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					Semantic Grid 101

Carole Goble and Sean Bechhofer
  University of Manchester, UK

        David De Roure
 University of Southampton, UK
To realise the Next Generation Grid requires semantically rich information
epresentation, the exploitation of knowledge, and
o-ordination and orchestration that is aware of context and task‖




                                               David Snelling,
                                               NextGRID, Fujitsu, GGF
                                               Ontologist                    2
    Don’t we have Semantics in the Grid
                 already?
•    Its called metadata.               •   And code libraries.
•    Or vocabularies.                   •   And type systems.
•    Or glossaries.                     •   And schemas.
•    It’s the state properties of a     •   And applications.
     resource.
                                        •   And data formats.
•    Its in information services.
                                        •   And best practice.
•    And registries and catalogues.
                                        •   And documentation.
•    And configuration files.
                                        •   And workflows.
•    And policy definitions.
                                        •   And notification events
•    And service level agreements.
                                        •   And monitoring logs
•    And file names.
                                        •   And embedded in XML tags …
•    And file headers.
                                        •   And even ontologies!
•    And directory naming conventions

                                        •   And protocols.
                                        •   And decision procedures.
                                                                         3
  Embedding and implicit meaning is the
enemy of shareability and reuse in an open
    and decoupled and collaborative
              environment.

    Machine processable descriptions
                  are
     machine actionable descriptions
An extension of the
current Grid in which
information and services
are given well-defined and
explicitly represented
meaning, so that it can be
shared and used by
humans and machines,
better enabling them to
work in cooperation

                             5
An extension of the
current Web in which
information and services
are given well-defined and
explicitly represented
meaning, so that it can be
shared and used by
humans and machines,
better enabling them to
work in cooperation

                             6
                How?
 Promoting information exchange
   by tagging web content with
machine processable descriptions
          of its meaning.
 And a slew of technologies and
     infrastructure to do this
•An automatically processable,   • On demand transparently
machine understandable web         constructed multi-organisational
•Distributed knowledge and         federations of distributed
information management             services
•Information integration         • Distributed computing
                                   middleware
                                 • Computational Integration

                                                    Carole Goble
                                                                   8
            Applying
Semantic Web and its Technologies
           to the Grid
        Grid Applications
    Semantic Grid Services
   Semantic Grid Resources
     A Semantic Data Grid

First – need to know what they are.
       Semantic Web 101




                Sean Bechhofer
             School of Computer Science,
             University of Manchester, UK
        sean.bechhofer@manchester.ac.uk
http://www.cs.manchester.ac.uk/people/bechhofer
              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 Tim Berners-Lee's vision of a Semantic Web




                                                                                   11
         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.

•   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…


                                                                                          12
Scientific American, May 2001:




                                                        Chuck D sez:
                            Don’t 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
                                                                       13
Where we are Today: the Syntactic
             Web                          Resource
                                  href         href                   href

                       Resource          Resource      Resource              Resource

                                  href        href      href
                       href              Resource

                                              href     href
                                   href
                       Resource          Resource      Resource


                                                href           href

                                                       Resource



                 •   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?


                                                                                        14
      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                         15
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…



                                         16
           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 Who’s Talking
Richard Granger reviews the revamping of the NHS IT programme
Look Who’s 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…

                                                                           17
  Information a machine can see…
WWW2002
The eleventh inteqnational woqld wide webcon
Sheqaton waikiki hotel
Honolulu, hawaii, USA
7-11 may 2002
1 location 5 days leaqn inteqact
Registeqed paqticipants coming fqom
austqalia, canada, chile denmaqk, fqance,
geqmany, ghana, hong kong, india, iqeland,
italy, japan, malta, new zealand, the
netheqlands, noqway, singapoqe, switzeqland,
the united kingdom, the united states,
vietnam, zaiqe
Registeq now
On the 7th May Honolulu will pqovide the
backdqop of the eleventh inteqnational woqld
wide web confeqence. This pqestigious event 
Speakeqs confiqmed
Tim beqneqs-lee
Tim is the well known inventoq of the Web,…




