17 april 2005sharif university of tech page 1 ontologies come of age amir hossein assiaee

17 April 2005 Sharif University of Tech

Ontologies Come of Age

Amir Hossein [email protected]

mailto:[email protected]

Sharif University of Tech 17 April 2005

Outline Introduction: Web’s growing needs Ontologies

Definition Ontology Spectrum

Uses of Ontology Simple Structured

Ontology Acquisition Ontology-based application Needs

Language Environment

Conclusion


Introduction: Web’s growing needs The web continues to grow at an astounding rate Finding the exact information one is seeking on

the web today is hard web pages do not contain markup information

about the contents of the page We need to add intelligence to search Solution: Semantic Web


Introduction: Web’s growing needs (Cont.) Berner’s Lee Architecture


Introduction: Web’s growing needs (Cont.) Berner’s Lee Architecture

We will consider at:







Conclusion


Ontologies: Definition

The term ontology has been in use for many years.

1720 Merriam Webster: Dates Ontology There are two historical definition:

A branch of metaphysics concerned with the nature and relations of being

A particular theory about the nature of being or the kinds of existents

From the view point of computational audience: “A specification of a conceptualization” by Gruber


Ontologies: Ontology Spectrum

One might visualize a simple (linear) spectrum of definitions

Catalog/ID

GeneralLogical

constraints

Terms/glossary

Thesauri“narrower

term”relation

Formalis-a

Frames(properties)

Informalis-a

Formalinstance Value

Restrs.

Disjointness, Inverse, part-of…


Ontologies: Ontology Spectrum (Cont.)

Catalogs: (a finite list of terms) can provide an unambiguous interpretation of terms

Glossary: (a list of terms and meanings) provides a kind of semantics, since humans can read the natural language statements and interpret them

Thesauri: provide some additional semantics in their relations between terms

They provide information such as synonym relationships. They do not provide an explicit hierarchy



Hierarchical:People prefer to have an explicit hierarchyYahoo, for example, provides a small number

of top-level categories This hierarchy should not be just “isa” or strict

subclasses (informal is-a) Sometimes it is strict subclass hierarchy (formal is-

a)



Frames: Classes include property information

Example: “apparel” has property “price” and “isMadeFrom” All subclasses of these categories would inherit these

properties Value Restriction:

Here we may place restrictions on what can fill a property

“isMadeFrom” and the value restriction of material a “price” property might be restricted to have a filler that is a

number



Logical ConstraintsOntologies need to express more informationExpressive ontology languages needed

Example: Ontolingua, CycL




Uses of Ontology Simple Structured (sophisticated)



Conclusion


Uses of ontology

Simple ontology example: DMOZ, for example, leverages over 35,000 volunteer

editors and at publication time, had over 360,000 classes in a taxonomy

Sophisticated ontology example: the unified medical language system (UMLS),

developed by the national library of medicine is a large sophisticated ontology about medical terminology


Uses of ontology: simple

provide a controlled vocabulary Every one can use the same vocabulary Programs can generate interfaces to encourage usage

of the controlled terms site organization and navigation support

Many web sites today expose on the left hand side of a page the top levels of a generalization hierarchy of terms.

Categories can expand to subcategories


Uses of ontology: simple (Cont.)

support expectation setting By exploring the top level categories you can

determine, if the site have your interest content taxonomies may be used as “umbrella”

structures from which to extend content There are some freely categories at high level that

you can inherit some terms form them. Example: Universal Standard Products and Services

Classification (UNSPSC)



browsing support Content on a site may be tagged with terms from the taxonomy Can be done:

manually automatically (using a clustering approach)

It can help search engines to use enhanced search capabilities search support

A query expansion method may be used in order to expand a user query with terms from more specific categories in the hierarchy



sense disambiguation support If the same term appears in multiple places in

a taxonomy, an application may move to a more general level

Example: “Jordan” as a name of Basket-ball player and name of a country


Uses of ontology: Structured

Once ontologies begin to have more structure, they can provide more power

in applications.


