The Semantic Web
and (vs?)
Knowledge Representation
ENC 2004,
September 2004
Peter F. Patel-Schneider
Bell Labs Research
Murray Hill, NJ, USA
pfps@lucent.com
Abstract
The vision of the Semantic Web is a collection of documents in the
World Wide Web whose content is meaningful to computers. In
this vision computers can directly and effectively process
information stored in the Web without having to depend on human
guidance. This vision also underlies Knowledge Representation, a
subdiscipline of Artificial Intelligence. Research in Knowledge
Representation over the last four decades has produced some
surprising results but has also identified some very difficult
problems that need to be overcome if this vision is to be realized in
its entirety. The Semantic Web vision provides several
opportunities for Knowlege Representation that have not been
explioted in the past as well as some new difficulties that could
hinder application of techniques from Knowledge Representation to
the Semantic Web. Achieving the vision of the Semantic Web
require exploiting the successes of Knoweldge Representation in a
challenging new environment.
Acknowledgments and Caveat
Some of the slides for this talk have been taken from
various talks on the Semantic Web by Ian Horrocks
and others, including Jeen Broekstra, Carole Goble,
Frank van Harmelen, Austin Tate, and Raphael Volz.
Thanks go to all of them.
This talk contains my personal opinions on the
Semantic Web and Knowledge Representation. Other
researchers in both areas have differing opinions.
What is the Semantic Web?
The Semantic Web aims to make information on the
World Wide Web accessible to computers.
• Not only parsable by computers (i.e., XML), but also
understandable (in some sense) by computers.
• Prior agreements between humans are not needed to
provide meaning (as is the case for XML).
• Human guidance is not always needed.
History of the Semantic Web
•
•
Web was “invented” by Tim Berners-Lee (amongst others), a
physicist working at CERN
TBL’s original vision of the Web was much more ambitious than
the reality of the existing (syntactic) Web:
“... a goal of the Web was that, if the interaction between person and
hypertext could be so intuitive that the machine-readable information
space gave an accurate representation of the state of people's
thoughts, interactions, and work patterns, then machine analysis could
become a very powerful management tool, seeing patterns in our work
and facilitating our working together through the typical problems which
beset the management of large organizations.”
•
TBL (and others) have since been working towards realising this
vision, which has become known as the Semantic Web
– E.g., article in May 2001 issue of Scientific American…
Scientific American, May 2001:
What is Knowledge Representation?
Knowledge Representation aims to make information
accessible to computers.
• Not only parsable by computers (i.e., databases) but
also understandable (in some sense) by computers.
• Prior agreements between humans are not needed to
provide meaning (as is the case for databases).
• Human guidance is not always needed.
Why the Semantic Web matches
Knowledge Representation
• Semantic Web is not just data
– Divergent interpretations of reality
– Missing information (and not just null values)
• Doesn‘t match assumptions of databases or
object-oriented programming
– Open world (no closed-world assumption)
– Objects change status over time
• Semantic Web is a representation system
The Semantic Web and/vs
Knowledge Representation
• The Semantic Web is both an opportunity and a challenge for
Knowledge Representation
– An opportunity because the Semantic Web is (or will be) a source
of information with very similar goals to those of Knowledge
Representation
– A challenge because some of the characteristics of the Semantic
Web violate some of the assumptions that have generally been held
in Knowledge Representation
• Knowledge Representation is both a resource and a cautionary
tale for the Semantic Web
– Knowledge Representation techniques can be utilized in the
Semantic Web
– Problems encountered in Knowledge Representation have already
plauged the Semantic Web.