                                                18
Solution: XML markup with “meaningful”
                tags?
<name>WWW2002
The eleventh inteqnational woqld wide webcon</name>
<date>7-11 may 2002</date>
<location>Sheqaton waikiki hotel
Honolulu, hawaii, USA</location>
<introduction>Registeq now
On the 7th May Honolulu will pqovide the
backdqop of the eleventh inteqnational woqld
wide web confeqence. This pqestigious event 
Speakeqs confiqmed</introduction>
<speaker>Tim beqneqs-lee
 <bio>Tim is the well known inventoq of the Web,</bio>
</speaker>
<speaker>Tim beqneqs-lee
 <bio>Tim is the well known inventoq of the Web,</bio>
</speaker>
<registration>Registeqed paqticipants coming fqom
austqalia, canada, chile denmaqk, fqance,
geqmany, ghana, hong kong, india, iqeland,
italy, japan, malta, new zealand, the
netheqlands, noqway, singapoqe, switzeqland, the
united kingdom, the united states, vietnam,
zaiqe<registration>

                                                         19
             But What About…?

<conf>WWW2002
The eleventh inteqnational woqld wide webcon<conf>
<date>7-11 may 2002</date>
<place>Sheqaton waikiki hotel
Honolulu, hawaii, USA<place>
<introduction>Registeq now
On the 7th May Honolulu will pqovide the
backdqop of the eleventh inteqnational woqld
wide web confeqence. This pqestigious event 
Speakeqs confiqmed</introduction>
<speaker>Tim beqneqs-lee
 <bio>Tim is the well known inventoq of the Web,</bio>
</speaker>
<speaker>Tim beqneqs-lee
 <bio>Tim is the well known inventoq of the Web,</bio>
</speaker>
<registration>Registeqed paqticipants coming fqom
austqalia, canada, chile denmaqk, fqance,
geqmany, ghana, hong kong, india, iqeland,
italy, japan, malta, new zealand, the
netheqlands, noqway, singapoqe, switzeqland, the
united kingdom, the united states, vietnam,
zaiqe<registration>

                                                         20
     Still the Machine only sees…
<conf>WWW2002
The eleventh inteqnational woqld wide webcon<conf>
<date>7-11 may 2002</date>
<place>Sheqaton waikiki hotel
Honolulu, hawaii, USA<place>
<intqoduction>Registeq now
On the 7th May Honolulu will pqovide the
backdqop of the eleventh inteqnational woqld
wide web confeqence. This pqestigious event 
Speakeqs confiqmed</intqoduction>
<speakeq>Tim beqneqs-lee
 <bio>Tim is the well known inventoq of the
Web,</bio>
</speakeq>
<speakeq>Tim beqneqs-lee
 <bio>Tim is the well known inventoq of the
Web,</bio>
</speakeq>
<qegistqation>Registeqed paqticipants coming fqom
austqalia, canada, chile denmaqk, fqance,
geqmany, ghana, hong kong, india, iqeland,
italy, japan, malta, new zealand, the
netheqlands, noqway, singapoqe, switzeqland, the
united kingdom, the united states, vietnam,
zaiqe<qegistqation>                                  21
          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
                  Machine Processable
      • Inflexible
                                not
      • Limited number of things can be expressed
• Use Ontologies to specify meaning of annotations
             Machine Understandable
   – 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


                                                                             22
       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




                                                                               23
          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




                                               24
                       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




                                                                             Inference
   –   XML                         OWL
   –   RDF




                                                               Integration
                                                               Integration
   –   RDF(S)                     RDF(S)
   –   OWL
   –   …


                                                  Annotation
                                   RDF

                                   XML

                                                                                         25
               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).
                     Every gardener likes the sun                  sister(X,S) :- female(S), parent(S,P), parent(X,P), X \== S.
                       8x.gardener(x) ) likes(x, Sun)
                     You can fool some of the people all of the time
                                                                   male(james1).
                       9x.8t.(person(x) Æ time(t)) ) can-fool(x,t) male(charles1).
                                                                   male(charles2).
                     You can fool all of the people some of the time
                       8x.9t.(person(x) Æ time(t)) ) can-fool(x,t) male(james2).
                     All purple mushrooms are poisonous            male(george1).
                                                                   female(catherine).
                       8x.(mushroom(x) Æ purple(x)) ) poisonous(x) female(elizabeth).
                     No purple mushroom is poisonous               female(sophia).
                       :9x.(mushroom(x) Æ purple(x) Æ poisonous(x))parent(charles1, james1).
                       8x.(mushroom(x) Æ purple(x)) ) : poisonous(x)
                                                                   parent(elizabeth, james1).
                     There are exactly two purple mushrooms parent(charles2, charles1).
                                                                   parent(catherine, charles1).
                       9x.9y.mushroom(x) Æ purple(x) Æ mushroom(y) Æ purple(y) Æ
                     (:x=y)                                        parent(james2, charles1).
                                                                   parent(sophia, elizabeth).
                                                                    (y=z)))
                          Æ (8x.mushroom(z) Æ purple(z) ) ((x=z) _parent(george1, sophia).
                     Clinton is not tall
                       : tall(Clinton)
                                                                                                                                  26
               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



                                                         27
          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


                                          ?
                                                                           28
                               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.




