What would you use an ontology for?

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What would you use an ontology for?
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Web Service model framework

• Semantic web services
• Semantic Grid services
• Domain for escience
• Services themselves
• Link to UDDI, WSDL and other technologies
• Processes and ontologies (controlled vocab. For
  processes).

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What are they used for taster

• A community reference.
• Support for community practice.
• Specification of database or database content.
• Controlled vocabulary for annotation.
• Application database interoperability.
• Consensual vocabulary.
• Searching, query formation indexing.
• Classification, interface/portal driving.
• Agent or service metadata management.
• Grid services description, advert, discover.

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What are they used for taster

• Support for knowledge intensive applications.
• Text extraction, decision support, resource
  planning, intelligent interfaces.
• Knowledge repository structure.
• Knowledge acquisition.

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GO used for

• Controlled annotation quality consistency
• Mediation navigation through content
• Classification / query / index

InterPro
SWISS-PROT
BLAST
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Ontology as content map

• Relies on the Gene Ontologys taxonomy.
• Lack of semantics and rigour begins to be
  problematic.

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Four Roles of Terminology/Ontologies

• Content of Databases and Patient Records
• Structural linkage within EPR/EHR messages
• Content of EPR/EHR messages
• Capturing information - the user interface
• Linkage between domains
• Health and Bio Sciences
• Macro, Micro, and Molecular scales
• Contexts Normal / abnormal species stage of
  development
• Healthcare delivery and Clinical research
• Patient Records and Decision Support
• Indexing Information
• Metadata and the semantic web
• www.semanticweb.org www.w3c.org

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• No one universal semantics gt semantic
  interoperability
• Ontology reconciliation
• Ontology merging
• Server based Web -gt peer to peer interactions
• Peer discovery
• Languages for p2pconversations
• Mediated communications
• Coordinating protocosl
• Tightly coupl,ed components -gt loose coupled
• Static workflows -gt dynamic distributed
  composition
• Flexible distributed control
• Partial failures
• Execution monitoring
• Efficient computations
• Intermitent comms
• Estab partnerships -gt on the fly discovery
• Partner discovery
• Estab trust -gt trust on the fly

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• Eyeball web-gt sw -gtssw -gt agents web
• Goal directed.

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Semantic interoperation
process
process
Semantic interoperation
views/queries
views/queries
ontologies
ontologies
metamodels
metamodels
objects
objects
Data exchange
transport
transport
packet
packet
data link
data link
physical
physical
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Web Service

• Descriptions
• gt Automated
• Discovery Search
• Selection
• Matching
• Composition Interoperation
• Invocation
• Execution monitoring

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Machine processable Knowledge on the Web

• Annotating services requires a shared vocabulary
• Ontologies
• a vocabulary of terms,
• a precise and principled specification of their
  meaning
• structure on the domain of the terms
• constrain the possible interpretations of terms
• Inference applies the knowledge in the metadata
  and the ontology to create new metadata and new
  knowledge

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The Web Services Stack
WFDL
Workflow
Ontology
UDDI
Adverts Description and discovery
WSDL
Service connection
SOAP
Message protocol
XML
Message syntax
HTTP
Transport
TCP/IP
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What do Ontologies offer?

• Knowledge discovery
• Knowledge-acquisition tools
• Decision Support
• Hypothesis generation ? RiboWeb, Ingenuity

Control Semantics Inference
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• The technical advantages of knowledge modeling
  are obvious. Knowledge bases can be automatically
  checked for consistency they support inference
  mechanisms which derive data which have not been
  explicitly stored they also offer extensive
  request and navigation facilities. However, the
  most immediate benefit of knowledge base design
  lies in the modeling process itself, through the
  effort of explication, organization and
  structuration sic of the knowledge it
  requires.
• Editorial, Bioinformatics, July 2000

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Scale gt Reasoning Inference

• Keeping the classification together
• Expressing constraints and sticking to em
• Ontology design
• Creation, extension, maintenance
• Large, multiply authored evolving ontologies
• Ontology integration
• Merging
• Ontology deployment
• Determining consistency of description
  instances
• Query validation/refinement/containment Service
  matching

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Ontologies and the Grid

• Domain Ontologies
• For eScience applications
• Service and Task Ontologies
• For Web/OGSA services
• Semantic Web Services.
• Service capabilities

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The Semantic Web
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Knowledge Technologies for the Grid

• Ability to store and retrieve huge volumes of
  data
• Ability to effectively process large volumes of
  data
• Ability to capture, enrich, classify and
  structure knowledge about
• Domains
• Organisations
• Individuals
• Research Collaborations
• Experiments
• Results

• Services

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Semantics A Many- Splendored Thing (from mike)

• Semantics means the study of meaning.
• What has semantics? Where are they?
• What do they look like? How are they used?
• Kinds of Semantics
• Real- world Semantics
• Axiomatic Semantics
• Model- theoretic Semantics
• Denotational, Procedural, Operational Semantics

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Semantic Networking
It is crucial for the interoperability layer to
migrate fromthe syntactic to the semantic!
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The Semantic Web / Grid FabricA Collection of
Metadata Descriptions and Ontologies
Ontology
Server
MetadataRepository
MetadataRepository
Distributed Computing Infrastructure (J2EE, .NET,
CORBA, Agents)
...
...
DATA REPOSITORIES
DATA REPOSITORIES
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Components of the Semantic Web Fabric

• Bootstrapping, Creation and Maintenance of
  Semantic Knowledge
• Collaborative and Sociological Processes,
  Statistical Techniques
• Ontology Building, Maintenance and Versioning
  Tools
• Re-use of Existing Semantic Knowledge
  (Ontologies)
• Annotation/Association/Extraction of Knowledge
  with/from Underlying Data
• Information Retrieval and Analysis (Distributed
  Querying/Search/Inference Middleware)
• Semantic Discovery and Composition of Services
• Distributed Computing/Communication
  Infrastructures
• Component based technologies, Agent based
  systems, Web Services
• Repositories for managing data and semantic
  knowledge
• Relational Databases, Content Management Systems,
  Knowledge Base Systems

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What are the missing gaps ?

• Ontology Integration/Interoperation
• Need to address semantics of relationships such
  as synonyms, hyponyms, etc.
• Ontology Impedance/Mismatch
• Relax the requirements of consistency and
  completeness
• Should be able to characterize the information
  error/loss that occurs..
• Dynamic Ontologies
• Need to relax the assumption of the staticness
• Inferences based on Semantics of the Data
• Performance/Scalability
• re-use of pre-existing data models/schemas/ontolog
  ies that describes the content of information
  sources

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Bibliography Data Ontology The Blue
Ontology
Conference
Agent
Person
Organization
Author
Publisher
University
Thesis
Periodical-Publication
http//www-ksl.stanford.edu/knowledge-sharing/onto
logies/html/bibliographic-data/
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A subset of WordNet 1.5 The Red
Ontology
Instructions
Reference-Manual
http//www.cogsci.princeton.edu/wn/w3wn.html
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Ontology architecture

• KAON?
• myGrid?

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