Why Knowledge Representation is a
resource for the Semantic Web
• Knowledge Representation provides formal rigor
– Meaning of information-bearing constructs are formally
determined
• Needed for computers to process the constructs
(compare with formal syntax, also required for
computer processing)
• Knowledge Representation is concerned with
reasoning
– Determining what follows from a collection of information
– Provides an account of what can be (reliably) done by a
computer
• Knowledge Representation systems are becoming
quite powerful and reliable
Why Knowledge Representation is a
cautionary tale for the Semantic Web
• Early Knowledge Representation was not formal
– Lead to many interminable debates about the meaning of
constructs
– Current work in Knowledge Representation is very
concerned with the formal meaning of constructs
• Computing with representations is difficult
– Many problems are intractable or even undecidable
– Lead to a retreat to simpler languages
– Current systems are quite capable, even on expressive
languages
• Heavily optimized code
• Computers are much more powerful
Why the Semantic Web is an opportunity
for Knowledge Representation
• The World Wide Web forms a (very) large source of
information (albeit in awkward formats)
• The Semantic Web aims to transform much of this
information into a form compatible with the goals of
Knowledge Representation
• The Semantic Web also will contain services that can
be controlled by other computers (and reasoned
about)
– A potential solution to the Grounding Problem in
Knowledge Representation
Why the Semantic Web is a challenge
for Knowledge Representation
• Very large amounts of information will be part
of the Semantic Web
– Can overwhelm formal reasoning methods
• The Semantic Web contains differing
interpretations of reality
– Which one(s) to choose?
• Recovering from inconsistencies
– How to determine how inconsistencies arise
– How to determine who to trust
Why is the Semantic Web
a good idea?
Where we are Today:
the Syntactic Web
[Hendler & Miller 02]
The Syntactic Web is…
• A hypermedia, a digital library
– A library of documents called (web pages) interconnected by
a hypermedia of links
• A database, an application platform
– A common portal to applications accessible through web
pages, and presenting their results as web pages
• A platform for multimedia
– BBC Radio 4 anywhere in the world! Terminator 3 trailers!
• A naming scheme
– Unique identity for those documents
A place where computers do the presentation (easy)
and people do the linking and interpreting (hard).
Why not get computers to do more of the hard work?
[Goble 03]
Hard Work using the Syntactic Web…
Find images of Peter Patel-Schneider, Frank van
Harmelen and Alan Rector…
Rev. Alan M. Gates, Associate Rector of the
Church of the Holy Spirit, Lake Forest, Illinois
Impossible (?) using the Syntactic Web…
• Complex queries involving background knowledge
– Find information about “animals that use sonar but are not
either bats or dolphins”
• Locating information in data repositories
– Travel enquiries
– Prices of goods and services
– Results of human genome experiments
• 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 speak French or English
What is the Problem?
• 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 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, ghana, hong kong, india,
ireland, italy, japan, malta, new zealand, the netherlands, norway,
singapore, switzerland, the united kingdom, the united states, vietnam,
zaire
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, …
Ian Foster
Ian is the pioneer of the Grid, the next generation internet …
What information can a machine 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, ghana, hong kong, india,
ireland, italy, japan, malta, new zealand,
the netherlands, norway, singapore,
switzerland, the united kingdom, the united
states, vietnam, zaire
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, 
Ian Foster
Ian is the pioneer of the Grid, the next
generation internet 
Solution: XML markup with “meaningful” tags?
