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CS690L Semantic Web and Knowledge Discovery: Concept, Technologies, Tool


CS690L Semantic Web and Knowledge Discovery: Concept, Technologies, Tool Yugi Lee STB #555 (816) 235-5932 leeyu_at_umkc.edu www.sice.umkc.edu/~leeyu This presentation ... – PowerPoint PPT presentation

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Title: CS690L Semantic Web and Knowledge Discovery: Concept, Technologies, Tool

CS690LSemantic Web and Knowledge Discovery
Concept, Technologies, Tool
  • Yugi Lee
  • STB 555
  • (816) 235-5932
  • leeyu_at_umkc.edu
  • www.sice.umkc.edu/leeyu
  • This presentation was designed based on
  • SWWS-01 symposium report and Farquhars Ontology

Semantic Web?
  • "The Semantic Web is an extension of the current
    web in which information is given well-defined
    meaning, better enabling computers and people to
    work in cooperation." -- Tim Berners-Lee, James
    Hendler, Ora Lassila, The Semantic Web,
    Scientific American, May 2001

Semantic Interoperability
  • Ihe interoperability layer to migrate from the
    syntactic to the semantic!
  • From Data space to Knowledge space
  • Integration and composition

  • Object Interoperability
  • This is the layer at which the current middleware
    products are aimed in the industry.
  • However these objects are primarily defined as
    containers for software and for streamlining the
    software development process.
  • The CORBA, EJB object models are examples of
    standards at this layer.

  • Meta-Model Interoperability
  • This is the layer at which the cross-over from
    the "data" space to the "knowledge" space takes
  • The objects here are viewed as containers of
    knowledge to be fleshed out by upper layers.
  • The OKBC and RDF(S) core models are examples of
    standards at this layer.

  • Ontology Interoperability
  • This is the layer where ontologies, schemas and
    classifications are built upon common underlying
    standardized meta-models.
  • The ability to use different ontologies to
    specify and query information constitutes
    interoperability at this layer.
  • Ontology Standardization

  • Meta-Data (View/Query) Interoperability
  • Semantic metadata descriptions can be constructed
    from one or more underlying ontologies.
  • Issues at this layer would be to decompose
    information requests into those supported by the
    individual semantic metadata descriptions
    corresponding to the information sources.
  • Ontology Query Language

  • Process/Services Interoperability
  • Semantic process/service descriptions can be
    constructed from resources on the Web and one or
    more underlying ontologies.
  • Issues at this layer would be to enable a better
    discovery, selection, composition, monitoring,
    and interoperability of services/process.
  • A resource description, informally called its
    semantics, includes that information about the
    resource that can be used by computers - not just
    for display purposes, but for using it for
    automatic processing in various applications.
  • Service Ontology, Semantic web service workflow,
    Web Service Discovery, addressing semantic
    heterogeneity handling, QoS specification for Web
    Services and Processes.

Practical Motivation Semantic Web/Application
  • The Semantic Web is more than simply some sort of
    academic foolishness or rewarmed AI vision.
  • The applications showed real technology and tools
    are being built in the Semantic web community,
    and that there is a lot of interest in these
    technologies on the part of industry and
  • The web services community showed one area where
    there is tremendous industrial interest and where
    semantic web technology could be an important
    part of the work.

Challenging Example Query Answering
  • How many acres of cotton are planted in China?
  • Response from todays Web Some documents --
    some of which may contain the answer -- somewhere
  • Response from the Semantic Web of the future
    15,485,000 in 2000, says the USDA
  • Deductive query answering rather than document
  • Ontologies will be a primary source of knowledge
    for reasoning enable derivation of answers not
    explicitly on a Web site
  • Even simple Web sites may reference large and
    distributed ontologies a challenge to
    query-answering reasoners
  • Ontologies could include special purpose
    query-answering reasoners
  • For proving instances of atomic formulas in the
    ontologys vocabulary and making inferences from
    sentences in the ontologys vocabulary
  • Requires an API for special purpose reasoners

Challenging Example Semantic Search
  • Taps Semantic Search (Stanford University)
  • Retrieves real-time data relevant to a quer
    Determines the semantic type of individuals in
    the query, Uses models of relevancy based on
  • Uses a background ontology and large KB of
  • 3,000 class and 72,000 individuals,
    Downloadable in RDF-S or DAMLOIL
  • Can be augmented with use-specific and
    user-specific ontologies and used to retrieve
    data relevant to a task being performed

Ontology - What Is an Ontology? A. Farquhar
  • To communicate, plan, think we need
    aconceptualization of the world
  • What kinds of things are there? What are their
    properties? What are their relationships?
  • These things define our ontology
  • We all have ontologies (e.g., of organizations,
    computers, animals)
  • Some are very idiosyncratic. Some are shared!
  • Communication and interaction require common
    shared ontologies.

Ontology - Problems in Communication
  • People, organizations, software programs must
  • Different needs and backgrounds imply different
    viewpoints, assumptions, jargon
  • This divergence is natural and valuable
  • But leads to problems in communication,
    interaction, and understanding
  • Explicit ontologies are crucial for
  • Communication
  • Education
  • Interoperation
  • Integration
  • Adaptive agents

Ontology - Example
  • Researchers in molecular biology need to
  • share results and check consistency between their
    models, data, and reported models and data
  • The Riboweb project (Stanford, SMI)
  • Building an ontology for ribosomes, models, data,
  • Molecular structure, experimental data, tests,
  • Encoding (by hand) relevant literature

Ontology - Example
  • Doctors, clinics, hospitals, insurance companies,
    government agencies need to share information
  • Clinical guidelines, drug interactions, covered
    procedures, best practices
  • Several efforts are addressing aspects of this
  • UMLS (unified medical language system)
  • SNOMED (standard nomenclature for medicine)

