This document discusses challenges with linking and querying data from multiple sources using ontologies and vocabularies. It describes issues with reusing, aligning, and modeling ontologies and vocabularies. Examples show difficulties with ontology and vocabulary alignment due to differences in concepts and logical definitions. The document advocates addressing these challenges through semantic shortcuts and views that bridge the gaps between ontologies and linked data.

1. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
SEMANTIC SHORTCUTS AND VIEWS:
BRIDGING THE GAP BETWEEN
ONTOLOGIES AND LINKED DATA
Krzysztof Janowicz, Pascal Hitzler, and Adila Krisnadhi
STKO Lab, University of California, Santa Barbara, USA
DaSe Lab, Wright State University, USA
AAG Meeting 2014
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

2. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
LINKED DATA MOTIVATION
LINKING DATA AS NEXT-GENERATION INFRASTRUCTURE
Data Silos
Web services
Databases
Web pages
hinder ad-hoc combination
enforce data models
limit re-usability
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

3. 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 other
entities, encode information about these entities using the
machine-understandable RDF, and make them available on the Web.
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

4. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
LINKED DATA MOTIVATION
BERNERS-LEE’S LINKED DATA PRINCIPLES AND STARS
Four Rules for Linked Data
Use 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 image
scan 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) to
identify things, so that people can point at your stuff
All the above, plus: Link your data to other people’s data to provide
context
See http://www.w3.org/DesignIssues/LinkedData.html
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

5. 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

6. 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 features
in data sets. This requires ontologies/vocabularies, their alignment,
and/or ontology reuse.
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

7. 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 used
vocabulary.
The information is available as machine-readable explicit
axiomatization of the vocabulary.
The vocabulary is linked to other vocabularies
Metadata about the vocabulary is available (in a dereferencable
and 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

8. 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;
...
a
dbpedia-owl:Crater,
...
ns5:Place,
...
Concerns:
Most ontologies are under-specific, how are they maintained,
versioning /evolution strategies are unclear, contact persons, are
they community-driven, legal issues, proper documentation,...?
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

9. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY REUSE
REUSE DIFFICULTIES EXAMPLE
The Fluidops interface renders the DBpedia RDF data from the
Copernicus crater and places it on the Surface of the Earth instead of
realizing that the given coordinates are selenographic coordinates.
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

10. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT
Alternative statement:
’Align your vocabulary to other vocabulary whenever possible.’
dbpedia − owl : Crater ADL: Crater (1)
dbpedia − owl : Person ≡ FOAF : Person (?) (2)
Concerns:
Most ontologies are under-specific, requires reasoning, simple
alignments may not be sufficient (despite improving tool
support),...?
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

11. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto a:IsConnected (1)
a:IrrigationCanal a:Canal (2)
∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3)
a:Waterbody a:Land ⊥ (4)
a:AgriculturalField a:Land (5)
b:flowsInto b:IsConnected (6)
b:Canal (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
(=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

12. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto a:IsConnected (1)
a:IrrigationCanal a:Canal (2)
∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3)
a:Waterbody a:Land ⊥ (4)
a:AgriculturalField a:Land (5)
b:flowsInto b:IsConnected (6)
b:Canal (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
(=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

13. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto a:IsConnected (1)
a:IrrigationCanal a:Canal (2)
∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3)
a:Waterbody a:Land ⊥ (4)
a:AgriculturalField a:Land (5)
b:flowsInto b:IsConnected (6)
b:Canal (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
(=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

14. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto a:IsConnected (1)
a:IrrigationCanal a:Canal (2)
∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3)
a:Waterbody a:Land ⊥ (4)
a:AgriculturalField a:Land (5)
b:flowsInto b:IsConnected (6)
b:Canal (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
(=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

15. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
VOCABULARY ALIGNMENT
VOCABULARY/ONTOLOGY ALIGNMENT DIFFICULTIES
a:flowsInto a:IsConnected (1)
a:IrrigationCanal a:Canal (2)
∃a:flowsInto.a:AgriculturalField a:IrrigationCanal (3)
a:Waterbody a:Land ⊥ (4)
a:AgriculturalField a:Land (5)
b:flowsInto b:IsConnected (6)
b:Canal (≥2 b:IsConnected.b:Waterbody) (7)
b:IrrigationCanal ≡ (=1 b:isConnected.b:Waterbody)
(=1 b:flowsInto.b:AgriculturalField) (8)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

16. LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS
WHAT/HOW TO MODEL?
FRAGMENT OF A MAP LEGEND ONTOLOGY DESIGN PATTERN
Ontological commitments
Should Geographic
Feature Types be classes
or instances?
Do we want to explicitly
define the depictedBy
relation
Is stating that a Legend
consists of LegendItems
redundant?
. . .
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

17. 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 ∃hasType.{classname} (3)
∃hasType.{classname} Classname (4)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

18. 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)
¬∃N. (3)
LegendItem ∃consistsOf.Symbol ∃consistsOf.LegendItem (4)
Label ∃SymbolizedBy.Symbol ∀SymbolizedBy.Symbol (5)
≤ 1isLabelFor. (6)
≤ 1isLabelOf. (7)
≤ 1SymbolizedBy. (8)
Label ∃isLabelFor.FeatureType (9)
Label Symbol ⊥ (also for Symbol, Label, FeatureType, LegendItem) (10)
isLabelOf−
◦ isLabelFor depictedBy−
(11)
¬∃consistsOf−
Legend (12)
. . . (13)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI

19. 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 Agent (1)
Agent ∀performsAgentRole.AgentRole (2)
Pairwise-disjointness axiom
Agent AgentRole ⊥ (3)
This axiom provides the role isPerformedBy as a view for the pattern.
performsAgentRole−
isPerformedBy (4)
SEMANTIC SHORTCUTS AND VIEWS JANOWICZ, HITZLER, KRISNADHI