<name>WWW2002
The eleventh
international world
wide webcon</name>
<location>Sheraton
Honolulu,
waikiki hotel
hawaii, USA</location>
<date>7-11 may 2002</date>
<slogan>1 location 5 days learn interact</slogan>
<participants>Registered participants coming from
australia, canada, chile denmark, france,
germany, ghana, hong kong, india, ireland,
italy, japan, malta, new zealand, the
netherlands, norway, singapore, switzerland,
the united kingdom, the united states,
vietnam, zaire</participants>
<introduction>Register
now
On the 7th May Honolulu will provide the
backdrop of the eleventh international world
wide web conference This prestigious event 
Speakers confirmed</introduction>
<speaker>Tim berners-lee</speaker>
<bio>Tim is the well known inventor
Web,</bio>…
of
the
But What About…
<conf>WWW2002
The eleventh
international world
wide webcon</conf>
<place>Sheraton
waikiki hotel
Honolulu, hawaii, USA</place>
<date>7-11 may 2002</date>
<slogan>1 location 5 days
learn interact</slogan>
<participants>Registered
participants coming from
australia, canada, chile denmark, france,
germany, ghana, hong kong, india, ireland,
italy, japan, malta, new zealand, the
netherlands, norway, singapore, switzerland,
the united kingdom, the united states,
vietnam, zaire</participants>
<introduction>Register
now
On the
May Honolulu will provide the
backdrop of the eleventh international world
wide web conference This prestigious event 
Speakers confirmed</introduction>
7th
<speaker>Tim berners-lee</speaker>
<bio>Tim is the well known inventor
of
the Web,…
Machine sees…
<name>WWW2002
The eleventh international world wide webc</name>
<location>Sheraton waikiki hotel
Honolulu, hawaii, USA</location>
<date>7-11 may 2002</date>
<slogan>1 location 5 days learn interact</slogan>
<participants>Registered participants coming from
australia, canada, chile denmark, france,
germany, ghana, hong kong, india, ireland,
italy, japan, malta, new zealand, the
netherlands, norway, singapore, switzerland,
the united kingdom, the united states,
vietnam, zaire</participants>
<introduction>Register now
On the 7th May Honolulu will provide the
backdrop of the eleventh international world
wide web conference This prestigious event 
Speakers confirmed</introduction>
<speaker>Tim berners-lee</speaker>
<bio>Tim is the well known inventor of the W</bio>
<speaker>Ian Foster</speaker>
<bio>Ian is the pioneer of the Grid, the ne</bio>
Need to Add “Semantics”
Two very different possible approaches:
1. External agreement on meaning of annotations
– Agree on the meaning of a set of annotation tags, e.g., Dublin core
– Problems with this approach
• Inflexible
• Limited number of things can be expressed
2. Use on-line 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
Semantic Web takes second approach
Characteristics of the Semantic Web
• Part of the Web
– Uses Web addressing (URIs)
– Adheres to Web philosophy
– Connected to the rest of the Web (e.g, services)
• Large part of the Web
– Very many connected documents
– Diverse, conflicting
• Semantic
– Contains ontological information about meaning of
objects
What are
Ontologies?
Ontology: Origins and History
Ontology in Philosophy
a philosophical discipline—a branch of philosophy that
deals with the nature and the organisation of reality
• Science of Being (Aristotle, Metaphysics, IV, 1)
• Tries to answer the questions:
What characterizes being?
Eventually, what is being?
Ontology in Linguistics
Concept
Relates to
activates
Form
“Tank“
[Ogden, Richards, 1923]
Stands for
Referent
?
Ontology in Computer Science
• An ontology is an engineering artifact:
– It is 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]
Structure of an Ontology
Ontologies typically have two distinct 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
– Adult_Elephant is a concept whose members are exactly those
elephants whose age is greater than 20 years
• 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
Ontology Design and Deployment
• Given key role of ontologies in the Semantic Web, it will be
essential to provide tools and services to help users:
– Design and maintain high quality ontologies, e.g.:
• Meaningful — all named classes can have instances
• Correct — captured intuitions of domain experts
• Minimally redundant — no unintended synonyms
• Richly axiomatised — (sufficiently) detailed descriptions
– 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
Ontology Languages
• Wide variety of languages for “Explicit Specification”
– Graphical notations
• Semantic networks
• Topic Maps (see http://www.topicmaps.org/)
• UML
• RDF
– Logic based
• Description Logics (e.g., OIL, DAML+OIL, OWL)
• Rules (e.g., RuleML, LP/Prolog)
• First Order Logic (e.g., KIF)
• Conceptual graphs
• (Syntactically) higher order logics (e.g., LBase)
• Non-classical logics (e.g., Flogic, Non-Mon, modalities)
– Probabilistic/fuzzy
• Degree of formality varies widely
– Increased formality makes languages more amenable to machine
processing (e.g., automated reasoning)
Many ontology languages use
“object oriented” model based on
• Objects/Instances/Individuals
– Elements of the domain of discourse
• Types/Classes/Concepts
– Sets of objects sharing certain characteristics
• Relations/Properties/Roles
– Sets of pairs (tuples) of objects
• Such languages are/can be:
–
–
–
–
Well understood
Formally specified
(Relatively) easy to use
Amenable to machine processing
Description Logics are a family of such ontology languages
What are
Description Logics?