Ontology - Example
  • There are many workflow management systems
  • In order to share information across them and
    support interoperation, we need to define an
    integrated ontology that covers
  • Processes, resources, products, services,
  • Several groups are involved in such an effort

Ontology - Example
  • Collaborative engineering projects need to
    communicate across discipline boundaries
  • Several projects (e.G., PACT, Boeing) have worked
    to build ontologies for the subdisciplines and
    span them
  • Goals include
  • Automated notifications on design modifications
  • Cross-disciplinary simulation
  • Improved design process

Ontology - Benefits
  • Explicit ontologies support
  • Shared understanding among people
  • Interoperability between tools
  • Systems engineering
  • Reusability
  • Declarative specification

Semantic Web Language
  • XML
  • Language for describing the structure of document
    content e.g., declare data to be a retail price,
    a sales tax, a book title, ...
  • Uniform method for describing and exchanging data
    using HTTP
  • Provides a syntactic schema
  • ltPublication URL "ftp//db.stanford xml.psgt
  • ltTitlegt From Semistructured Data ... Language
  • ltAuthorgt R. Goldman lt/Authorgt
  • ltPublishedgt Proceedings of ... Databases
  • ltLocationgt Location of
  • ltCitygt Philadelphia lt/Citygt
  • ltStategt Pennsylvania lt/Stategt
  • lt/Locationgt
  • ltDategt
  • ltMonthgt June lt/Monthgt
  • ltYeargt 1999 lt/Yeargt
  • lt/Dategt
  • lt/Publicationgt

When in June?
Semantic Web Language
  • XML Is Not Enough
  • Ontologies enable independently developed
    programs to exchange data XML provides
    syntactic schema
  • Ontologies specify intended meaning in a computer
    interpretable form XML provides no means of
    specifying intended meaning of tags
  • XML is like HTML, where you make up your own
  • But in XML, you cant say what your tags mean.

W3C Semantic Web Activity
  • Semantic Web Activity (http//www.w3.org/2001/sw/)
  • Established to serve a leadership role, in both
    the design of enabling specifications and the
    open, collaborative development of technologies
    that support the automation, integration and
    reuse of data across various applications.
  • Successor to the W3C Metadata Activity
  • RDF Core Working Group (http//www.w3.org/2001/sw/
  • Responsible for the Resource Description
    Framework (RDF)
  • Web Ontology Working Group (http//www.w3.org/2001
  • Charter Build upon the RDF Core work a language
    for defining structured web based ontologies
    which will provide richer integration and
    interoperability of data among descriptive
  • Developing Ontology Web Language (OWL)
  • Based on DAMLOIL, developed in DARPAs Agent
    Markup Language program

Open Issues
  • What can we do as individuals and as part of the
    semantic web community?
  • Everyone was frustrated by the "waiting around"
    for Semantic Web infrastructure to appear,
  • that creating "some" infrastructure was more
    important that resolving the remaining
    "expressivity vs. tractability" dilemmas (for
  • there is always risk solving the easy parts of
    problems first, because that can make it harder
    to solve the harder parts later. Nevertheless,
    the consensus was "forge ahead!"

Open Issues
  • Do we need to standardize on foundational models
  • agree on minimalist semantics (expressivity) and
    a syntax in which to represent units of meaning,
  • leaving for distributed, incremental, and local
    development the problem of creating actual
  • that would be expressed, represented and
    communicated using the foundational model.

Open Issues
  • Is the current Semantic Web standards development
    process adequate?
  • This addresses the dilemma posed by a general
    acknowledgement that the Semantic Web poses new
  • The current standards process may be the best
    that we know how to create, and it still may be
    inadequate - because, for instance, it deals with
    distributed semantics.
  • At worst, it needs field-testing and feedback
    from actual use.

Open Issues
  • Do we need Semantic Web glossaries? ("pumpkins?")
  • Even if there was not consensus on the
    definitions, all agreed that Semantic Web
    glossaries would be a big help
  • they would be something to disagree with, and
    catalyze alternative definitions for important
  • Do we need some ontology ontologies?
  • Everyone recognized the "ontology ontology"
    problem and that it's lack of resolution was an
    impediment to progress, and that "we are all part
    of the problem."
  • That is, it's hard to find out what ontologies
    exist, and whether they are worth using, etc.
    This is part, but not all, of the deep ontology
    re-use challenge.

Open Issues
  • How do we deal with the diversity of languages
    and tools that are starting to emerge for
    semantic content.
  • Currently XML, XML schema, RDF(S), DAMLOIL,
    WebML, and various other tools are available for
    metadata storage, querying, etc.
  • It is clear that there is a need for unifying
    frameworks, toolkits, etc.
  • Do we need well-defined semantics in the metadata
  • Many of the applications were using ontology
    languages like DAML, or extensions of RDF(S).
  • Consensus was that completing the RDFS standard,
    and moving to a web ontology standard that
    extended RDFS and XML Schema was important for
    these applications.

Open Issues
  • Do we all believe that experimentation should
  • expressivity vs. tractability
  • We have no proof that proposed Semantic Web
    standards and tools are useful or even work at
  • The chicken/egg problem
  • Without semantic markup, there's not a lot of
    motivation for the industrial base to pay
    attention to the semantic web.
  • Without industry investment/support, the W3C and
    others have trouble developing standards and
    getting sources marked up.
  • Current government funding helps to jump start
    this level, but the semantic web community needs
    to figure out how to both publicize these efforts
    and increase the dissemination of this

  • What will make the semantic web have a life of
    its own?
  • What are key ontologies that need to be created?
  • What are the killer apps for the semantic web?
  • Do you have ontologies you could contribute?
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