The document discusses the role of ontologies in linked data. It notes that while semantic web ontologies have been widely applied, linked data has grown rapidly using lightweight or no ontologies. However, ontologies could still provide benefits to linked data by helping integrate and reason over heterogeneous linked data sources. Open issues remain around how to best reuse and modularize ontologies for different linked data applications and domains.

1. Linked Data & Ontologies
Rudi Studer, Elena Simperl, Benedikt Kämpgen
2011 STI Semantic Summit,
July 6, 2011
Institute of Applied Informatics and Formal Description Methods (AIFB)
Institute of Applied Informatics and Formal Description Methods (AIFB)
KIT – University of the State of Baden-Wuerttemberg and
National Research Center of the Helmholtz Association www.kit.edu

2. Outline
! Semantic Web ontologies – widely applied
! Did Linked Data kill ontologies?
! Ontologies for Linked Data
! Linked Data for ontologies
! Research and discussion topics
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3. Semantic Web Ontologies – widely applied:
Content Navigation at BBC
Created ontologies for its website
! Develop and re-organize sites
based on domain model
! E.g., sports ontology,
programme ontology
! One URI per thing
! Link content and allow
exploration of topics
! Leverage external resources
! E.g., MusicBrainz
Mike Atherton: “the complexities of knowledge call for ontological structures”
http://www.slideshare.net/reduxd/beyond-the-polar-bear
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4. Semantic Web Ontologies – widely applied:
Content Publishing via schema.org
! Consensus of Yahoo!, Bing and Google
! Ontologies (and format) to markup web pages
! Web pages more easily interpreted and more
appropriately displayed by search engines
! Large impact on businesses
Rich Snippet at Google
diTii.com
schema.org
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5. Semantic Web Ontologies – widely applied:
Content Publishing via GoodRelation at
BestBuy
! GoodRelation ontology
! Describing businesses
! Machine interpretable
! BestBuy retailer
! Major GoodRelation
deployer
! RDFa created with forms
! Enhance visibility on the
Web
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6. Semantic Web Ontologies – widely applied:
BioPortal at Stanford – Content Navigation and
Semantic Search
! Ontologies
! Ontology repository
! provide means for reuse
! offer standadized vocabulary
! Enhanced information
management:
! biological objects annotated using
the ontology
! improved navigation, filtering
visualization
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7. Summary
! Many applications for Semantic Web ontologies
! Some adoption at big players with strong
influence on businesses
Nowadays:
! Linked Data principles well adopted
! Many Linked Data sources popping up
Not yet clear: What role do ontologies play in the age
of Linked Data?
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8. Did Linked Data Kill Ontologies?
! A Little Semantics Goes a Long Way
(Jim Hendler)
! Lightweight, easy-to-understand ontologies adopted
! Semantic is not the goal, it is a way to solve a task
(Chris Welty)
! Machine learning, statistics and machine power equally
important
! Sloppy, scruffy Semantic Web does not need
ontologies (David R. Karger)
! Ontologies are a luxury and should not hinder open
data publishing and usage
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9. Ontologies in the Age of Linked Data
! Success of Linked Data ! Slow improvement of
! Viral growth works ontology usage
surprisingly well ! Needs a good balance
between effort and added
! Open Data trend value that is provided
! Lightweight ontologies are
! Heterogeneous, dirty, vs more easily understood,
inconsistent, accepted and used
not trustworthy… ! Reuse of ontologies not yet
done in practice
! However: Value of grounding
Linked Data by ontological
structures not yet recognized
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10. Ontologies for Linked Data (1)
! When publishing and consuming Linked Data, use of
ontologies/vocabularies would provide benefits
! Publishing:
! Less effort in publishing: Reusing well-defined collections of
URIs contained in ontologies (e.g., SKOS, Geonames)
! Easier integration of data when publishing based on ontology
! Having well-defined conceptualizations available
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11. Ontologies for Linked Data (2)
! When publishing and consuming Linked Data, use of
ontologies/vocabularies would provide benefits
! Consumption:
! Self-describing data guide agents when using Linked Data sources
! Splitting the integration / alignment effort between instance and
schema level
! Reasoning for implicit knowledge
! e.g., gr:DeliveryModeParcelService rdfs:subClassOf
gr:DeliveryMethod
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12. Linked Data for Ontologies
! When building and consuming ontologies use of Linked Data
sources would provide benefits
! Building:
! Inductive, incremental approach to ontology engineering
! Less manual modeling effort needed: use Linked Data as source
! No perfection needed: define mappings if you need them
! Collaborative approach to ontology engineering
! Exploiting Linked Data in games, tagging systems, wikis
! Consumption:
! The more reuse of Linked Data sources the easier the dynamic
extension of the ontology (e.g., instance of a class)
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13. Research and Discussion Topics
! New Challengies for ontology engineering methodologies
! Open Issues for Exploiting Linked Data & ontologies
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14. Do traditional methodologies for ontology
engineering and evaluation need to be revised?
DILIGENT CommonKADS
[Pinto et al., 2004] [Schreiber et al., 1999]
NeOn Methodology
Enterprise Ontology [Gómez-Pérez, 2008]
[Uschold & King, 1995]
Holsapple&Joshi
IDEF5 [Holsapple & Joshi, 2002]
[Benjamin et al. 1994] On-To-Knowledge
CO4 [Sure, 2002]
Ontometric [Euzenat, 1995] ONTOCOM
[Gómez-Pérez, 2004] [Simperl et al., 2006]
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15. New Requirements for Methodologies
! More data-driven
! data first, ontology second
! More reuse-focused
! Leveraging ontology repositories, semantic search
engines
! Emphasis on alignment, especially at the instance level
! Application-oriented
! Human vs machine-oriented consumption (using
specific technologies)
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16. Open Issues for Exploiting Linked Data
& ontologies
! What ontologies when to reuse for what kinds of
data (statistical data, sensor information…)
! What guidelines are around
! Best practices for ontology reuse
! Statistics of ontology reuse in Linked Data
! Better usage of modularization concepts
! Application-driven reuse of parts of ontologies and Linked Data
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17. Open Issues for Exploiting Linked Data
& ontologies
! What are the mechanisms for viral growth of Linked Data
! How to release open data’s potential as a major driver for
innovation and for unlocking the full data value
! Exploiting the social Web
! What are business models for such initiatives
! Major driver for Open Linked Data: eGovernment
! Specification of standard ontologies in order to push the
release of public sector information as Linked Data
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18. Questions / Comments?
http://www.aifb.kit.edu
http://www.ksri.kit.edu
http://www.fzi.de
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