Description Logics
• A family of logics that can be used to represent ontologies
• Based on
– Individuals, e.g., John
– Concepts, e.g., Person, Parent
– Roles, e.g, childOf
• Concepts can have defining characteristics
– E.g., Parent is precisely those Persons who have a child
• Description Logics are
–
–
–
–
Well understood
Formally specified with model-theoretic semantics
(Relatively) easy to use
Amenable to machine processing
Sample Description Logic
Ontology Fragment
child  Person  Person
father  inverse(child)
spouse  Person  Person
spouse is symmetric
Student  Person
Parent = Person  1 child
john  Person  name=“John Smith”
<john,sally>  spouse
sally  name=“Sally Brown”
<sally,paul>  father
paul  Person   0 spouse
Description Logic Inferences
sally  Person  spouse:Person
because sally is john’s spouse, spouse’s range is Person, and spouse is
symmetric
paul  Parent
because paul is a Person, paul is sally’s father, father is a subrole of the inverse
of child
paul  spouse : {sally}
because paul has no spouse
mary  ( spouse : Person )  ( spouse:  {john} )
because if mary has a non-Person spouse, it can’t be john,
because john is a Person
Reasoning in Description Logics
• Description Logics can be quite complex
– Boolean constructs (, , )
– (Counted) modalities (, , , )
– Concept definitions (=)
• Determining inference in Description Logics can be difficult
– Computationally intractable (EXPTIME complete or worse)
– Generally decidable, however
• Nevertheless, systems exist that are effective in practice
–
–
–
–
Highly optimized, tuned for normal situations
Always return the right answer
Almost always return very quickly
E.g., FaCT, RACER, DLP
Characteristics of Description Logics
• Logics
– Formal basis
– Model theory provides meaning
– Inference can be used to determine implicit information
• Ontology Languages
– Provide definitions for terms (concepts)
– Provide information about individuals
• Expressive
– Can say lots of things (but not everything)
– Determining inference is difficult
• Implemented
– Powerful systems exist (FaCT, DLP, RACER)
(Representation)
Languages
for the Semantic Web
Initial Semantic Web Languages
• RDF (Resource Description Framework)
– W3C recommendation (http://www.w3.org/RDF)
– RDF is a language ( + XML syntax + semantics)
• for representing metadata
• for describing the semantics of information in a machineaccessible way
• RDFS (RDF Schema) extends RDF with “schema
vocabulary”
– Class, Property
– type, subClassOf, subPropertyOf
– range, domain
• RDFS is a very simple ontology language
The RDF Data Model
• Statements are <subject, predicate, object> triples:
<Ian,hasColleague,Uli>
• Can be represented as a graph:
Ia
n
hasColleague
Ul
i
• Statements describe properties of resources
• A resource is any object that can be pointed to by a URI:
–
–
–
–
–
a document, a picture, a paragraph on the Web;
http://www.cs.man.ac.uk/index.html
a book in the library, a real person (?)
