The Immediate Prospects for the Application of Ontologies in Digital Libraries

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The Immediate Prospects for the
Application of Ontologies in
Digital Libraries


Jody DeRidder

Spring 2007

IS 565, Digital Libraries

Dr. Suzie Allard, Professor

What are Ontologies?

Thesaurus:

…. And MORE!!

Parent Child
Instance:

Andy is a child. Sarah is a child.


NT Father NT Daughter
Relation:

“fight”


NT Mother NT Son
Axiom:

Sarah and Andy always fight.


RT Child RT Parent
Constraint:

Until their parents stop them!

An ontology is like an expanded thesaurus.


Methods of encoding our concepts and their
relationships so computers can understand us, and
help us find what we need.

Ontology components (constructs)



concepts (and their properties)



instances (examples of concepts)



relations



axioms (always true)



constraints (only true if)


Wine

Beringer White Zinfandel

Grapes

Zinfandel Grapes

BUT!!

Only when produced by

Beringer

Corp.

White Zinfandel

Type of

Produces

Type of

Lightweight Ontologies:


are little more than taxonomies, and include:



concepts,



properties that describe concepts, and



relationships.


An example of this would be Dublin Core

http://www.cs.umd.edu/projects/plus/SHOE/onts/dublin.html


Heavyweight ontologies:


also include



axioms and



logic constraints


An example of this would be Cyc
http://www.cyc.com/cyc/technology/whatiscyc
.




Ontology Types: Depth of Territory


The more heavyweight the ontology is, the more expressive and powerful




and the more complex and costly to create, implement, and maintain.

Global Ontologies


Domain Ontologies


Application Ontologies



Ontology Types: Breadth of Territory

Simpler for computer applications if we can all map to a single global ontology


but MUCH more difficult for humans to agree on, implement, and maintain.

Botany

Art

History

Creating Lesson

Plans

Identifying Diseases

By Symptomology

An Audio Tape Ontology Example


http://jodi.tamu.edu/Articles/v01/i08/Hunter/

An example use of an ontology in
education…


Alexandria Digital Earth Prototype

http://www.alexandria.ucsb.edu/research/learning/index.htm




http://onto.stanford.edu:8080/wino/index.jsp




--

where you can select a food and the software will choose appropriate
wines to accompany your meal.




An example you can play with:
Wine Agent 1.0

How does it work?



If seafood is tagged as having the

property

of


requiring a dry white wine;


and swordfish is listed as an
instance

(type) of


seafood;


and a certain Swiss Chardonnay has been added


as an
instance

(type) of dry white wine




then when you ask what wine to serve with swordfish,


this Chardonnay would be suggested to


accompany your dinner.


Ontology Mapping

So… we all speak different “languages” or ontologies; to support searching
across the variation of terms, we need to map each ontology onto the others… a
form of
translation.

One such ontology mapping language is XeOml,
which allows one
-
to
-
one or one
-
to
-
many mappings
between elements of two ontologies.

From:
http://dit.unitn.it/~bouquet/ISWC
-
04
-
MCN/papers/10
-
Pazienza.pdf

Harebell

Downiniga
elegans



showy
downinig
a

Purple
flower

Problems in Ontology Mapping


Michael Klein, 2001.


<
http://www.informatik.uni
-
bremen.de/agki/www/buster/IJCAIwp/Finals/klein.pdf
>

How do query engines use ontology mapping?

An example from OBSERVER

[Mena, 2000]

Standards, the bottom line for interoperability



To represent knowledge so that computers can


“understand” us, we need to use formats


they can process, and a language they


can understand.



For applications to be interoperable,


we need agreed
-
upon standards:



Resource Description Framework (RDF)

is a simple
notation for representing relationships between and among
concepts. Each concept is represented by a URL.



Web Ontology Language (OWL)

is a more complex artificial
language for the exact description of things and their
relationships.


RDF
: Resource Description Framework

<?xml version="1.0"?>

<rdf:RDF xmlns:rdf=


"http://www.w3.org/1999/02/22
-
rdf
-
syntax
-
ns#"


xmlns:contact=


"http://www.w3.org/2000/10/swap/pim/contact#">


<
contact:Person

r
df:about="http://www.w3.org/People/EM/contact#me">


<
contact:fullName
>
Eric Miller
</contact:fullName>


<contact:mailbox


rdf:resource="mailto:em@w3.org"/>


<contact:personalTitle>Dr.</contact:personalTitle>

</contact:Person>

</rdf:RDF>

From:
http://www.w3.org/TR/REC
-
rdf
-
syntax/


Subject: Contact Person

Predicate: fullName

Object: Eric Miller

Example: RDF/XML Describing Eric Miller

OWL
: Web Ontology Language


OWL and RDF have similarities, but


OWL is a much stronger language with greater


machine interpretability than RDF
.


Three Sublanguages
:

*
OWL Lite


Hierarchical Classification, simple constraints.


*
OWL DL


Description Logic: as expressive as is possible while maintaining


the logic needed for computers to reason and make inferences.


*
OWL Full



Maximum expressiveness with no computational guarantees.


Think of this OWL and RDF as
frameworks
for concepts and their possible relations.