                                                                 29
               The RDF Data Model
• Statements are <subject, predicate, object> triples:
    –   <Sean,hasColleague,Ian>
• Can be represented as a graph:
                                               hasColleague
                                     Sean                      Ian

• 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)


                                                                           30
                 Linking Statements
• The subject of one statement can be the object of another
• Such collections of statements form a directed, labeled graph

                      “Sean K. Bechhofer”
            hasName
                        hasColleague
     Sean                                   Ian
                                                      hasHomePage
                      hasColleague

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


• The object of a triple can also be a ―literal‖ (a string)


                                                                                31
                             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>
                                                                        32
           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.




                                                     33
                 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




                                                                             34
                  RDF(S) Inference
                                                              rdfs:Class
                                       rdf:type

                             Person
                                              rdf:type
                  rdfs:subClassOf
                                                         rdf:type
                           Academi
rdfs:subClassOf               c

                  rdf:subClassOf



                            Lecturer

                                                                           35
            RDF(S) Inference
                                                   rdfs:Class
                                 rdf:type

                     Academic

                                        rdf:type
            rdfs:subClassOf


                      Lecturer
rdfs:type


                    rdf:type



                        Sean

                                                                36
        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




                                                                  37
               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



                                                                             38
          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




                                                                          39
              The OWL Family Tree

                   DAML

RDF/RDF(S)           DAML-ONT

                                        Joint EU/US Committee

                                         DAML+OIL                     OWL
   Frames                 OIL                                   W3C



                 OntoKnowledge+Others

Description
  Logics
                                                                            40
                             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.


                                                       41
                       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




                                                       42
               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.




                                                                      43
Sean Bechhofer:
Concrete Examples: Grid/VO?


                         Reasoning Tasks
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?



                                                                            44
Classification




                 45
                   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.



                                                                         46
           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




                                                               47
                      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



                                                                                 48
Potential Pitfalls




                     49
                      Conflicting Views
• The Semantic Web community is diverse, with a rough division
  between the ―neats‖ and the ―scruffies‖.




   •   Neats                           •   Scruffies
       –   Logic and languages              –   Practice
       –   Completeness/decidability        –   Bottom up/real-world
       –   Top down, well-behaved           –   Lightweight
       –   Heavyweight                      –   Folksonomies
       –   Rich ontologies                  –   FOAF
       –   OWL                              –   RDF
                                                                       50
            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!




                                                                         51
                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



                                                                     52
    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
                 Thin Apps                 Thin Apps




                          Thick Infrastructure


                                                                         53
    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?



                Thick Apps              Thick Apps




                        Thin Infrastructure
                                                                    54
                 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.



                                                                    55
                        Scalability
•   Will this stuff work on a web scale?
•   Millions of triples/fact
•   Thousands of ontologies
•   Are you ever going to get global agreements?




                                                   56
                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




                                                                        Inference
                              OWL
   –   RDF
   –   RDF(S)




                                                          Integration
                                                          Integration
   –   OWL                   RDF(S)




                                             Annotation
                              RDF


                              XML


                                                                                    57
Thanks Sean!
                      Metadata Matters
• Flexible and extensible self describing schemas that don’t have to be
  nailed down
    – ―Lets describe my data set, or the output format of this tool‖
    – Configuration, policy, discovery
    – Lightweight schemas
• Open world
    – ―I need to comment on that experiment‖
    – ―That fact is now incorrect because …‖
    – ―I trust that security attribute assertion‖
• Data fusion across different data models
    – Like policy models
    – Or resource models
    – Cross linked by shared instances and shared concept
 Resistance to frequent syntactic changes
 Formalisation and Reasoning support
 Eliminate chronic dependence on human-intervention
                                                                          59
            A common vocabulary for data
                     pooling
                        www.godatabase.org