isbn://5031-4444-3333
…
• Properties themselves are also resources (URIs)
URIs
• URI = Uniform Resource Identifier
• "The generic set of all names/addresses that are
short strings that refer to resources"
• URLs (Uniform Resource Locators) are a particular
type of URI, used for resources that can be accessed
on the WWW (e.g., web pages)
• In RDF, URIs typically look like “normal” URLs, often
with fragment identifiers to point at specific parts of a
document:
– http://www.somedomain.com/some/path/to/file#fragmentID
RDF Syntax
•
•
•
•
RDF has an XML syntax
Every Description element describes a resource
Every attribute or nested element inside a Description is a property of
that Resource
We can refer to resources by using URIs
<Description about="some.uri/person/ian_horrocks">
<hasColleague resource="some.uri/person/uli_sattler"/>
</Description>
<Description about="some.uri/person/uli_sattler">
<hasHomePage>http://www.cs.mam.ac.uk/~sattler</hasHomePage>
</Description>
<Description about="some.uri/person/carole_goble">
<hasColleague resource="some.uri/person/uli_sattler"/>
</Description>
RDF Schema (RDFS)
•
RDF gives a language for meta data annotation, and a way to write it
down in XML, but it does not provide any way to structure the
annotations
•
RDF Schema augments RDF to allow you to define vocabulary terms
and the relations between those terms
– it gives “extra meaning” to particular RDF predicates and resources
– e.g., Class, subClassOf, domain, range
•
These terms are the RDF Schema building blocks (constructors) used
to create vocabularies:
<Person,type,Class>
<hasColleague,type,Property>
<hasColleague,range,Person>
<hasColleague,domain,Person>
<Professor,subClassOf,Person>
<Carole,type,Professor>
<Carole,hasColleague,Ian>
RDF and RDFS circa 2001
• Initial definition of RDF and RDFS was informal
– A document giving an English description of what everything meant
– Not adequate for representation
• Debate on exact meaning of constructs, e.g., blank nodes
– Similar to problems with informal Knowledge Representation work
• W3C chartered the RDF Core Working Group to fix this (and
other problems)
– Produced cleaned up syntax for RDF
– Produced formal semantics
• RDF and RDFS are now real representation languages
– Formal syntax, formal semantics, inference
Semantics and Model Theories
•
•
•
•
Ontology/KR languages aim to model (part of) world
Terms in language correspond to entities in world
Meaning given by a Model Theory (MT)
MT defines relationship between syntax and interpretations
– Can be many interpretations (models) of one piece of syntax
– Models are supposed to be analogue of (part of) world
• E.g., elements of model correspond to objects in world
– Formal relationship between syntax and models
• Structure of models reflect relationships specified in syntax
– Inference (e.g., subsumption) is defined in terms of MT
• E.g., T ² A  B iff in every model of T, ext(A)  ext(B)
RDF/RDFS Semantics
• RDF has non-standard semantics to deal with certain bits of RDF
• Semantics given by RDF Model Theory (MT)
• In RDF MT, an interpretation I of a vocabulary V consists of:
– IR, a non-empty set of resources
– IS, a mapping from V into IR
– IP, a distinguished subset of IR (the properties)
• A vocabulary element v 2 V is a property iff IS(v) 2 IP
– IEXT, a mapping from IP into the powerset of IR£IR
• I.e., a set of elements <x,y>, with x,y elements of IR
– IL, a mapping from typed literals into IR
• Class interpretation ICEXT simply induced by IEXT(IS(type))
• ICEXT(C) = {x | <x,C> 2 IEXT(IS(type))}
RDFS Interpretations
• RDFS adds extra constraints on interpretations
– E.g., interpretations of <C,subClassOf,D> constrained to those
where ICEXT(IS(C)) µ ICEXT(IS(D))
• Can deal with triples such as
– <Species,type,Class>
<Lion,type,Species>
<Leo,type,Lion>
– <SelfInst,type,SelfInst>
• And even with very unusual triples such as
– <type,subPropertyOf,subClassOf>
• But not clear if meaning of unusual triples matches intuition (if
there is one)
Problems with RDFS
• RDFS too weak to describe resources in sufficient detail
– No localised range and domain constraints
• Can’t say that the range of hasChild is person when applied to
persons and elephant when applied to elephants
– No existence/cardinality constraints
• Can’t say that all instances of person have a mother that is also
a person, or that persons have exactly 2 parents
– No transitive, inverse or symmetrical properties
• Can’t say that isPartOf is a transitive property, that hasPart is
the inverse of isPartOf, or that touches is symmetrical
– Need to extend RDFS to provide better support for ontologies
• Difficult to provide reasoning support
– No “native” reasoners for non-standard semantics
– May be possible to reason via FO axiomatisation
An Ontology Language
for the Semantic Web
• Create a richer ontology language for the Semantic Web
• Desirable features identified for Web Ontology Language:
– Extend existing Web standards
• Such as 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
(In)famous “Layer Cake”
???
