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Principles and Foundations of Ontologies and Semantic Grids

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Principles and Foundations of Ontologies and Semantic Grids

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  1. Principles and Foundations of Ontologies and Semantic Grids Oscar Corcho University of Manchester International Summer School on Grid Computing 2006 (ISSGC 2006) Session 32. Wednesday, July 19th 2006 http://www.cs.man.ac.uk/~ocorcho/ISSGC2006/

  2. Motivation Organisations that manage large datasets have to find agreements on what terms mean Data versus metadata: we need bindings between the data and the data structure Well-typed workflows can be annotated with semantic types Kepler can use keyword-based or ontology-based search Data, metadata an ontology (NSF report) Provenance in Taverna is stored in RDF and OWL Workflow reuse Making this change in the code would change the [implicit] semantics of this Globus service

  3. Motivation. Metadata Matters • Particularly for the following activities: • Resource discovery • Provenance • Data integration • Systems Configuration • Policy representation and reconciliation • Using: • Open, flexible and extensible self describing schemas that don’t have to be nailed down • “Let’s describe my data set, or the output format of this tool” • Lightweight schemas • Decoupled, interoperable systems, which resist to syntactic changes • Open world • “This metadata is no longer valid because...” • Data integration across different data models (e.g. RDF) • Like policy or resource models • Formalization & Reasoning support

  4. Overview • Ontologies and the Semantic Web(45 minutes) • Introduction • What is the Semantic Web • Annotation, Integration, Inference • Semantic Web Technologies • RDF, RDF Schema and OWL • Semantic Grid: History, Projects and Case Studies (15 minutes) • Semantic Grid History • Semantic Grid: Use Cases • Semantic-OGSA (S-OGSA) (30 minutes) • S-OGSA Reference Model and Capabilities • S-OGSA Mechanisms and Interaction Patterns • A Sample Deployment of S-OGSA • Credits

  5. What is the Semantic Web • An extension of the current Web… • … where 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 • How? • Promoting information exchange by tagging web content with machine processable descriptions of its meaning. • And technologies and infrastructure to do this

  6. The Semantic Web Vision The Semantic Web The Syntactic Web • 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 • Generations • 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

  7. 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? Where we are Today: the Syntactic Web Resource href href href Resource Resource Resource Resource href href href Resource href href href href Resource Resource Resource href href Resource

  8. Hard Work using the Syntactic Web… Find images of Oscar Corcho …Malcolm Atkinson … David Fergusson …

  9. 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…

  10. Information we can see… International Summer School on Grid Computing (ISSGC2006) Ischia (Naples) July 9-21, 2006 Organisers/sponsors/... ? ICEAGE, GGF, EGEE Curriculum Structured in two weeks Sessions each day Agenda for each day Session title Session speaker Session description Session slides and additional material …

  11. 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,…

  12. Solution: XML markup with “meaningful” tags? <name>WWW2002 The eleventh international world wide webcon</name> <date>7-11 may 2002</date> <location>Sheraton waikiki hotel Honolulu, hawaii, USA</location> <introduction>Register now On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event … Speakers confirmed</introduction> <speaker>Tim berners-lee <bio>Tim is the well known inventor of the Web,</bio> </speaker> <speaker>Tim berners-lee <bio>Tim is the well known inventor of the Web,</bio> </speaker> <registration>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<registration>

  13. But What About…? <conf>WWW2002 The eleventh international world wide webcon</conf> <date>7-11 may 2002</date> <place>Sheraton waikiki hotel Honolulu, hawaii, USA</place> <introduction>Register now On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event … Speakers confirmed</introduction> <speaker>Tim berners-lee <bio>Tim is the well known inventor of the Web,</bio> </speaker> <speaker>Tim berners-lee <bio>Tim is the well known inventor of the Web,</bio> </speaker> <registration>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<registration>

