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The Semantic Web and Ontologies

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The Semantic Web and Ontologies OntoGrid Semantic Grid Tutorial, February, 2007, Manchester, UK Sean Bechhofer, School of Computer Science, University of Manchester, UK – PowerPoint PPT presentation

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Title: The Semantic Web and Ontologies


1
The Semantic Web and Ontologies
  • OntoGrid Semantic Grid Tutorial,
  • February, 2007, Manchester, UK
  • Sean Bechhofer,
  • School of Computer Science, University of
    Manchester, UK

2
The Semantic Web Vision
  • The Web was made possible through established
    standards
  • TCP/IP for transporting bits down a wire
  • HTTP HTML for transporting and rendering
    hyperlinked text
  • Applications able to exploit this common
    infrastructure
  • Result is the WWW as we know it
  • 1st generation web mostly handwritten HTML pages
  • 2nd generation (current) web often machine
    generated/active
  • Both intended for direct human processing/interact
    ion
  • In the next generation web, resources should be
    more accessible to automated processes
  • To be achieved via semantic markup
  • Metadata annotations that describe
    content/function
  • Coincides with vision of a Semantic Web

3
History of the Semantic Web
  • Web was invented by Tim Berners-Lee (amongst
    others), while working at CERN
  • TBLs original vision of the Web was much more
    ambitious than the reality of the existing
    (syntactic) Web
  • A number of researchers 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

... 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.
4
Scientific American, May 2001
Chuck D sez
Dont Believe the Hype!
  • Realising the complete vision is too hard for
    now (probably)
  • But we can make a start by adding semantic
    annotation to web resources

5
Where we are Today the Syntactic Web
  • 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?

6
Hard Work using the Syntactic Web
Find images of Steve Furber
Carole Goble
Alan Rector
Rev. Alan M. Gates, Associate Rector of the
Church of the Holy Spirit, Lake Forest, Illinois
7
Whats the Problem?
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
8
Information we can see
WWW2006 Edinburgh, Scotland The eleventh
international world wide web conference 23rd--26th
May Edinburgh International Conference
Centre Who should attend and who will you
meet? No other event draws the breadth Look
Whos Talking Richard Granger reviews the
revamping of the NHS IT programme Look Whos
Talking VeriSign's pincipal scientist, Dr Phillip
Hallam-Baker, goes phishing... Registration
opens with special offer tickets Professor Wendy
Hall has announced the opening of registration
for the 15th annual World Wide Web Conference
2006
9
Information a machine can see
WWW2002 The eleventh international world wide
webcon 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,
10
Solution XML markup with meaningful tags?
ltnamegtWWW2002 The eleventh international world
wide webconlt/namegt ltdategt7-11 may 2002lt/dategt
ltlocationgtSheraton waikiki hotel Honolulu,
hawaii, USAlt/locationgt ltintroductiongtRegister
now On the 7th May Honolulu will provide the
backdrop of the eleventh international world wide
web conference. This prestigious event Speakers
confirmedlt/introductiongt ltspeakergtTim
berners-lee ltbiogtTim is the well known inventor
of the Web,lt/biogt lt/speakergt ltspeakergtTim
berners-lee ltbiogtTim is the well known inventor
of the Web,lt/biogt lt/speakergt ltregistrationgtRegist
ered 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,
zaireltregistrationgt
11
But What About?
ltconfgtWWW2002 The eleventh international world
wide webconltconfgt ltdategt7-11 may 2002lt/dategt
ltplacegtSheraton waikiki hotel Honolulu, hawaii,
USAltplacegt ltintroductiongtRegister now On the 7th
May Honolulu will provide the backdrop of the
eleventh international world wide web conference.
This prestigious event Speakers
confirmedlt/introductiongt ltspeakergtTim
berners-lee ltbiogtTim is the well known inventor
of the Web,lt/biogt lt/speakergt ltspeakergtTim
berners-lee ltbiogtTim is the well known inventor
of the Web,lt/biogt lt/speakergt ltregistrationgtRegist
ered 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,
zaireltregistrationgt
12
Still the Machine only sees
lt????gtWWW2002 The eleventh international world
wide webconlt????gt lt????gt7-11 may 2002lt/????gt
lt?????gtSheraton waikiki hotel Honolulu, hawaii,
USAlt?????gt lt????????????gtRegister now On the 7th
May Honolulu will provide the backdrop of the
eleventh international world wide web conference.
This prestigious event Speakers
confirmedlt/????????????gt lt???????gtTim
berners-lee lt???gtTim is the well known inventor
of the Web,lt/???gt lt/???????gt lt???????gtTim
berners-lee lt???gtTim is the well known inventor
of the Web,lt/???gt lt/???????gt lt????????????gtRegist
ered 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,
zairelt????????????gt
13
Need to Add Semantics
  • External agreement on meaning of annotations
  • E.g., Dublin Core for annotation of
    library/bibliographic information
  • Agree on the meaning of a set of annotation tags
  • Problems with this approach
  • Inflexible
  • 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
  • Conceptual Lego
  • Meaning (semantics) of such terms is formally
    specified
  • Can also specify relationships between terms in
    multiple ontologies

