LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
SEMANTIC SHORTCUTS AND VIEWS:BRIDGING THE GAP BETWEEN
ONTOLOGIES AND LINKED DATA
Krzysztof Janowicz, Pascal Hitzler, and Adila KrisnadhiSTKO Lab, University of California, Santa Barbara, USA
DaSe Lab, Wright State University, USA
AAG Meeting 2014
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
LINKED DATA MOTIVATION
LINKING DATA AS NEXT-GENERATION INFRASTRUCTURE
Data SilosWeb servicesDatabasesWeb pages
hinder ad-hoc combinationenforce data modelslimit re-usability
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
LINKED DATA MOTIVATION
FROM LINKED DOCUMENTS TO LINKED DATA
Use Uniform Resource Identifiers (URI) to identify entities, link them to otherentities, encode information about these entities using themachine-understandable RDF, and make them available on the Web.
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
LINKED DATA MOTIVATION
BERNERS-LEE’S LINKED DATA PRINCIPLES AND STARS
Four Rules for Linked DataUse URIs as names for things
Use HTTP URIs so that people can look up those names.
When someone looks up a URI, provide useful information, using the standards(RDF*, SPARQL)
Include links to other URIs. so that they can discover more things.
Is your Linked Open Data 5 Star?? Available on the web (whatever format) but with an open licence, to be Open Data
?? Available as machine-readable structured data (e.g. excel instead of imagescan of a table)
? ? ? as (2) plus non-proprietary format (e.g. CSV instead of excel)
? ? ?? All the above plus, Use open standards from W3C (RDF and SPARQL) toidentify things, so that people can point at your stuff
? ? ? ? ? All the above, plus: Link your data to other people’s data to providecontext
See http://www.w3.org/DesignIssues/LinkedData.html
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
EXPLORING AND QUERYING LINKED DATA
EXPLORING LINKED DATA ABOUT PLEACES, PEOPLE, EVENTS
Follow-your-nose: Explore information using Linked Data (DBpedia).
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
EXPLORING AND QUERYING LINKED DATA
HOW TO QUERY LINKED DATA (OVER MULTIPLE SOURCES)?
Integration by searching equivalent classes or/and same featuresin data sets. This requires ontologies/vocabularies, their alignment,and/or ontology reuse.
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY STAR RATING
ONTOLOGIES TO MAKE YOUR DATA MORE USABLE
Five Stars of Linked Data Vocabulary Use© Linked Data without any vocabulary.
? There is dereferencable human-readable information about the usedvocabulary.
?? The information is available as machine-readable explicitaxiomatization of the vocabulary.
? ? ? The vocabulary is linked to other vocabularies
? ? ?? Metadata about the vocabulary is available (in a dereferencableand machine-readable form).
? ? ? ? ? The vocabulary is linked to by other vocabularies.
See http://semantic-web-journal.net/content/
five-stars-linked-data-vocabulary-use
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY REUSE
VOCABULARY/ONTOLOGY REUSE
A typical statement:’Reuse external vocabulary whenever possible.’
<http://dbpedia.org/resource/Copernicus_(lunar_crater)>...
geo:lat"9.7"^^xsd:decimal;
geo:long"20.0"^^xsd:decimal;
...adbpedia-owl:Crater,
...ns5:Place,
...
Concerns:Most ontologies are under-specific, how are they maintained,versioning /evolution strategies are unclear, contact persons, arethey community-driven, legal issues, proper documentation,...?
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY REUSE
REUSE DIFFICULTIES EXAMPLE
The Fluidops interface renders the DBpedia RDF data from theCopernicus crater and places it on the Surface of the Earth instead ofrealizing that the given coordinates are selenographic coordinates.
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT
Alternative statement:’Align your vocabulary to other vocabulary whenever possible.’
dbpedia− owl : Crater v ADL : Crater (1)
dbpedia− owl : Person ≡ FOAF : Person (?) (2)
Concerns:Most ontologies are under-specific, requires reasoning, simplealignments may not be sufficient (despite improving toolsupport),...?
