the semantic web and ontologies

The Semantic Web and Ontologies

OntoGrid Semantic Grid Tutorial,

February, 2007, Manchester, UK

Sean Bechhofer,School of Computer Science, University of Manchester, UK

OntoGrid: Semantic Grid Tutorial 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/interaction

• 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


History of the Semantic Web• Web was “invented” by Tim Berners-Lee (amongst others), while

working at CERN• TBL’s 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.

http://www.w3.org/People/Berners-Lee/


• Realising the complete “vision” is too hard for now (probably)• But we can make a start by adding semantic annotation to web resources

Scientific American, May 2001:

Don’t Believe

the Hype!

Chuck D sez:


Where we are Today: the Syntactic Web

Resource

ResourceResource Resource Resource

ResourceResource Resource

Resource

Resource

hrefhrefhref

hrefhrefhref

hrefhrefhref

href href

href

• 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?


Hard Work using the Syntactic Web…

Find images of Steve Furber

Rev. Alan M. Gates, Associate Rector of the Church of the Holy Spirit, Lake Forest, Illinois

…Carole Goble … Alan Rector…


What’s 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…


Information we can see…WWW2006 Edinburgh, ScotlandThe eleventh international world wide web conference23rd--26th MayEdinburgh International Conference Centre

Who should attend and who will you meet?No other event draws the breadth…

Look Who’s TalkingRichard Granger reviews the revamping of the NHS IT programme

Look Who’s TalkingVeriSign's pincipal scientist, Dr Phillip Hallam-Baker, goes phishing...

Registration opens with special offer ticketsProfessor Wendy Hall has announced the opening of registration for the 15th annual World Wide Web Conference 2006…


…

…

Information a machine can see…


Solution: XML markup with “meaningful” tags?

<name> </name><date> </date> <location> </location>

<introduction>

… </introduction><speaker> <bio> </bio></speaker><speaker> <bio> </bio></speaker> <registration>

<registration>


But What About…?

<conf> <conf><date> </date> <place> <place>

<introduction>

… </introduction><speaker> <bio> </bio></speaker><speaker> <bio> </bio></speaker> <registration>

<registration>


Still the Machine only sees…

< > < >< > </ > <> <>

< >

… </ >< > < > </ ></ >< > < > </ ></ > < >

< >


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


• 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

Ontology in Computer Science


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


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– …

OWL

Integration

RDF(S)

RDF

XML

Annotation

Integration

Inference


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

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 (:x=y) Æ (8x.mushroom(z) Æ purple(z) ) ((x=z) _ (y=z)))Clinton is not tall : tall(Clinton)

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).female(elizabeth).female(sophia).parent(charles1, james1).parent(elizabeth, james1).parent(charles2, charles1).parent(catherine, charles1).parent(james2, charles1).parent(sophia, elizabeth).parent(george1, sophia).


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


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.

Black

Telephone

?


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.


The RDF Data Model• Statements are <subject, predicate, object> triples:

– <Sean,hasColleague,Ian>

• 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)

Sean

IanhasColleague


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

Ian

hasColleague

Carole

http://www.cs.man.ac.uk/~horrocks

hasColleague

hasHomePage

“Sean K. Bechhofer”

hasName


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

<rdf:Description rdf:about="some.uri/person/sean_bechhofer"> <o:hasColleague resource="some.uri/person/ian_horrocks"/> <o:hasName rdf:datatype="&xsd;string">Sean K. Bechhofer</o:hasName></rdf:Description><rdf:Description rdf:about="some.uri/person/ian_horrocks"> <o:hasHomePage>http://www.cs.mam.ac.uk/~horrocks</o:hasHomePage></rdf:Description><rdf:Description rdf:about="some.uri/person/carole_goble"> <o:hasColleague resource="some.uri/person/ian_horrocks"/></rdf:Description>


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.


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


RDF(S) Inference

Lecturer

Academic

Person

rdfs:subClassOf

rdf:subClassOf

rdfs:subClassOf

rdf:type

rdfs:Class

rdf:type

rdf:type


RDF(S) Inference

Sean

Lecturer

rdf:type

rdfs:Class

Academic

rdfs:subClassOf

rdf:type

rdf:type

rdfs:type


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


Problems with RDFS• RDFS is 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

• It can be difficult to provide reasoning support– No “native” reasoners for non-standard semantics– May be possible to reason via FO axiomatisation


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


Joint EU/US Committee

DAML

OntoKnowledge+Others

The OWL Family Tree

Frames

Description Logics

RDF/RDF(S)

OIL

DAML-ONT

DAML+OIL OWLW3C


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.


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


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.


Reasoning Tasks

• 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?

Sean Bechhofer:

Concrete Examples: Grid/VO?

GONG?

Sean Bechhofer:

Concrete Examples: Grid/VO?

GONG?


Classification


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.


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


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


Potential Pitfalls


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


Semantic Web vs Semantic Web• Semantics/AI/KR community with little attention paid to Web

aspects– “You’re not doing it properly”

• Web community with little attention paid to Semantics. – “Just stick everything in a big RDF store and it’ll all be fine”

• Diversity can be healthy, but can also lead to fragmentation and pointless arguments. Splitters!


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


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 don’t have to guess how to interpret things. – But can be harder to build

Thick Infrastructure

Thin Apps Thin Apps


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 can’t get at it. Is that sharing? Are you exposing the semantics?

Thin Infrastructure

Thick Apps Thick Apps


Trust and Security• Publishing my information in machine-processable forms may

allow you to:– Work out what I’m 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.


Scalability

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


Language Summary• We’ve 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

OWL

Integration

RDF(S)

RDF

XML

Annotation

Integration

Inference

the semantic web and ontologies

Documents

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