TU Wien – April 24-29, 2006Semantics and Ontologies in GI Services Ontologies for the Semantic Web...

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TU Wien – April 24-29, 2006 Semantics and Ontologies in GI Services

Ontologies for the Semantic Web

Michael Lutz

[email protected]

Slides based on Co-ode OWL Tutorial (University of Manchester)http://co-ode.man.ac.uk/resources/tutorials/intro/slides/

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Overview

• Why do we need ontologies and the Semantic Web?

• What are ontologies ontology definitions properties

• Ontology languages OWL & Description Logics Reasoning

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Impossible (?) using the Syntactic Web…

• Complex queries involving background knowledge Find information about “animals that use sonar

but are not either bats or dolphins”, e.g., Barn owl

• Locating information in data repositories Travel enquiries Prices of goods and services

• Finding and using web services Visualise surface interactions between two proteins

• Delegating complex tasks to web agents Book me a holiday next weekend somewhere

warm, not too far away, and where they speakFrench or English

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Consider a typical web page:

• Markup consists of: • rendering information

(e.g., font size and colour)

• Hyper-links to related content

• Semantic content is accessible to humans but not (easily) to computers…

What is the Problem?

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What information can we see…

• WWW2002

• The eleventh international world wide web conference

• Sheraton waikiki hotel, Honolulu, hawaii, USA, 7-11 may 2002

• 1 location. 5 days. learn. interact

• Registered participants coming from: australia, canada, chile denmark, france, germany, …

• Register now

• On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event …

• Speakers confirmed Tim Berners-Lee (Tim is the well known inventor of the

Web, …) …

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…

… …

What information can a machine see…

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<name> </name>

<location> </location><date> </date><slogan> </slogan><participants>

</participants>

<introduction>

… </introduction>

<speaker> </speaker><bio> </bio>…

XML markup with “meaningful” tagsThe Solution?

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< > </ >< >

</ >< > </ >< > …</ >< >

, … </ >

< >

… </ >< > </ >< > </

>…

Machine sees…

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Need to Add “Semantics”

• External agreement on meaning of annotations e.g., Dublin Core: agree on the meaning of a set of

annotation tags but this approach is inflexible and only a limited

number of things can be expressed

• Use ontologies to specify meaning of annotations Ontologies provide a vocabulary of terms New terms can be formed by combining existing ones Meaning (semantics) of such terms is formally

specified Can also specify relationships between terms

in multiple ontologies

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Ontology in Computer Science

• An ontology is an engineering artifact: constituted by a specific vocabulary used to describe

a certain reality, plus a set of explicit assumptions regarding the intended

meaning of the vocabulary.

• Thus, an ontology describes a formal specification of a certain domain: Shared understanding of a domain of interest Formal and machine manipulable model of a domain

of interest

“An explicit specification of a conceptualisation” [Gruber93]

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Ontology Components

• Names for important concepts in the domain Elephant is a concept whose members are a kind of

animal Herbivore is a concept whose members are exactly

those animals who eat only plants or parts of plants

• Background knowledge/constraints on the domain Adult_Elephants weigh at least 2,000 kg All Elephants are either African_Elephants or

Indian_Elephants No individual can be both a Herbivore and

a Carnivore

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A semantic continuum

[Mike Uschold, Boeing Corp]

Shared human consensus

Implicit

Text descriptions

Pump: “a device for moving a gas or liquid from one place or container to another”

Informal(explicit)

Semantics hardwired; used at runtime

Formal(for humans)

Semantics processed and used at runtime

(pump has (superclasses (…))

Formal(for machines)

• Less ambiguity• Better interoperation• More robust – less

hardwiring• More difficult

Further to the right

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Ontology Design and Deployment

• In the Semantic Web there should be tools and services to help users: Design and maintain high quality ontologies Store (large numbers) of instances of ontology classes,

e.g. annotations from web pages Answer queries over ontology classes and instances,

e.g.- Find more general/specific classes- Retrieve annotations/pages matching a given

description Integrate and align multiple ontologies

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• Clash of intuitions: Domain Experts vs. Logicians Transparency & predictability vs.

Rigour & Completeness Neophytes caught in the muddled middle

• The knowledge acquisition “bottleneck”

• Assuring quality & managing change

• Confusion of terminology and usage

• Interdisciplinarity: Linguistics, Cognitive science, Software engineering, Philosophy

• A jumble of syntaxes

Why Ontology Engineering is hard…

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• “Class” “Concept” “Category” “Type” are often used synonymously, but more precisely…

- categories are in the world- concepts are in the mind- classes/types are in the ontology

• “Instance” “Individual”

• “Entity” “object” can be class or individual

• “Property” “Slot” “Relation” “Relationtype” “Attribute” Semantic link type” “Role”

Vocabulary

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OntologiesOntologies

Software agents

Software agents Problem-

solving methods

Problem-solving

methods Domain-independent applications

Domain-independent applications

DatabasesDatabasesDeclarestructure

Knowledgebases

Knowledgebases

Providedomain

description

The “Semantic

Web”

The “Semantic

Web”

An Ontology should be just the Beginning

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Ontology Languages

• Wide variety of languages for “Explicit Specification” Graphical notations, e.g. UML, Semantic networks,

Topic Maps (see http://www.topicmaps.org/), RDF Logic based, e.g. Description Logics (e.g., OWL), Rules

(e.g., RuleML, LP/Prolog), First Order Logic (e.g., KIF), Conceptual graphs, higher order and non-classical logics

Probabilistic/fuzzy

• Degree of formality varies widely Increased formality makes languages more amenable

to machine processing(e.g., automated reasoning)

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• Objects/Instances/Individuals Elements of the domain of discourse Equivalent to constants in FOL

• Types/Classes/Concepts Sets of objects sharing certain characteristics Equivalent to unary predicates in FOL

• Relations/Properties/Roles Sets of pairs (tuples) of objects Equivalent to binary predicates in FOL

Language Primitives

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Web Ontology Language Requirements

• Desirable features for Web Ontology Language: Extends existing Web standards, e.g. XML, RDF,

RDFS Easy to understand and use should be based on

familiar KR idioms Formally specified Of “adequate” expressive power Possible to provide automated reasoning support

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OWL Language

• Three species of OWL OWL full is union of OWL syntax and RDF OWL DL restricted to FOL fragment ( DAML+OIL) OWL Lite is “easier to implement” subset of OWL DL

• OWL DL based on SHIQ Description Logic

• OWL DL benefits from many years of DL research Well defined semantics Formal properties well understood (complexity,

decidability) Known reasoning algorithms Implemented systems (highly optimised)

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(In)famous “Layer Cake”

Data Exchange

Semantics+reasoning

Relational Data?

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???

???

???

TimBL, 2000 (http://www.w3.org/2000/Talks/1206-xml2k-tbl/slide10-0.html)

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References

• Daconta, M.C., Obrst, L.J. & Smith, K.T. (2003): The Semantic Web. A Guide to the Future of XML, Web Services, and Knowledge Management, John Wiley & Sons

esp. chapter 8: Understanding Ontologies

• McGuinness, D.L. & van Harmelen, F. (Eds.) (2004): OWL Web Ontology Language Overview, available from http://www.w3.org/TR/owl-features/

• Baader, F., Calvanese, D., McGuinness, D., Nardi, D. & Patel-Schneider, P. (2003): The Description Logic Handbook, Cambridge University Press

esp. introductory chapters 1 & 2

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Questions???

TU Wien – April 24-29, 2006Semantics and Ontologies in GI Services Ontologies for the Semantic Web...

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