  14. Still the Machine only sees… <>WWW2002 The eleventh international world wide webcon<> <>7-11 may 2002</> <>Sheraton waikiki hotel Honolulu, hawaii, USA<> <>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,</> </> <>Tim berners-lee <>Tim is the well known inventor of the Web,</> </> <>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<>

  15. 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

  16. 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 concepts and their classification • Almost always including properties between concepts • Similar to an object oriented model • 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

  17. Ontology Languages OWL Inference RDFS • Reasoning over the information we haveCould be light-weight (taxonomy)Could be heavy-weight (logic-style) Integration Integration RDF Annotation Integrating information sources XML Associating metadata to resources (bindings) • Work on Semantic Web has concentrated on the definition of a collection or “stack” of languages. • Used to support the representation and use of metadata • Basic machinery that we can use to represent the extra semantic information needed for the Semantic Web RDF(S)

  18. 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.

  19. The RDF Data Model • Statements are <subject, predicate, object> triples: • <Oscar,presents,Session32> • 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/~ocorcho/index.html, a book in the library, a real person, isbn://0141184280 • Do not mistake them for Grid resources, though they could be the same, as we will see later in this talk!! • Properties themselves are also resources (URIs) presents Oscar Session32

  20. 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) “Oscar Corcho” hasName presents Oscar Session32 preparedBy hasHomePage preparedBy http://www.gs.unina.it/session-32.htm Pinar

  21. 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/ocorcho"> <o:presentsrdf:resource="some.uri/session/Session32"/> <o:hasName rdf:datatype="&xsd;string">Oscar Corcho</o:hasName> </rdf:Description> <rdf:Description rdf:about="some.uri/session/Session32"> <o:hasHomePage>http://www.gs.unina.it/session-32.htm </o:hasHomePage> <o:preparedBy rdf:resource=“some.uri/person/ocorcho"> <o:preparedBy rdf:resource=“some.uri/person/pinar_alper"> </rdf:Description>

  22. What does RDF give us? OWL Inference RDFS Integration Integration RDF Annotation XML • Single (simple) data model. • Syntactic consistency between names (URIs). • A mechanism for annotating data and resources. • Low level integration of data. RDF(S)

  23. What doesn’t RDF give us? • RDF does not give any special meaning to vocabulary • Such as subClassOf or type (supporting OO-style modelling) • So, what’s the difference between this graph... • ... and this one? “Oscar Corcho” hasName presents Oscar Session32 preparedBy “Oscar Corcho” isAlsoKnownAs talksIn Oscar Session32 presentedBy

  24. RDFS: RDF Schema • RDF Schema is another W3C Recommendation • http://www.w3.org/TR/rdf-schema/ • It 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 • The combination of RDF and RDF Schema is normally known as RDF(S)

  25. RDFS simple example xsd:date eventDate Event subClassOf subClassOf subClassOf Personal_Event Local_Event Regional_Event involves Person subClassOf subClassOf Professor Researcher <?xml version="1.0" encoding="UTF-8"?> <rdf:RDF xml:base="http://www.ontogrid.net/StickyNote#" xmlns="http://www.ontogrid.net/StickyNote#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"> <rdfs:Class rdf:ID="Event"> <rdfs:subClassOf rdf:resource="http://www.w3.org/2002/07/owl#Thing"/> </rdfs:Class> <rdfs:Class rdf:ID="Local_Event"> <rdfs:subClassOf rdf:resource="#Event"/> </rdfs:Class> <rdfs:Class rdf:ID="Regional_Event"> <rdfs:subClassOf rdf:resource="#Event"/> </rdfs:Class> <rdfs:Class rdf:ID="Personal_Event"> <rdfs:subClassOf rdf:resource="#Event"/> </rdfs:Class> <rdfs:Class rdf:ID="Person"> <rdfs:subClassOf rdf:resource="http://www.w3.org/2002/07/owl#Thing"/> </rdfs:Class> <rdfs:Class rdf:ID="Professor"> <rdfs:subClassOf rdf:resource="#Person"/> </rdfs:Class> <rdfs:Class rdf:ID="Researcher"> <rdfs:subClassOf rdf:resource="#Person"/> </rdfs:Class> <rdf:Property rdf:ID="involves"> <rdfs:domain rdf:resource="#Personal_Event"/> <rdfs:range rdf:resource="#Person"/> </rdf:Property> <rdf:Property rdf:ID="eventDate"> <rdfs:domain rdf:resource="#Event"/> <rdfs:range rdf:resource="http://www.w3.org/2001/XMLSchema#date"/> </rdf:Property> </rdf:RDF>