Uses of ontology: Structured (Cont.)

consistency checking If ontologies contain value restrictions on the

properties, then type checking can be done within applications

example, “Goods” has a property called “price” that has a value restriction of number

Completion By an ontology we can complete needed information

about things “HighResolutionScreen” contains “verticalResolution”

and “horizontalResolution”



Interoperability support Since different users/applications are using the same set of

terms, We can use equality axioms to express one term precisely in

terms of another Example: StanfordEmployee ≡ Person ∩ Employer(Stanford

University) exploit generalization/specialization information

One may get too many answers for a query, by using ontology search application can suggest specializing that term

And vice versa



support structured, comparative, and customized search if one is looking for televisions, a class description for

television may be obtained from an ontology, its properties may be obtained (such as diagonal, price, manufacturer, etc)

a comparative presentation may be made of televisions by presenting the values of each of the properties

Search interfaces can help you by showing more detailed properties of product



The foundation for configuration support Classes defined so that they contain descriptions of

what kinds of parts may be in a system Interactions between properties can be defined so

that filling in a value for one property can cause another value to be filled in for another slot

Example: a class of HighQualityTelevisions is defined so that users may choose from this class and the configurator will automatically fill in limited sets of manufacturers to choose from minimum price ranges







Conclusion


Ontology Acquisition Some sources of ontologies:

many ontologies exist in the public domain Many taxonomic structures exist on the web or in the

table of contents of documents Where to look for exiting ontologies:

Standard organizations: NIST (the National Institute of Standards and Technology -

http://www.nist.gov/) Some consortiums are forming to generate ontologies

RosettaNet (http://www.rosettanet.org)

http://www.nist.gov/



http://www.rosettanet.org/

http://www.rosettanet.org/







Conclusion


Ontology-based application Needs

Two major concernsLanguageEnvironment


Ontology-based application Needs: Language An ontology must be encoded in some

language As we saw the spectrum is very wide and

it contains simple and sophisticated ontologiesThe language should support bothMore expressive = More complex ontologies

= More sophisticated language needed


Ontology-based application Needs: Language (Cont.)

Some candidates: KRSS – the Knowledge Representation System

Specification [Patel-Schneider-Swartout 1992] KIF -the Knowledge Interchange Format OKBC – Open Knowledge Base Connectivity

[Chaudhri-et-al, 1997] Current solution: DAML+OIL


Ontology-based application Needs: Environment We need an environment for ontologies to

analyze, modify, and maintain an ontology over time

Some examples: “Verity” is a topic editor to generating taxonomies Ontolingua [Farquhar-et-al 1997] Stanford University Chimaera [McGuinness-et-al. 2000] Stanford University OilEd from Manchester University [Protégé 2000] from Stanford Medical Informatics


Ontology-based application Needs: Environment (Cont.)There are some issues to consider for choosing ontology

environment: Collaboration and distributed workforce support: (allow

users to share a session –i.e., see each other’s work environments)

Platform interconnectivity: example Java-based applications

Scale Versioning Security:

Differing access to portions of ontology Environment should expose portions of ontology based on

security model


Ontology-based application Needs: Environment (Cont.)

Analysis: To support acquisition, evolution and maintenance Analysis can support users attention to modification and …

Lifecycle issues: ontologies become larger and longer lived It should be supported for evolution, breaking apart, multiple

namespaces etc. Ease of use Diverse user support

Allow users to customize environments as appropriate to the type of user

Work for power users and naïve users


Ontology-based application Needs: Environment (Cont.)

Presentation Style textual, graphical, or other

Extensibility can adapt along with the needs of the users and the

projects







Conclusion


Conclusion Ontologies have a wide spectrum of definitions They grows with growth of needs More complex ontologies can define more

precise relations in taxonomies They have many types of applications Important issues to build ontologies are


17 april 2005sharif university of tech page 1 ontologies come of age amir hossein assiaee

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