You use the framework to encode the Ontology.

Ontologies… who needs them?


Findability


Query Expansion


Reasoning






…to help us sift through the exponential growth


of digital materials

We do!!!

Ontology Implementation Tasks

Simperl and Tempich, 2006
<
http://ontocom.ag
-
nbi.de/docs/odbase2006.pdf
>

What are the costs?


Product factors


complexity of the domain analysis, conceptualization, implementation,
instantiation, evaluation, integration, reusability, and documentation



Personnel factors



ontologist/domain expert capability & experience,


language and tool experience, and personnel continuity



Project factors


tool support, multi
-
site development, required development schedule



Reuse/maintenance factors


ontology understandability, domain/expert unfamiliarity, complexity of
evaluation, modifications, and translations




ONTOCOM
:
http://ontocom.ag
-
nbi.de/index.html

How complex is your ontology? Heavyweight or lightweight?

And how broad is your domain?

Where are ontologies most feasible?


Commercial Ventures and


Commercially Funded Research


Example: Xyleme

http://www.reddogsoftware.com/page/xyleme/




Government


(
especially Defense
)


Example: Ontology Works
http://www.ontologyworks.com/


Possibly Education
?

If you work in one of these areas,


*
you
* will likely be using ontologies!

For the rest of us…

If you are in a general purpose digital library,


and you
have
the funding:



start

the research


(what is your target audience’s terminology,


versus the terminology of your content descriptions?)




watch

the tools

develop, and test them



watch
for
domain
and

global ontologies

that are


given
the

W3C stamp of approval
!




. . . IT WON’T BE LONG NOW!

Until the development, application, and
maintenance becomes cheaper and easier,
ontologies will not be feasible for general
purpose digital libraries without major ongoing
funding.

Bibliography


Simperl, Elena Paslaru Bontas and Christoph Tempich. “Ontology Engineering: a Reality Check.”



5th International Conference on Ontologies, Databases, and Applications of Semantics, 2006.



<
http://ontocom.ag
-
nbi.de/docs/odbase2006.pdf
> (16 March 2007).




Bontas, Elena Paslaru and Malgorzata Mochol. “Ontology Engineering Cost Estimation with ONTOCOM.”


Technical Report TR
-
B
-
06
-
01, Freie Universität Berlin, Germany, 7 February 2006.


<
http://ontocom.ag
-
nbi.de/docs/tr
-
b
-
06
-
01.pdf
> (16 March 2007).


Data, Refnes. "Introduction to OWL.” W3Schools, 2006. <
http://www.w3schools.com/rdf/rdf_owl.asp
>


(1 April 2007).


de Bruijn, Jos. “Using Ontologies: Enabling Knowledge Sharing and Reuse on the Semantic Web.”


Digital Enterprise Research Institute Technical Report DERI
-
2003
-
10
-
29, October 2003.


<
http://www.deri.at/fileadmin/documents/DERI
-
TR
-
2003
-
10
-
29.pdf
> (13 March 2007).


Doerr, Martin, Jane Hunter, and Carl Lagoze. “Towards a Core Ontology for Information Integration.”


Journal of Digital Information
, 4:1, Article 169, 9 April 2003.


<
http://jodi.ecs.soton.ac.uk/Articles/v04/i01/Doerr/
> (3 February 2007).


Hsu, Eric L. “Wine Agent: How does it work?” Stanford University Knowledge Systems


Artificial Intelligence Laboratory

, 8 April 2003.


<
http://www.ksl.stanford.edu/projects/wine/explanation.html
> (3 February 2007).


Hunter, Jane. “MetaNet


A Metadata Term Thesaurus to Enable Semantic Interoperability Between


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<
http://jodi.tamu.edu/Articles/v01/i08/Hunter/
> (24 February 2007).


Institut für Informatik. “ONTOCOM Cost Drivers.” Institut für Informatik, Networked Information Systems,


Freie Universität Berlin, 2006. <
http://ontocom.ag
-
nbi.de/ontocom.html
> (1 April 2007).



Bibliography, continued

International Conference on Ontologies, Databases, and Applications of Semantics, 2006.


<
http://ontocom.ag
-
nbi.de/docs/odbase2006.pdf
> (16 March 2007).


Klein, Michael. “Combining and Relating Ontologies: An Analysis of Problems and Solutions.” International


Joint Conferences on Artificial Intelligence
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2001.


<
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-
bremen.de/agki/www/buster/IJCAIwp/Finals/klein.pdf
> (13 March 2007).


Mena, Eduardo, et. al. “OBSERVER: An Approach for Query Processing in Global Information Systems


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-
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, 8, 223
-


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Intelligence
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<
http://highered.org/docs/milam
-
ontology.pdf
> (1 April 2007)


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>


(18 March 2007).


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-
based extensible Ontology Mapping Language.” Paper


presented at the 3rd International Semantic Web Conference (ISWC2004) in Hiroshima, Japan,


November 2004. <
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-
nlp.info.uniroma2.it/stellato/publications/2004_ISWC
-
04_XeOML%20An%20XML
-
based%20extensible%20Ontology%20Mapping%20Language.pdf
>


(6 February 2007).


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<
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