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

5.
http://en.wikipedia.org/wiki/Semantic_Web_Stack
#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

13.
MODELING LINKED DATA
WITH RDFS
#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

27.
SELECT ?propertyName ?propertyValue
WHERE
{
<http://chi.cci.fsu.edu/person/rurban#>
?propertyName ?propertyValue .
}
#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