Machine Processable not Machine Understandable
14
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.
  • Almost always including how concepts should be
    classified
  • 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

15
Building a Semantic Web
  • Annotation
  • Associating metadata with resources
  • Integration
  • Integrating information sources
  • Inference
  • Reasoning over the information we have.
  • Could be light-weight (taxonomy)
  • Could be heavy-weight (logic-style)
  • Interoperation and Sharing are key goals

16
Languages
  • Work on Semantic Web has concentrated on the
    definition of a collection or stack of
    languages.
  • These languages are then used to support the
    representation and use of metadata.
  • The languages provide basic machinery that we can
    use to represent the extra semantic information
    needed for the Semantic Web
  • XML
  • RDF
  • RDF(S)
  • OWL

17
Ontology Languages
  • We need languages that allow us to represent this
    information
  • Ontology Languages!
  • There are a wide variety of languages for this
    Explicit Specification
  • Graphical
  • Semantic Networks, Topic Maps, UML, RDF
  • Logical
  • Description Logics, First Order Logic, Rules,
    Conceptual Graphs

mother(X,M) - parent(X,M), female(M). father(X,F)
- parent(X,F), male(F). sister(X,S) -
female(S), parent(S,P), parent(X,P), X \
S. male(james1). male(charles1). male(charles2).
male(james2). male(george1). female(catherine). fe
male(elizabeth). female(sophia). parent(charles1,
james1). parent(elizabeth, james1). parent(charles
2, charles1). parent(catherine,
charles1). parent(james2, charles1). parent(sophia
, elizabeth). parent(george1, sophia).
Every gardener likes the sun 8x.gardener(x) )
likes(x, Sun) You can fool some of the people all
of the time 9x.8t.(person(x) Æ time(t)) )
can-fool(x,t) You can fool all of the people some
of the time 8x.9t.(person(x) Æ time(t)) )
can-fool(x,t) All purple mushrooms are poisonous
8x.(mushroom(x) Æ purple(x)) ) poisonous(x) No
purple mushroom is poisonous 9x.(mushroom(x)
Æ purple(x) Æ poisonous(x)) 8x.(mushroom(x) Æ
purple(x)) ) poisonous(x) There are exactly two
purple mushrooms 9x.9y.mushroom(x) Æ purple(x)
Æ mushroom(y) Æ purple(y) Æ (xy) Æ
(8x.mushroom(z) Æ purple(z) ) ((xz) _
(yz))) Clinton is not tall tall(Clinton)
18
Object Oriented Models
  • Many languages use an object oriented model
    with
  • 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
  • Such languages are/can be
  • Well understood
  • Formally specified
  • (Relatively) easy to use
  • Amenable to machine processing

19
Why (Formal) Semantics?
  • Increased formality makes languages more amenable
    to machine processing (e.g. automated reasoning).
  • The formal semantics provides an unambiguous
    interpretation of the descriptions.
  • What does an expression in an ontology language
    mean?
  • The semantics of a language tell us precisely how
    to interpret a complex expression.
  • Well defined semantics are vital if we are to
    support machine interpretability
  • They remove ambiguities in the interpretation of
    the descriptions.

Telephone
Black
?
20
RDF
  • RDF stands for Resource Description Framework
  • It is a W3C Recommendation
  • http//www.w3.org/RDF
  • RDF is a graphical formalism ( XML syntax
    semantics)
  • for representing metadata
  • for describing the semantics of information in a
    machine- accessible way
  • Provides a simple data model based on triples.