???
???
 Semantics+reasoning
 Relational Data
 Data Exchange
?
?
• Relationship between layers is not clear
From RDF to OWL
•
Two languages were developed to satisfy above requirements
– OIL: developed by group of (largely) European researchers (several from
EU OntoKnowledge project)
– DAML-ONT: developed by group of (largely) US researchers (in DARPA
DAML programme)
•
Efforts merged to produce DAML+OIL http://www.daml.org/language/
– Development was carried out by “Joint EU/US Committee on Agent Markup
Languages”
– DAML+OIL extends (“DL subset” of) RDF
•
DAML+OIL submitted to W3C as basis for standardisation
– Web-Ontology (WebOnt) Working Group formed
– WebOnt group developed OWL language based on DAML+OIL
– OWL language now a W3C Recommendation
DAML+OIL Characteristics
• Web language for Ontologies
– Uses Web syntax (XML, RDF/XML)
– Uses URI references as identifiers
– Has a notion of Web documents
– Has formal semantics and decidable inference
– Can be implemented (very close to languages already supported)
• Not completely integrated with Semantic Web
– Formal semantics not related to formal semantics of RDF
• Because RDF semantics had not yet been developed!
• Therefore work needed to be done to integrate RDF and
ontology language semantics
The OWL Language (Overview)
• OWL is an ontology language, based on ideas from
Description Logics
– Well defined semantics
• OWL extends the expressive power of RDFS
– Can talk about defined properties of classes
• OWL is not a full first-order language
• OWL DL (the Description Logic subset of OWL)
benefits from many years of DL research
– Formal properties well understood (complexity, decidability)
– Known reasoning algorithms
– Implemented systems (highly optimised)
The OWL Language (Details)
• Three species of OWL
– OWL full is union of OWL syntax and RDF
– OWL DL restricted to well-behaved fragment (¼ DAML+OIL)
– OWL Lite is “easier to implement” subset of OWL DL
• Semantic layering
– OWL DL ¼ OWL full within DL fragment
– DL semantics officially definitive
• OWL DL based on SHIQ Description Logic
– In fact it is equivalent to SHOIN(Dn) DL
• 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)
OWL Ontology Fragment
OWL class definition in RDF/XML syntax (taken from OWL wine
and food ontology)
<owl:Class rdf:ID="WhiteWine">
<owl:intersectionOf rdf:parseType="Collection">
<owl:Class rdf:about="#Wine" />
<owl:Restriction>
<owl:onProperty rdf:resource="#hasColor" />
<owl:hasValue rdf:resource="#White" />
</owl:Restriction>
</owl:intersectionOf>
</owl:Class>
In other (more readable) words
A WhiteWine is precisely a Wine that has color White.
(From now on, I will not use this RDF/XML syntax!)
OWL Ontology Fragment
wine:Wine  food:PotableLiquid  1 wine:madeFromGrape) 
(=1 wine:hasMaker)   wine:hasMaker wine:Winery) 
(=1 wine:hasColor)  ...
wine:madeFromGrape wine:Wine  wine:WineGrape
wine:hasColor wine:Wine  wine:WineColor
wine:WineColor = { wine:White, wine:Rose, wine:Red }
wine:White  wine:Rose wine:White  wine:Red wine:Rose  wine:Red
wine:WineDescriptor = wine:WineTaste  wine:WineColor
wine:WhiteWine = wine:Wine  ( wine:hasColor wine:White )
wine:Riesling = wine:Wine  1 wine:madeFromGrape) 
(wine:madeFromGrape wine:RieslingGrape )
wine:Riesling  wine:hasColor wine:White
wine:CorbansDryWhiteRiesling  wine:Riesling 
(wine:hasMaker wine:Corbans)  ...
Consequences of the Fragment
OWL is a logic, with an entailment (consequence) relationship.