        Gene Symbol   Function                  Locus Name   Function
        ASA1          tryptophan biosynthesis   F15D2.31     tryptophan biosynthesis




Courtesy Chris Wroe
                                                                                  60
                                                                            Seamark Demo:
              Keywords.rdf                GO2Keyword.rdf                      ID new drug
                                                           ProbeSet.rdf
                                                                             candidates for
                                                                               BRKCB-1

                                     Keyword
     GO2UniProt.rdf                                                  GO2OMIM.rdf
                                                        Probe

                               Protein
                                                 Gene
                                                                 MIM Id
                                                                                     OMIM.rdf

IntAct.rdf
                                               GO.rdf
             UniProt.rdf                                   Enzyme         GO2Enzyme.rdf
                               Organism

                    Citation

                                                                     Compound
                                Taxonomy.rdf
         PubMed.xml                               Enzymes.rdf         KEGG.rdf
                                                                    Pathway
Courtesy Joanne Luciano
                                                                                          61
                                    Evolution from existing
                                  ontology using curation and
                                          reasoning
                  [chemical] biosynthesis (GO:0009058)
                       [i] carbohydrate biosynthesis (GO:0016051)
     View 1:
                            [i] aminoglycan biosynthesis (GO:0006023)
     Chemicals
                                        [i] heparin biosynthesis (GO:0030210)
                                  [i] glycosaminoglycan biosynthesis (GO:0006024)
     View 2:
     Process              [i] heparin metabolism (GO:0030202)
                               [i] heparin biosynthesis (GO:0030210)



C.J. Wroe, R. Stevens, C.A. Goble, M. Ashburner A Methodology to Migrate the Gene
Ontology to a Description Logic Environment Using DAML+OIL
Pacific Symposium on Biocomputing 8:624-635(2003).                                  62
                             BioPAX Biochemical Reaction
                            OWL                                                   Instances
                          (schema)                                              (Individuals)
                                                                                    (data)
Courtesy Joanne Luciano




                               phosphoglucose
                                 isomerase      5.3.1.9




K Wolstencroft, A Brass, I Horrocks, P. Lord, U Sattler, R Stevens, D Turi A little semantics goes a
long way in Biology Proc 4th ISWC 2005                                                                 63
       The semantics of knowledge
• Semantics for Grid Applications and Grid Middleware

• Semantic Grids
   – Grids and Grid middleware that makes use of semantics for its
     installation, deployment, running etc.
   – I.e. Semantics IN the Grid FOR the Grid.
• Knowledge Grids
   – A virtual knowledge base derived by using the Grid resources, in
     the same spirit as a data grid is a virtual data resource and a
     compute grid a virtual computer.
   – Knowledge Grids include services for knowledge and data mining.
   – I.e Semantics ON the Grid arising from the USE of the Grid.


                                                                        64
Discovery
              Semantic Web Services
• Automated Discovery services or
   workflows
• Knowledge assisted brokering &
   match making
• Guided instantiation and
   substitution
                                    Composition
                                    • Automated Composition
                                    • Self organising SOA
                                    • Guided workflow assembly
                                    • Composition (workflow)
                                      verification and validation


                                                                    65
                     http://www.swsi.org/


             OWL-S




                             WSMO

OWL-WS


    WSDL-S


                                            66
  Semantic Web
Services 4 Science




http://www.mygrid.org.uk
                           67
   Resource and Grid Service Semantic
              Descriptions
           Person View                            Person View??

State                    Specific      State                  Specific
                         Application                          Application
                         Ontology                             Ontology
Grid                                   Grid
Service     Functional                 Resource
Ontology    Non-Functional             Ontology

           Machine View                           Machine View



             Grid                                    Grid
            Service                                Resource



                                                                            68
Grid resource ontology
                         69
                                  VO formation
                        • Describing and asserting policy
                           – Flexible and extensible schemas,
                             transparency
                        • Can you be a member of this VO?
                           – Matching task, integration
    Static and          • How do we set your roles so you can be?
   dynamic VO              – Configuration
      model             • Are these set of policies mutually
                          consistent?
                           – Configuration and verification
                        • Service Level Agreements
                           – Matching provides and expects
Intelligent decision         clauses
making and operations   • Authentication & Authorisation
                           – Reconcile diverse policies
                                                                    70
71
                                      VO Run Time                                    VO Formation Time
CONSUMER

