc. #lod4h
  • 19. ex:loa rdfs:range xsd:float ex:vessel_type rdf:type rdfs:property; rdfs:domain ex:vessel ; rdfs:range skos:concept . #lod4h
  • 20. Limitations of RDFs • Cardinality • Transitivity • Equivalence (of classes/instances) • Constraining range based on domain – Domain:basketball teamMembers 5 – Domain:soccer teamMembers 11 #lod4h
  • 21. An easier way! • Protégé Ontology Editor http://protege.stanford.edu/ #lod4h
  • 22. Cultural Heritage • CIDOC – Conceptual Reference Model – Lightweight Information Describing Objects (LIDO) (XML Schema) – Europeana Data Model (EDM) • Bibliontology • Open Annotation Collaboration #lod4h
  • 23. LUNCH #lod4h
  • 24. SPARQL #lod4h
  • 25. Basic SPARQL Prefix <…> Declare what schemas you are using. Prefix <…> SELECT ?displayVariables Query results FROM/FROM NAMED Specify a dataset WHERE { subject object predicate . Query pattern } Query modifiers ORDER BY/LIMIT/OFFSET #lod4h
  • 26. SELECT ?person WHERE { ?person :givenName "Richard" . } http://mith.umd.edu/dhwiwiki/index.php/SPARQL_Examples #lod4h
  • 28. SELECT * WHERE { ?s ?p ?o . } #lod4h
  • 29. SELECT * WHERE { ?s ?p ?o . } #lod4h
  • 30. SELECT * WHERE { ?s ?p ?o . FILTER (regex (?o, "edu", "i")) } Additional functions: http://www.w3.org/TR/rdf-sparql-query/#tests #lod4h
  • 31. • CONSTRUCT: returns results as RDF triples (not a web page to browse) • ASK: returns boolean (true/false) • DESCRIBE: provide a specified set of properties for a resource #lod4h
  • 32. dbPedia • SPARQL endpoint http://dbpedia.org/snorql/ • Faceted Search http://dbpedia.org/fct/ – View SPARQL #lod4h
  • 33. LINKED DATA FOR PROJECTS #lod4h
  • 34. Next up: • 5:30-7:00 pm DHWI Public DH: API Workshop Registered Attendees Only #lod4h