For example, the ontology fragment above entails:
wine:Riesling  wine:WhiteWine
wine:Riesling  wine:WhiteWine  (=1 wine:hasMaker)
wine:Riesling  (wine:hasColor : wine:Red)  wine:WineDescriptor
wine:CorbansDryWhiteRiesling  wine:WhiteWine
Determining consequence in OWL is difficult
– NEXPTIME complete for the DL fragment of OWL
– Sound and complete reasoners not (quite) yet ``practical''
What You Can't Say in OWL
• Certain kinds of disjunction
– ``Either Saddam is a terrorist or Bush is a liar''
– But can say ``Bush is either a terrorist or a liar''
• ex:Bush  ex:Terrorist  ex:Liar
• Many kinds of universal quantification
– ``For every student and university, there is an application by that
student to that university''
– But can say ``Every student applies to at least five universities''
• ex:Student  ( 5 ex:application)  (ex:application ex:University)
• Many kinds of relationships between properties
– ``Uncle is the composition of parent and brother''
• Limitations are in place to keep DL fragment of OWL decidable
What Can be Done in OWL
• Build ontologies
– What is an ontology? (See below)
• State facts (including disjunctive and vague facts)
– What is the difference between an ontology and a bunch of
–
facts? (Not much!)
• OWL specifies the consequences of an ontology
– What sorts of consequences? (Whatever can be said in
OWL.)
• OWL specifies the consequences of a bunch of facts
– What sorts of consequences? (Whatever can be said in
OWL)
– Is determining consequence different from retrieval? (Yes, in
some sense, as consequence provides YES/NO answers.)
OWL DL Semantics
• Mapping OWL to equivalent DL (SHOIN(Dn)):
– Facilitates provision of reasoning services (using DL systems)
– Provides well defined semantics
• DL semantics defined by interpretations: I = (DI, ¢I), where
– DI is the domain (a non-empty set)
– ¢I is an interpretation function that maps:
• Concept (class) name A ! subset AI of DI
• Role (property) name R ! binary relation RI over DI
• Individual name i ! iI element of DI
Inference Tasks
• Knowledge is correct (captures intuitions)
– C subsumes D w.r.t. K iff for every model I of K, CI µ DI
• Knowledge is minimally redundant (no unintended synonyms)
– C is equivallent to D w.r.t. K iff for every model I of K, CI = DI
• Knowledge is meaningful (classes can have instances)
– C is satisfiable w.r.t. K iff there exists some model I of K s.t. CI  ;
• Querying knowledge
– x is an instance of C w.r.t. K iff for every model I of K, xI 2 CI
– hx,yi is an instance of R w.r.t. K iff for, every model I of K, (xI,yI) 2 RI
• Knowledge base consistency
– A KB K is consistent iff there exists some model I of K
Performing these tasks in OWL DL is difficult (intractable) but possible
(decidable)
OWL as a Semantic Web Language
Talking the Web Talk
• OWL uses URI references as identifiers
• OWL has an XML syntax
• OWL syntax is compatible with RDF syntax
• OWL uses media type application/xml+rdf
OWL as a Semantic Web Language
Walking the Web Walk
• OWL is compatible with RDF semantics
• OWL has interdocument references
– Can pick and choose which documents to use
– Don‘t need to consider the entire Web
• Handling inconsistency is delegated to higherlevel components
– Component that picks which documents to start
with
OWL as a
Knowledge Representation Language
• OWL is an ontology language
– Provides constructs for defining concepts and roles
– Provides constructs for providing information about
individuals
– Based on Description Logics
• OWL has a formal semantics
– Determines meaning of OWL constructs
– Provides notions of inference
• OWL is can be effectively implemented
– Similar to Description Logics underlying FaCT, DLP, RACER
Conclusion
• OWL is an existence proof that the Semantic
Web and Knowledge Representation can help
each other
– Knowledge Representation provides expressive
power, formal rigor, ways around computational
difficulties
– The Web provides syntax, global name space,
documents, locality
The End?
Not nearly!
• This story is not finished
– Semantic Web just starting
– OWL tools not nearly mature
• Need
– Users – willing to work in an experimental area
– Developers – more tools for OWL, including
ontology-development environments and better
interfaces