21
The RDF Data Model
  • Statements are ltsubject, predicate, objectgt
    triples
  • ltSean,hasColleague,Iangt
  • Can be represented as a graph
  • Statements describe properties of resources
  • A resource is any object that can be pointed to
    by a URI
  • The generic set of all names/addresses that are
    short strings that refer to resources
  • 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//0141184280
  • Properties themselves are also resources (URIs)

hasColleague
Sean
Ian
22
Linking Statements
  • The subject of one statement can be the object of
    another
  • Such collections of statements form a directed,
    labeled graph
  • The object of a triple can also be a literal (a
    string)

Sean K. Bechhofer
hasName
hasColleague
Sean
Ian
hasHomePage
hasColleague
Carole
http//www.cs.man.ac.uk/horrocks
23
RDF Syntax
  • RDF has an XML syntax that has a specific
    meaning
  • 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 URIs

ltrdfDescription rdfabout"some.uri/person/sean_b
echhofer"gt ltohasColleague resource"some.uri/pe
rson/ian_horrocks"/gt ltohasName
rdfdatatype"xsdstring"gtSean K.
Bechhoferlt/ohasNamegt lt/rdfDescriptiongt ltrdfDesc
ription rdfabout"some.uri/person/ian_horrocks"gt
ltohasHomePagegthttp//www.cs.mam.ac.uk/horrocks
lt/ohasHomePagegt lt/rdfDescriptiongt ltrdfDescripti
on rdfabout"some.uri/person/carole_goble"gt
ltohasColleague resource"some.uri/person/ian_horr
ocks"/gt lt/rdfDescriptiongt
24
What does RDF give us?
  • A mechanism for annotating data and resources.
  • Single (simple) data model.
  • Syntactic consistency between names (URIs).
  • Low level integration of data.

25
RDF(S) RDF Schema
  • RDF gives a formalism for meta data annotation,
    and a way to write it down in XML, but it does
    not give any special meaning to vocabulary such
    as subClassOf or type (supporting OO-style
    modelling)
  • Interpretation is an arbitrary binary relation
  • RDF Schema extends RDF with a schema vocabulary
    that allows you to define basic vocabulary terms
    and the relations between those terms
  • Class, type, subClassOf,
  • Property, subPropertyOf, range, domain
  • it gives extra meaning to particular RDF
    predicates and resources
  • this extra meaning, or semantics, specifies how
    a term should be interpreted

26
RDF(S) Inference
rdfsClass
rdftype
Person
rdftype
rdfssubClassOf
rdftype
Academic
rdfssubClassOf
rdfsubClassOf
Lecturer
27
RDF(S) Inference
rdfsClass
rdftype
Academic
rdftype
rdfssubClassOf
Lecturer
rdfstype
rdftype
Sean
28
What does RDF(S) give us?
  • Ability to use simple schema/vocabularies when
    describing our resources.
  • Consistent vocabulary use and sharing.
  • Simple inference
  • CS AktiveSpace
  • Lightweight schema to integrate data from
    University sites
  • myGrid
  • Service descriptions for e-Science

29
Problems with RDFS
  • RDFS is too weak to describe resources in
    sufficient detail
  • No localised range and domain constraints
  • Cant say that the range of hasChild is person
    when applied to persons and elephant when applied
    to elephants
  • No existence/cardinality constraints
  • Cant 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
  • Cant say that isPartOf is a transitive property,
    that hasPart is the inverse of isPartOf or that
    touches is symmetrical
  • It can be difficult to provide reasoning support
  • No native reasoners for non-standard semantics
  • May be possible to reason via FO axiomatisation

30
Web Ontology Language Requirements
  • Desirable features identified for Web Ontology
    Language
  • Extends existing Web standards
  • Such as XML, RDF, RDFS
  • Easy to understand and use
  • Should be based on familiar KR idioms (e.g.
    OO-style, frames etc).
  • Formally specified
  • Of adequate expressive power
  • Possible to provide automated reasoning support

31
The OWL Family Tree
DAML
RDF/RDF(S)
DAML-ONT
Joint EU/US Committee
DAMLOIL
OWL
Frames
OIL
W3C
OntoKnowledgeOthers
Description Logics
32
OWL
  • W3C Recommendation (February 2004)
  • Well defined RDF/XML serializations
  • A family of Languages
  • OWL Full
  • OWL DL
  • OWL Lite
  • Formal semantics
  • First Order (DL/Lite)
  • Relationship with RDF
  • Comprehensive test cases for tools/implementations
  • Growing industrial takeup.

33
OWL Basics
  • Set of constructors for concept expressions
  • Booleans and/or/not
  • Quantification some/all
  • Axioms for expressing constraints
  • Necessary and Sufficient conditions on classes
  • Disjointness
  • Property characteristics transitivity, inverse
  • Facts
  • Assertions about individuals

34
Reasoning with OWL
  • OWL (DL) has a well defined semantics that tells
    us how to interpret expressions in the language.
  • This semantics corresponds to traditional
    interpretations given to first order logic or
    subsets of FOL like Description Logics.
  • OWL DL based on a well understood Description
    Logic (SHOIN(Dn))
  • Formal properties well understood (complexity,
    decidability)
  • Known reasoning algorithms
  • Implemented systems (highly optimised)
  • Because of this, we can reason about OWL
    ontologies, allowing us to draw inferences from
    the basic facts that we provide.