  26. RDF(S) Inference rdfs:Class rdf:type Person rdf:type rdfs:subClassOf rdf:type Academic rdfs:subClassOf rdf:subClassOf Lecturer

  27. RDF(S) Inference rdfs:Class rdf:type Academic rdf:type rdfs:subClassOf Lecturer rdf:type rdf:type Oscar

  28. Seamark Demo: ID new drug candidates for BRKCB-1 GO2Keyword.rdf Keywords.rdf ProbeSet.rdf Keyword GO2OMIM.rdf GO2UniProt.rdf Protein Gene Probe MIM Id OMIM.rdf IntAct.rdf GO.rdf GO2Enzyme.rdf UniProt.rdf Enzyme Organism Citation Compound Taxonomy.rdf Enzymes.rdf PubMed.xml KEGG.rdf Pathway Courtesy Joanne Luciano http://139.91.183.30:9090/RDF/VRP/Examples/schema_go.rdf http://139.91.183.30:9090/RDF/VRP/Examples/go.rdf

  29. What does RDFS give us? • Ability to use simple schema/vocabularies to describe our resources • Consistent vocabulary use and sharing • Simple inference • Query mechanisms: SPARQL, SeRQL, RDQL, … • SELECT N FROM {N} rdf:type {sti:Event} USING NAMESPACE sti=<http://www.ontogrid.net/StickyNote#> • Examples • CS AktiveSpace • Lightweight schema to integrate data from University sites • myGrid • Service descriptions for e-Science

  30. What doesn’t RDFS give us? • RDFS is too weak to describe resources in sufficient detail • No localised range and domain constraints • Can’t say that the range of hasEducationalMaterial is Slides when applied to TheoreticalSession and Code when applied to HandsonSession • TheoreticalSession hasEducationalMaterial Slides • HandsonSession hasEducationalMaterial Code • No existence/cardinality constraints • Can’t say: • Sessionsmust have some EducationalMaterial • Sessionshave at least one Presenter • No transitive, inverse or symmetrical properties • Can’t say that presents is the inverse property of isPresentedBy

  31. The OWL Family Tree DAML RDF/RDF(S) DAML-ONT Joint EU/US Committee DAML+OIL OWL Frames OIL W3C OntoKnowledge+Others Description Logics

  32. OWL • W3C Recommendation (February 2004) • A family of Languages • OWL Full • OWL DL • OWL Lite • Formal semantics • Description Logics (DL/Lite) • Relationship with RDF

  33. OWL Basics (on top of RDF and RDFS) • Set of constructors for concept expressions • Booleans: and/or/not • A Session is a TheoreticalSession or a HandsonSession • Slides are not the same as Code • Quantification: some/all • Sessionsmust have some EducationalMaterial • Sessions can only have Presenters that have developed Grid applications or Grid middleware • Axioms for expressing constraints • Necessary and Sufficient conditions on classes • A Session that hasEducationalMaterialCode is a HandsonSession. • Disjointness • TheoreticalSessions are disjoint with HandsonSessions • Property characteristics: transitivity, inverse

  34. OWL Ontology ExampleBioPAX Biochemical Reaction OWL (schema) Instances (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

  35. OWL Ontology Example. BioPAX ontology • http://www.biopax.org/release/biopax-level2.owl

  36. Reasoning Tasks Sean Bechhofer: Concrete Examples: Grid/VO? GONG? • OWL DL based on a well understoodDescription 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 • 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?

