Semantic Web & Case Based Reasoning

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Semantic Web

&

Cased Based Reasoning

AIST Meeting JPL, CA 2003

Mehmet S. Aktas

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Outline

n

Semantic Web Overview

¨

Semantic Web

¨

Motivations

¨

Ontology Languages

¨

Semantic Web and Cased Based Reasoning

n

Cased Based Reasoning Overview

¨

Cased Based Reasoning

¨

CBR Process

¨

Conversational Cased Based Reasoning

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Semantic Web Overview

n “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

n “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,

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Motivations

n

Difficulties to find, present, access, or maintain

available electronic information on the web

n

Need for a data representation to enable software

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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

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Resource Description Framework (RDF)

-n Resource Description Framework (RDF) is a framework for

describing and interchanging metadata (data describing the web resources).

n RDF provides machine understandable semantics for metadata.

This leads,

¨ better precision in resource discovery than full text search, ¨ assisting applications as schemas evolve,

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Resource Description Framework (RDF)- I

n 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.

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The Dublin Core Definition Standard

n RDF is dependent on metadata conventions for definitions.

n The Dublin Core is an example definition standard which

defines a simple metadata elements for describing Web authoring.

n It is named after 1995 Dublin (Ohio) Metadata Workshop.

n 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

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Example

http://www.cs.indiana.edu /~Alice creator = http://purl.org/dc/elements/1.1/creator

Alice is the creator of the resource http://www.cs.indiana.edu/~Alice.

• 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. “http://www.cs.indiana.edu/People/auto/b/Alice”

Alice

Resource Property

Property Value

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Example

Alice is the creator of the resource http://www.cs.indiana.edu/~Alice.

Inspired from “The Semantic Web” technical report by Pierce

<rdf:RDF xmlns:rdf=”http://www.w3c.org/1999/02/22-rdf-syntax-ns##”

xmlns:dc=”http://purl.org/dc/elements/1.1”

xmlns:cgl=”http://cgl.indiana.edu/people”>

<rdf:Description about=” http://www.cs.indiana.edu/~Alice”> <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.

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RDF Schema (

RDFS

n RDF Schema is an extension of Resource Description Framework. n 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.

n RDFS allows specific resources to be described as instances of more

general classes.

n RDFS provides mechanisms where custom RDF vocabulary can be

developed.

n Also, RDFS provides important semantic capabilities that are used by

enhanced semantic languages like DAML, OIL and OWL.

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n No standard for expressing primitive data types such as integer, etc.

All data types in RDF/RDFS are treated as strings.

n No standard for expressing relations of properties (unique,

transitive, inverse etc.)

n No standard for expressing whether enumerations are closed.

n No standard to express equivalence, disjointedness etc. among

properties

Limitations of RDF/RDFS

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n RDF\RDFS define a framework, however they have limitations. There is a

need for new semantic web languages with following requirements

n They should be compatible with (XML, RDF/RDFS)

n They should have enough expressive power to fill in the gaps in

RDFS

n They should provide automated reasoning support

n Ontology Inference Layer (OIL) and DARPA Agent Markup Language

(DAML) are two important efforts developed to fulfill these requirements.

n Their combined efforts formed DAML+OIL declarative semantic language.

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n DAML+OIL is built on top of RDFS.

n It uses RDFS syntax.

n It has richer ways to express primitive data types.

n DAML+OIL allows other relationships (inverse and transitivity) to be

directly expressed.

n DAML+OIL provides well defined semantics, This provides followings:

n Meaning of DAML+OIL statements can be formally specified.

n Machine understanding and automated reasoning can be supported. n More expressive power can be provided.

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Example: T. Rex is not herbivore and not a currently living species.

n This statement can be expressed in DAML+OIL, but not in RDF/RDFS

since RDF/RDFS cannot express disjointedness.

n 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.

Example

How is DAML+OIL is different than RDF/RDFS?

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n Web Ontology Language (OWL) is another effort developed by the OWL

working group of the W3Consorsium.

n OWL is an extension of DAML+OIL. n OWL is divided following sub languages.

n OWL Lite

n OWL (Description Logics) DL n OWL Full – limited cardinality

n 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.

n OWL Lite is a subset of OWL DL, which in turn is a subset of OWL Full.

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n Developing new tools, applications and architectures on top of the

Semantic Web is the real challenge.

n AI techniques should be used to utilize the Semantic Web up to its

potentials.

n CBR is an AI technique based on reasoning on stored cases.

n CBR technique can be applied to do intelligent retrieval on metadata

of codes related Earthquake Science.

From Semantic Web to Cased

Based Reasoning

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n CBR is reasoning by remembering: It is a starting point for new

reasoning

n Problem-solving: CBR solves new problems by retrieving and

adapting records from similar prior problems.

n Interpretive/classification: CBR understands new situations by

comparing and contrasting them to similar situations in the past

n Case-based reasoning is a methodology of reasoning from specific

experiences, which may be applied using various technologies (Watson 98)

What is CBR?

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Everyday Examples of CBR

n Remembering today’s route from the place you live to campus and

taking the same route.

n Diagnosing a computer problem based on a similar prior problem.

n Predicting an opponent’s actions based on how they acted under

similar past circumstances

n Assessing a hiring candidate by comparing and contrasting to

existing employees

What is CBR?

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CBR Process

n

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:

n

Stored cases contain a lesson and a specific

context where the lesson applied.

n

The context is used to determine when the

lesson may apply again.

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CBR Process

n

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.

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CBR Process

Problem

Retrieve

Reuse

Revise Retain

Proposed solution Confirmed solution

Case-Base

The CBR Cycle

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Conversational CBR (CCBR)

n

CCBR is a method of CBR where user interacts

with the system to retrieve the right cases.

n

System responds with ranked cases and

questions at each step

n

Question-answer-ranking cycle continues until

success or failure

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Conversational CBR

n

CCBR facilities

¨

Question management facility

¨

Case management facility

¨

GUI for user-system interaction

¨

Facilities to display questions or cases

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A Prototype CCBR Application

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A Prototype CCBR Application

n

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

n

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.

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A Prototype CCBR Application

n

Classification

¨ Initial effort – dummy cases created to classify the different codes ¨ A general approach is needed

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A Prototype CCBR Application

CCBR CASE

Problem Solution

Feature

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A Prototype CCBR Application

How does Case Ranking take place in CCBR?

n

Retrieved cases are sorted based on their consistency

with the query case.

n

As the questions are answered more cases are

eliminated.

n

A case is ruled out only if there is a conflict between the

case and the query case

n

Consistency number for a case remains same if the case

has no answer for the question.

n

Consistency number for a case gets incremented if the

case has the same answer to the question as the query

case.

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A Prototype CCBR Application

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

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A Prototype CCBR Application

How does question ranking take place in CCBR?

n

Questions can be ranked based on their frequency factor

n

Questions can be ranked based on predefined inference

rules

n

Only distinguishing questions are to be ranked

n

Questions can be YES/NO questions, multiple choice

questions or questions with numerical answers.

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n W3C Semantic Web Activity Page. Available from

http://www.w3.org/2001/sw/.

n T. Berners-Lee, J. Hendler, and O. Lassila, “The Semantic Web.”

Scientific American, May 2001.

n Resource Description Framework (RDF)/W3C Semantic Web Activity

Web Site: http://www.w3.org/RDF/.

n D. Brickley and R. V. Guha (eds), “RDF Vocabulary Description

Language 1.0: RDF Schema.” W3C Working Draft 23 January 2003.

n The DARPA Agent Markup Language Web Site: http://www.daml.org.

n OIL Project Web Site: http://www.ontoknowledge.org/oil

References

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References

n CBR on the web

http://www.cbr-web.org

n Case-Based Reasoning Resources

http://www.aaai.org/Resources/CB-Reasoning/cbr-resources.html

n AI Topics - CBR

http://www.aaai.org/AITopics/html/casebased.html

n A mailing list including announcements, questions, and discussion about

CBR, managed by Ian Watson [email protected]

n Riesbeck & Schank, Inside Case-Based Reasoning, Erlbaum, 1989.

n Kolodner, Case-Based Reasoning, Morgan Kaufmann, 1993.