35
Reasoning Tasks
Sean Bechhofer Concrete Examples Grid/VO? GONG?
  • Subsumption reasoning
  • Allows us to infer when one class is a subclass
    of another
  • Can then build concept hierarchies representing
    the taxonomy.
  • This is classification of classes.
  • Satisfiability reasoning
  • Tells us when a concept is unsatisfiable
  • i.e. when it is impossible to have instances of
    the class.
  • Allows us to check whether our model is
    consistent.
  • Instance Retrieval/Instantiation
  • What are the instances of a particular class C?
  • What are the classes that x is an instance of?

36
Classification
37
Why Reasoning?
  • Reasoning can be used as a design support tool
  • Check logical consistency of classes
  • Compute implicit class hierarchy
  • May be less important in small local ontologies
  • Can still be useful tool for design and
    maintenance
  • Much more important with larger
    ontologies/multiple authors
  • Valuable tool for integrating and sharing
    ontologies
  • Use definitions/axioms to establish
    inter-ontology relationships
  • Check for consistency and (unexpected) implied
    relationships
  • Basis for answering queries.
  • Reasoning can help underpin the provision of the
    machine processing required of the Semantic Web.

38
What does OWL give us?
  • Rich language for describing domain models.
  • Unambiguous interpretations of complex
    descriptions.
  • The ability to use inference to manage our
    vocabularies.
  • GONG
  • VO Formation
  • PhosphaBase

39
More Languages
  • RDF, RDF(S) and OWL provide basic
    representational capabilities.
  • We also need mechanisms that allow us to access
    and query the information.
  • RDF has an underlying concrete syntax based on
    XML. Why not just use something like XPath to
    query the RDF?
  • RDQL, RQL, SeRQL,
  • W3C Data Access Working Group attempting to
    standardise on SPARQL
  • Elements of the earlier languages with a
    well-defined semantic basis
  • OWL-QL Query language for OWL.
  • Allow specification of conjunctive queries using
    OWL concept expressions
  • Also investigations into extensions of the
    expressivity of OWL.
  • Rules

40
Potential Pitfalls
41
Conflicting Views
  • The Semantic Web community is diverse, with a
    rough division between the neats and the
    scruffies.
  • Neats
  • Logic and languages
  • Completeness/decidability
  • Top down, well-behaved
  • Heavyweight
  • Rich ontologies
  • OWL
  • Scruffies
  • Practice
  • Bottom up/real-world
  • Lightweight
  • Folksonomies
  • FOAF
  • RDF

42
Semantic Web vs Semantic Web
  • Semantics/AI/KR community with little attention
    paid to Web aspects
  • Youre not doing it properly
  • Web community with little attention paid to
    Semantics.
  • Just stick everything in a big RDF store and
    itll all be fine
  • Diversity can be healthy, but can also lead to
    fragmentation and pointless arguments.

Splitters!
43
Tools and Services
  • We need to provide tools and services to help
    users to
  • 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

44
How thick is your infrastructure?
  • Sharing is about interoperations. Ensuring that
    when you look at or process my data, you do it in
    a consistent way.
  • Thick infrastructure can help interoperability.
    Clients dont have to guess how to interpret
    things.
  • But can be harder to build

Thin Apps
Thin Apps
Thick Infrastructure
45
How thick is your infrastructure?
  • A lightweight infrastructure (e.g. RDF) means
    that clients/apps have to do more. And may do it
    differently.
  • Metadata can end up being locked away within the
    applications where others cant get at it. Is
    that sharing? Are you exposing the semantics?

Thick Apps
Thick Apps
Thin Infrastructure
46
Trust and Security
  • Publishing my information in machine-processable
    forms may allow you to
  • Work out what Im doing
  • Integrate across multiple sources to produce new
    conclusions
  • How do I control this?
  • We need mechanisms that will allow us to control
    access to knowledge
  • We need mechanisms that allow us to ascribe
    provenance and trust information to our
    knowledge.
  • The SW stack sees these at the top. Some of
    this has to come from the bottom though.

47
Scalability
  • Will this stuff work on a web scale?
  • Millions of triples/fact
  • Thousands of ontologies
  • Are you ever going to get global agreements?

48
Language Summary
  • Weve seen some of the technology being proposed
    as a basis for building the Semantic Web
  • These languages provide basic machinery that we
    can use to represent the extra semantic
    information needed for the Semantic Web
  • XML
  • RDF
  • RDF(S)
  • OWL
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