  37. Reasoning Tasks. Classification

  38. What does OWL give us? • Ability to use complex schema/vocabularies to describe our resources. • Consistent vocabulary use and sharing. • Robust data integration techniques • Complex inference and several reasoning functions • Query mechanisms: OWL QL

  39. Overview • Ontologies and the Semantic Web (45 minutes) • Introduction • What is the Semantic Web • Annotation, Integration, Inference • Semantic Web Technologies • RDF, RDF Schema and OWL • Semantic Grid: History, Projects and Case Studies(15 minutes) • Semantic Grid History • Semantic Grid: Use Cases • Semantic-OGSA (S-OGSA) (30 minutes) • S-OGSA Reference Model and Capabilities • S-OGSA Mechanisms and Interaction Patterns • A Sample Deployment of S-OGSA • Credits

  40. The Semantic Grid “The Semantic Grid is 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 computers and people to work in cooperation” D. De Roure, et. al Semantics in and on the Grid • Web Sites • www.semanticgrid.org • Setting up the www.semanticgridcafe.org • GGF Semantic Grid Research Group (SEM-RG) • Mailing List: sem-grd@gridforum.org

  41. Semantic Grid history CombeChem SDK Demonstration Phase Efforts Systematic Investigation Phase Specific experiments Part of the Architecture Dagstuhl Schloss Seminar Grid Resource Ontology Many projects Pioneering Phase Ad-hoc experiments, early pioneers SRB GGF Semantic Grid Research Group Many workshops Implicit Semantics OGSA generation Implicit Semantics 1st generation Time

  42. Semantic Grid: Use Cases • Semantic Grid for Annotation of Data • Already seen before in the cases of BioPAX and Gene Ontology • Semantic Grid in Workflows • Service description and discovery (myGrid) • Semantic Grid in Data Integration • Data Integration (www.godatabase.org) • Data Integration (GEON) • Semantic Grid in Authorisation • We will see an example later

  43. myGrid: Workflow and Service Annotation • Large # of services, 3000+ • No real description of capabilities • A common abstraction “Processor” • Users do the selection ?

  44. myGrid: Workflow and Service Annotation Service Providers Ontologists Others Ontology Store Description extraction WSDL Interface Description Vocabulary Soap- lab Pedro Annotation tool Annotation providers Annotation/ description Taverna Workbench Registry Registry plug-in

  45. myGrid: Workflow and Service Annotation Service Providers Ontologists Others Ontology Store Description extraction WSDL Interface Description Vocabulary Soap- lab Pedro Annotation tool Annotation providers Annotation/ description Taverna Workbench Registry Registry plug-in

  46. myGrid: Workflow and Service Annotation Service Providers Ontologists Others Ontology Store Description extraction WSDL Interface Description Vocabulary Soap- lab Pedro Annotation tool Annotation providers Annotation/ description Taverna Workbench Registry Registry plug-in

  47. myGrid: Workflow and Service Annotation Service Providers Ontologists Others Ontology Store Description extraction WSDL Interface Description Vocabulary Soap- lab Pedro Annotation tool Annotation providers Annotation/ description Taverna Workbench Registry Registry plug-in

  48. myGrid: Workflow and Service Annotation • Word-based search • Semantic annotation for later discovery and (re)use • User chooses services/workflows • Unlike in Semantic Web Services approaches • A common ontology is used to annotate and query myGrid services/workflows • In the example, we are looking for all workflows/services that accept an input of semantic type nucleotide sequence

  49. Data Integration in GO www.godatabase.org ASA1 tryptophan biosynthesis tryptophan biosynthesis Gene Symbol Locus Name Function Function F15D2.31 Courtesy Chris Wroe

  50. Data Integration in GEON Virginia Tech & GEON CYBERINFRASTRUCTURE FOR THE GEOSCIENCES A.K.Sinha, Virginia Tech, 2005