aag 2014 talk on ontology views, reusue, alignment

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LINKED SPATIOTEMPORAL DATA ONTOLOGIES/VOCABULARIES VIEWS S EMANTIC S HORTCUTS AND VIEWS : B RIDGING THE GAP BETWEEN ONTOLOGIES AND L INKED 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

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Page 1: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 2: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 3: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 4: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 5: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 6: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 7: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 8: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 9: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 10: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 11: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 12: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 13: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 14: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 15: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 16: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 17: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 18: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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

Page 19: AAG 2014 Talk on Ontology Views, Reusue, Alignment

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