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto v a:IsConnected (1)
a:IrrigationCanal v a:Canal (2)
∃a:flowsInto.a:AgriculturalField v a:IrrigationCanal (3)
a:Waterbody u a:Land v ⊥ (4)
a:AgriculturalField v a:Land (5)
b:flowsInto v b:IsConnected (6)
b:Canal v (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
u (=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto v a:IsConnected (1)
a:IrrigationCanal v a:Canal (2)
∃a:flowsInto.a:AgriculturalField v a:IrrigationCanal (3)
a:Waterbody u a:Land v ⊥ (4)
a:AgriculturalField v a:Land (5)
b:flowsInto v b:IsConnected (6)
b:Canal v (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
u (=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto v a:IsConnected (1)
a:IrrigationCanal v a:Canal (2)
∃a:flowsInto.a:AgriculturalField v a:IrrigationCanal (3)
a:Waterbody u a:Land v ⊥ (4)
a:AgriculturalField v a:Land (5)
b:flowsInto v b:IsConnected (6)
b:Canal v (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
u (=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto v a:IsConnected (1)
a:IrrigationCanal v a:Canal (2)
∃a:flowsInto.a:AgriculturalField v a:IrrigationCanal (3)
a:Waterbody u a:Land v ⊥ (4)
a:AgriculturalField v a:Land (5)
b:flowsInto v b:IsConnected (6)
b:Canal v (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
u (=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto v a:IsConnected (1)
a:IrrigationCanal v a:Canal (2)
∃a:flowsInto.a:AgriculturalField v a:IrrigationCanal (3)
a:Waterbody u a:Land v ⊥ (4)
a:AgriculturalField v a:Land (5)
b:flowsInto v b:IsConnected (6)
b:Canal v (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
u (=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
WHAT/HOW TO MODEL?
FRAGMENT OF A MAP LEGEND ONTOLOGY DESIGN PATTERN
Ontological commitmentsShould GeographicFeature Types be classesor instances?Do we want to explicitlydefine the depictedByrelationIs stating that a Legendconsists of LegendItemsredundant?. . .
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
INSTANCES VS. CLASSES
MODELING DIFFERENCES: INSTANCES VS. CLASSES
As illustrated before alignments and mappings can be difficult
However, often, even major modeling differences can be aligned/mapped
Instances vs. classes
Florence rdf : type City (1)
Florence xyz : hasType ”City”@en (2)
Mapping between those cases
Classname v ∃hasType.{classname} (3)
∃hasType.{classname} v Classname (4)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
ROLE CHAINS
FRAGMENT OF THE MAP LEGEND ONTOLOGY
NC = {LegendItem,Symbol ,Label ,FeatureType} (1)NR = {consistsOf , isLabelFor , isLabelOf ,depictedBy} (2)
> v ¬∃N.> (3)
LegendItem v ∃consistsOf .Symbol t ∃consistsOf .LegendItem (4)
Label v ∃SymbolizedBy .Symbol u ∀SymbolizedBy .Symbol (5)> v≤ 1isLabelFor.> (6)> v≤ 1isLabelOf.> (7)
> v≤ 1SymbolizedBy.> (8)Label v ∃isLabelFor .FeatureType (9)
Label u Symbol v ⊥ (also for Symbol, Label, FeatureType, LegendItem) (10)isLabelOf− ◦ isLabelFor v depictedBy− (11)
¬∃consistsOf− v Legend (12). . . (13)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI
LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
ANOTHER SIMPLE VIEW EXAMPLE
SIMPLE VIEW FOR THE AGENT ONTOLOGY PATTERN STUB
Guarded domain and range restrictions of performsAgentRole
∃performsAgentRole.AgentRole v Agent (1)
Agent v ∀performsAgentRole.AgentRole (2)
Pairwise-disjointness axiom
Agent u AgentRole v ⊥ (3)
This axiom provides the role isPerformedBy as a view for the pattern.
performsAgentRole− v isPerformedBy (4)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI