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  • 1.
    • Semantic Web
      • &
      • Cased Based Reasoning
      • AIST Meeting JPL, CA 2003
    Mehmet S. Aktas [email_address]
  • 2. Outline
    • Semantic Web Overview
      • Semantic Web
      • Motivations
      • Ontology Languages
      • Semantic Web and Cased Based Reasoning
    • Cased Based Reasoning Overview
      • Cased Based Reasoning
      • CBR Process
      • Conversational Cased Based Reasoning
    AIST Meeting JPL, CA 2003
  • 3.
    • “ The Semantic Web is a major research initiative of the World Wide
    • Web Consortium (W3C) to create a metadata-rich Web of resources
    • that can describe themselves not only by how they should be
    • displayed (HTML) or syntactically (XML), but also by the meaning of the
    • metadata.”
    • From W3C Semantic Web Activity Page
    • “ 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
    AIST Meeting JPL, CA 2003 Semantic Web Overview
  • 4.
    • Difficulties to find, present, access, or maintain
    • available electronic information on the web
    • Need for a data representation to enable software
    • products (agents) to provide intelligent access to
    • heterogeneous and distributed information.
    AIST Meeting JPL, CA 2003 Motivations
  • 5. AIST Meeting JPL, CA 2003 The Semantic Stack and Ontology Languages From “The Semantic Web” technical report by Pierce The Semantic Language Layer for the Web A B A = Ontology languages based on XML syntax B = Ontology languages built on top of RDF and RDF Schema
  • 6.
    • Resource Description Framework (RDF) is a framework for
    • describing and interchanging metadata (data describing the web
    • resources ).
    • RDF provides machine understandable semantics for metadata.
    • This leads,
      • better precision in resource discovery than full text search,
      • assisting applications as schemas evolve,
      • interoperability of metadata.
    AIST Meeting JPL, CA 2003 Resource Description Framework (RDF) - I
  • 7.
    • RDF has following important concepts
      • Resource : The resources being described by RDF are anything that can be named via a URI.
      • Property : A property is also a resource that has a name, for instance Author or Title.
      • Statement : A statement consists of the combination of a Resource, a Property, and an associated value.
    AIST Meeting JPL, CA 2003 Resource Description Framework (RDF)- II Example: Alice is the creator of the resource .
  • 8.
    • RDF is dependent on metadata conventions for definitions.
    • The Dublin Core is an example definition standard which defines a simple metadata elements for describing Web authoring.
    • It is named after 1995 Dublin (Ohio) Metadata Workshop.
    • Following list is the partial tag element list for Dublin Core standard.
      • Creator: the primary author of the content
      • Date: date of creation or other important life cycle events
      • Title: the name of the resource
      • Subject: the resource topic
      • Description: an account of the content
      • Type: the genre of the content
      • Language: the human language of the content.
    AIST Meeting JPL, CA 2003 The Dublin Core Definition Standard
  • 9. AIST Meeting JPL, CA 2003 Example creator = Alice is the creator of the resource .
    • Property “creator” refers to a specific definition. (in this example by Dublin Core
    • Definition Standard). So, there is a structured URI for this property. This URI makes this
    • property unique and globally known.
    • By providing structured URI, we also specified the property value Alice as following.
    • “”
    Alice Resource Property Property Value Inspired from “The Semantic Web” technical report by Pierce
  • 10. AIST Meeting JPL, CA 2003 Example Alice is the creator of the resource . Inspired from “The Semantic Web” technical report by Pierce <rdf:RDF xmlns:rdf=” ” xmlns:dc=” ” xmlns:cgl=” http:// /people ”> <rdf:Description about=” ”> <dc:creator> <cgl:staff> Alice </cgl:staff> </dc:creator> </rdf:RDF>
    • Information in the graph can be modeled in diff. XML organizations. Human readers would
    • infer the same structure, however, general purpose applications would not.
    • Given RDF model enables any general purpose application to infer the same structure.
    Why bother to use RDF instead of XML?
  • 11.
    • RDF Schema is an extension of Resource Description Framework.
    • RDF Schema provides a higher level of abstraction than RDF .
      • specific classes of resources ,
      • specific properties,
      • and the relationships between these properties and other resources can be described.
    • RDFS allows specific resources to be described as instances of more general classes .
    • RDFS provides mechanisms where custom RDF vocabulary can be developed.
    • Also, RDFS provides important semantic capabilities that are used by enhanced semantic languages like DAML, OIL and OWL.
    AIST Meeting JPL, CA 2003 RDF Schema ( RDFS ) It resembles objected-oriented programming
  • 12.
    • No standard for expressing primitive data types such as integer, etc. All data types in RDF/RDFS are treated as strings.
    • No standard for expressing relations of properties (unique, transitive, inverse etc.)
    • No standard for expressing whether enumerations are closed.
    • No standard to express equivalence, disjointedness etc. among properties
    Limitations of RDF/RDFS AIST Meeting JPL, CA 2003
  • 13.
    • RDFRDFS define a framework, however they have limitations. There is a need for new semantic web languages with following requirements
        • They should be compatible with (XML, RDF/RDFS)
        • They should have enough expressive power to fill in the gaps in RDFS
        • They should provide automated reasoning support
    • Ontology Inference Layer (OIL) and DARPA Agent Markup Language (DAML) are two important efforts developed to fulfill these requirements.
    • Their combined efforts formed DAML+OIL declarative semantic language.
    AIST Meeting JPL, CA 2003 DAML, OIL and DAML+OIL - I
  • 14.
    • DAML+OIL is built on top of RDFS.
        • It uses RDFS syntax.
        • It has richer ways to express primitive data types.
    • DAML+OIL allows other relationships (inverse and transitivity) to be directly expressed.
    • DAML+OIL provides well defined semantics, This provides followings:
        • Meaning of DAML+OIL statements can be formally specified.
        • Machine understanding and automated reasoning can be supported.
        • More expressive power can be provided.
    AIST Meeting JPL, CA 2003 DAML, OIL and DAML + OIL - II
  • 15.
    • Example: T. Rex is not herbivore and not a currently living species.
    • This statement can be expressed in DAML+OIL, but not in RDF/RDFS since RDF/RDFS cannot express disjointedness.
    • DAML+OIL provides automated reasoning by providing such expressive power.
      • For instance, a software agent can find out the “list of all the carnivores that won’t be any threat today” by processing the DAML+OIL data representation of the example above.
      • RDF/RDFS does not express “is not” relationships and exclusions.
    AIST Meeting JPL, CA 2003 Example How is DAML+OIL is different than RDF/RDFS? From “The Semantic Web” technical report by Pierce
  • 16.
    • Web Ontology Language (OWL) is another effort developed by the OWL working group of the W3Consorsium.
    • OWL is an extension of DAML+OIL.
    • OWL is divided following sub languages.
        • OWL Lite
        • OWL (Description Logics) DL
        • OWL Full – limited cardinality
    • OWL Lite provides many of the facilities of DAML+OIL provides. In addition to RDF/RDFS tags, it also allows us to express equivalence, identity, difference, inverse, and transivity.
    • OWL Lite is a subset of OWL DL, which in turn is a subset of OWL Full.
    AIST Meeting JPL, CA 2003 Web Ontology Language (OWL)
  • 17.
    • Developing new tools, applications and architectures on top of the Semantic Web is the real challenge.
    • AI techniques should be used to utilize the Semantic Web up to its potentials.
    • CBR is an AI technique based on reasoning on stored cases.
    • CBR technique can be applied to do intelligent retrieval on metadata of codes related Earthquake Science.
    From Semantic Web to Cased Based Reasoning AIST Meeting JPL, CA 2003
  • 18.
    • CBR is reasoning by remembering: It is a starting point for new reasoning
    • Problem-solving: CBR solves new problems by retrieving and adapting records from similar prior problems.
    • Interpretive/classification: CBR understands new situations by comparing and contrasting them to similar situations in the past
    • Case-based reasoning is a methodology of reasoning from specific experiences, which may be applied using various technologies (Watson 98)
    AIST Meeting JPL, CA 2003 What is CBR? Overview of Case-Based Reasoning
  • 19.
    • Everyday Examples of CBR
    • Remembering today’s route from the place you live to campus and taking the same route.
    • Diagnosing a computer problem based on a similar prior problem.
    • Predicting an opponent’s actions based on how they acted under similar past circumstances
    • Assessing a hiring candidate by comparing and contrasting to existing employees
    What is CBR? AIST Meeting JPL, CA 2003
  • 20. CBR Process
    • What is a Case?
      • Input cases are descriptions of a specific problem.
      • Stored cases encapsulate previous specific problem situations with solutions.
      • Another way to look at it:
        • Stored cases contain a lesson and a specific context where the lesson applied.
        • The context is used to determine when the lesson may apply again.
    AIST Meeting JPL, CA 2003
  • 21. CBR Process
    • When and how are cases used?
    • Given a Problem Description (P.D.) to be solved,
    • CBR follows a cyclical process.
      • REtrieve the most similar case(s)
      • REuse the case(s) to attempt to solve the problem
      • REvise the proposed solution if necessary
      • REtain the new solution as a part of new case.
    AIST Meeting JPL, CA 2003
  • 22. CBR Process Problem Retrieve Reuse Revise Retain Proposed solution Confirmed solution Case-Base The CBR Cycle AIST Meeting JPL, CA 2003
  • 23. Conversational CBR (CCBR)
    • CCBR is a method of CBR where user interacts with the system to retrieve the right cases.
    • System responds with ranked cases and questions at each step
    • Question-answer-ranking cycle continues until success or failure
    AIST Meeting JPL, CA 2003
  • 24. Conversational CBR
    • CCBR facilities
      • Question management facility
      • Case management facility
      • GUI for user-system interaction
      • Facilities to display questions or cases
    AIST Meeting JPL, CA 2003
  • 25. A Prototype CCBR Application AIST Meeting JPL, CA 2003
  • 26. A Prototype CCBR Application
    • Purpose
      • Intelligent retrieval on metadata describing codes written for earthquake science.
      • Guidance on how to run the codes to get reasonable results.
      • Guidance for inexpert users to browse and select codes
    • Casebase
      • disloc - produces surface displacements based on multiple arbitrary dipping dislocations in an elastic half-space
      • simplex - inverts surface geodetic displacements to produce fault parameters
      • VC - simulates interactions between vertical strike slip faults.
    AIST Meeting JPL, CA 2003
  • 27. A Prototype CCBR Application
    • Classification
      • Initial effort – dummy cases created to classify the different codes
      • A general approach is needed
    AIST Meeting JPL, CA 2003
  • 28. A Prototype CCBR Application AIST Meeting JPL, CA 2003 CCBR CASE Problem Solution Feature Feature Feature = <Question, Answer>
  • 29. A Prototype CCBR Application
    • How does Case Ranking take place in CCBR?
    • Retrieved cases are sorted based on their consistency with the query case.
    • As the questions are answered more cases are eliminated.
    • A case is ruled out only if there is a conflict between the case and the query case
    • Consistency number for a case remains same if the case has no answer for the question.
    • Consistency number for a case gets incremented if the case has the same answer to the question as the query case.
    AIST Meeting JPL, CA 2003
  • 30. A Prototype CCBR Application AIST Meeting JPL, CA 2003 CCBR CASEBASE Case Feature 1 Feature 2 Feature 5 Case = <Problem, Solution> Feature 1 Feature 2 Feature 3 Feature 4 A Case from CASEBASE Query Case IF (( A .Feature1.Solution = B .Feature1.Solution) & ( A .Feature2.Solution = B .Feature2.Solution)) THEN Consistency # = 2 A B
  • 31. A Prototype CCBR Application
    • How does question ranking take place in CCBR?
    • Questions can be ranked based on their frequency factor
    • Questions can be ranked based on predefined inference rules
    • Only distinguishing questions are to be ranked
    • Questions can be YES/NO questions, multiple choice questions or questions with numerical answers.
    AIST Meeting JPL, CA 2003
  • 32.
    • W3C Semantic Web Activity Page. Available from .
    • T. Berners-Lee, J. Hendler, and O. Lassila, “The Semantic Web.” Scientific American, May 2001.
    • Resource Description Framework (RDF)/W3C Semantic Web Activity Web Site: .
    • D. Brickley and R. V. Guha (eds), “RDF Vocabulary Description Language 1.0: RDF Schema.” W3C Working Draft 23 January 2003.
    • The DARPA Agent Markup Language Web Site: http:// .
    • OIL Project Web Site:
    References AIST Meeting JPL, CA 2003
  • 33. References
    • CBR on the web
    • Case-Based Reasoning Resources
    • AI Topics - CBR
    • http://
    • A mailing list including announcements, questions, and discussion about CBR, managed by Ian Watson [email_address]
    • Riesbeck & Schank, Inside Case-Based Reasoning, Erlbaum, 1989.
    • Kolodner, Case-Based Reasoning, Morgan Kaufmann, 1993.
    AIST Meeting JPL, CA 2003