                                Institutions
    Annotation Service          Departments             Roles                     Ontology         HR
                                , Titles                                         Development      Dept.
                                                                                     Tool
                                      Ontology Service
  RD      Metadata Service
                                                                     Reasoning
   F                             Obtain current
John Doe is an                                                        Service                  VO Policies
insurer at Boyd,                 role definitions                                                Dept.
                                                4            5 Infer the roles
has this DN,
                                                               John Doe plays
etc..            Obtain properties
                                                       PDP




                                                                                                    Mapping Generation
                  of John Doe 3
                                    Authorization Service            Mapping
                         PIP                                    6    Role Op
 Subject                                             Lookup whether
John Doe                                             resource can be
                                            2        accessed
                                  Is John Doe
                                  authorized?       7
                                                    YES/NO
                    Resource
                  1 Request
                                         Service       PEP



                                            resource                                             Provider
PROVIDER

                                                                                                                         72
                                       73
Courtesy of Line Pouchard, Oak Ridge Labs
74
    ―It was difficult to break the boundaries between
different organizations, where they maintain their own
           ontologies and metadata systems.
These ontologies and metadata vocabularies contain
                       many conflicts.‖


―It is difficult to interpret the
policies/regulations, which we are expecting to
turn into (ontological/reasoning) rules.
Unnecessary trivia and vague definitions are
the obstacles of extracting semantics.‖


                          William Song, Durham, UK
                                                         75
Knowledge guided
     scripting
  Person-driven
   composition




                   76
                            Provenance
• Flexible and extensible
  schema
• Data fusion and
  aggregation across
  provenance metadata
• Reasoning and querying
  over descriptions
• Transparent description
                                         77
                      Annotate Anything
•   People, meetings, discussions, conference talks
•   Scientific publications, recommendations, quality comments
•   Events, notifications, logs
•   Services and resources
      – GRIMOIRES semantic UDDI registry, OMII-UK
•   Schemas and catalogue entries
      – Shout out to Jane Hunter and her Semantic SRB
•   Models, codes, builds, workflows,
•   Data files and data streams
•   Sensors and sensor data …
•   DFDL, JSDL, SAML, WSDL, WSRF, DL*, ML* as RDF?
•   If you are using a controlled vocabulary, then lets use a standard
    controlled vocabulary language.
                                                                         78
           + Haystack


haystack




                   79
                       myGrid
• Provenance example




                                80
Unicore                       GLUE




          http://www.grid-interoperability.org/
                                             81
                  Exposed Explicit Semantic
                         Descriptions
                  are Manageable Resources
• Knowledge is a Commodity.
• Semantic Infrastructure to go with the Grid Infrastructure.
• It has a representation, a state, a life cycle and a life time, an
  access policy, an owner, it changes, it can be integrate and
  aggregation.
• It gets produced and consumed.
• It gets used and stored and destroyed.



            A Semantic Data Grid
                                                                       82
                            From OGSA to the S-OGSA
                        Application 1              Application N



                                        composes
Semantic-OGSA




                        Security                     Optimization
      OGSA




                                                   extends
                                                                      Data
                                    Execution
                delegates
                                   Management

                                                     refers         Semantic
                      Resource                                      Services
                     management
                                                 Information
                                                 Management        delegates

                       Infrastructure Services




                                                                               83
          Service Oriented Knowledge Utilities
•   Next Generation Grids Expert Group
    Report 3 (NGG3) published January
    2006
•   Converged vision of Next Generation
    Grids and Service Oriented Knowledge
    Utilities
                                           •   Service Oriented –services may
                                               be instantiated and assembled
                                               dynamically
                                           •   Knowledge –knowledge-assisted
                                               to facilitate automation, and
                                               processing and delivering
    Ecosystem of Dependable,                   knowledge
    Knowledge-aware, Societal,             •   Utility –directly and immediately
    Autonomic, Stateful services               useable service with established
                                               functionality, performance and
                                               dependability

                                                                                   84
                             What is Grid ?




Courtesy of Eoghan O’Neill                    85
          SDK


                      Semantic Grid trajectory                            Demonstration
                                                                             Phase
Efforts

                                                  Systematic Investigation
                                                          Phase
                                                      Specific experiments
                                                     Part of the Architecture

              Combe                                                       Dagstuhl Schloss Seminar
              Chem
                                    Pioneering Phase                      Grid Resource Ontology
                                  Ad-hoc experiments, early
                                          pioneers                        Many projects
    SRB
                                                      GGF Semantic Grid
             Implicit Semantics                       Research Group
             OGSA generation                          Many workshops

      Implicit Semantics
      1st generation

                                                                                   Time
                                                                                               86
       Decision making
       Knowledge Discovery
Lots
       Ontology
        building
                   Workflow
                   discovery      VO mgt           Configuration
                   and design         Resource
                                     discovery &
                                                      Information
                                      brokering
       Flexible &                                        linking
Not                             Provenance
       extensible
much
       metadata              General
       schemas          language annotation

               Not
                                                   Lots
               much                Grid                             87
 1:30 pm to 3:00 pm

  Workshop Session 1 - Approaches
• S-OGSA as a Reference Architecture for OntoGrid and for
  the Semantic Grid
  Pinar Alper, Oscar Corcho, Ioannis Kotsiopoulos, Paolo
  Missier, Sean Bechhofer, Dean Kuo, Carole Goble
• The "5S" Grid in the Roadmap for Networked Organizations
  Ziga Turk, Peter Katranusckov
• A Semantic Search Engine for the Storage Resource Broker
  Stephen J. Jeffrey, Jane Hunter
• Using Triple Space Computing for Communication and
  Coordination of Services in Semantic Grid
  Omair Shafiq, Ioan Toma, Reto Krummenacher, Thomas
  Strang, Dieter Fensel

• Panel discussion

                                                             88
      3:30 pm to 5:00 pm

      Workshop Session 2 Building Bridges
•   Session 2A - Use Cases                  •   Session 2B - Combining
•   The Chemical SmartLab: Intelligent          Technologies
    Information Publication for             •   Web Service Information
    Chemists                                    Systems and Applications
    H. R. Mills, J. G. Frey, S. J. Coles,       Mehmet S. Aktas, Galip Aydin,
    David De Roure                              Geoffrey C. Fox,
•   Improving a Satellite Mission               Harshawardhan Gadgil, Marlon
    System by means of a semantic grid          E. Pierce, Ahmet Sayar
    architecture                            •   AgentWeb Gateway
    Manuel Sánchez-Gestido, María S.            integration of FIPA Multi
    Pérez-Hernández, Rafael González-           Agent System and W3C Web
    Cabero, Asunción Gómez-Perez                Service System
•   Supporting the Music Information            M. Omair Shafiq, Arshad Ali,
    Retrieval Research Community - A            Hiroki Suguri, H. Farooq Ahmad
    Use Case for the Semantic Grid
    David De Roure, J. Stephen Downie

                                                                                 89
    5:30 pm to 7:00 pm

    Workshop Session 3 Tech & Stds
•   Semantic Grid Resource Discovery using DHTs in Atlas
    Manolis Koubarakis, Zoi Kaoudi, Iris Miliaraki, Matoula Magiridou,
    Antonios Papadakis-Pesaresi
•   Putting Semantics in Grid Workflow Management: the OWL-WS
    approach
    Stefano Beco, Barbara Cantalupo, Nikolaos Matskanis, Mike Surridge
•   WS-DAIOnt: Ontology Access Provisioning in Grid Environments
    Miguel Esteban Gutiérrez, Asunción Gómez-Pérez, Oscar Muoz
    García, Boris Villazón Terrazas
•   Design and Implementation of OGSA-DAI-RDF
    Isao Kojima
•   S-MDS: A Semantic Information Service for Advanced Resource
    Discovery and Monitoring in WS-Resource Framework
    Said Mirza Pahlevi, Isao Kojima



                                                                         90
                         Thanks to
National and Kapodistrian, University of Athenas
• Manolis Koubarakis, Iris Miliaraki for organising the videoing

Colleagues and friends from:
OntoGrid
myGrid

Comb-e-Chem
Geodise
CMCS
BioPAX
GONG
UniGrids
NextGRID
                                                                   91
        Semantic Grid Community
• Web Site
   – www.semanticgrid.org
   – Setting up the www.semanticgridcafe.org


• Mailing List
   – sem-grd@gridforum.org


• GGF Semantic Grid Research Group (SEM-RG)
   – Chairs: David De Roure, Carole Goble, Geoffrey Fox
   – Secretary: Marlon Pierce

                                                          92

				
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