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Semantic Web

&Cased Based Reasoning

AIST Meeting JPL, CA 2003

Mehmet S. Aktas

[email protected]



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





Semantic Web Overview

³The 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

³The 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




Motivations

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.




The Semantic Stack and Ontology Languages

XML, XML S chem a

DF

DAML,

OIL,

DAML+OIL O L L ite

DF Schem a

O L DL

O L Fu ll

From ³The Semantic Web´ technical report by PierceThe Semantic Language Layer for the Web

A

B

A = Ontology languages based on XML syntax

B = Ontology languages built on top of DF and DF Schema




Resource escription ramework (R ) -

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.




Resource escription ramework (R )- RDF has following important concepts

Resource : The resources being described by RDF areanything 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.

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




The ublin Core efinition Standard

RDF is dependent on metadata conventions for definitions.

The Dublin Core is an example definition standard whichdefines a simple metadata elements for describing Webauthoring.

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

Following list is the partial tag element list for Dublin Corestandard.

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.




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 ³creator´ refers to a specific definition. (in this example by ublin Core

efinition Standard). So there is a structured UR for this property. This UR makes this

property unique and globally known.

� By providing structured UR we also specified the property value lice as following.

³http://www.cs.indiana.edu/People/auto/b/ lice´

Alic e

ResourceProperty

Property

Value

Inspired from ³The Semantic Web´ technical report by Pierce




Example

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

Inspired from ³The 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. ML organizations. Human readers would

infer the same structure however general purpose applications would not.

�Given R model enables any general purpose application to infer the same structure.

Why bother to use

RDF instead of XML?




R Schema (RDFS )

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 bedescribed.

RDFS allows specific resources to be described as instances of moregeneral classes.

RDFS provides mechanisms where custom RDF vocabulary can bedeveloped.

Also, RDFS provides important semantic capabilities that are used byenhanced semantic languages like DAML, OIL and OWL.

It resemblesobjected-oriented

programming



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 R /R S




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

need for new semantic web languages with following requirements

T hey should be compatible with (XML, RDF/RDFS)

T hey should have enough expressive power to fill in the gaps in

RDFS

T hey 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.


DAML OIL and DAML OIL - I



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 bedirectly 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.


DAML OIL and DAML OIL - II



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 ³list 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 ³is not´ relationships and exclusions.


Example

How is DAML+OIL isiff r t th RDF/RDFS?

From ³The Semantic Web´ technical report by Pierce



Web Ontology Language (OWL) is another effort developed by the OWLworking group of the W3Consorsium.

OWL is an extension of DAML+OIL.

OWL is divided following sub languages. OW L Lite

OW L (Description Logics) DL

OW L 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.


Web Ontology Language (OWL)



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





CBR is reasoning by remembering: It is a starting point for newreasoning

P roblem-solving: CBR solves new problems by retrieving andadapting records from similar prior problems.

I nterpretive/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 specificexperiences, which may be applied using various technologies(Watson 98)

What is CBR?

Overview of C

ase-Based Reasoning



Everyday Examples of CBR

Remembering today¶s route from the place you live to campus andtaking 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 toexisting employees

Wh

at isC

BR?




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 thelesson may apply again.




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.




CBR

P

rocessProblem

etrieve

euse

evise

etain

Proposed solutionConfirmed solution

Case-Base

T e CB Cycle




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




Conversational CBR CCBR facilities

Question management facility

Case management facility

GUI for user-system interaction

Facilities to display questions or cases




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.





Classification Initial effort ± dummy cases created to classify the different codes

A general approach is needed






CCBR CASE

Problem Solution

¡ ¢

F

¡ t

¢ r

¡ ¢

F

¡ t ¢ r

¡ ¢

F

¡ t

¢ r

= <Question Answer>




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 areeliminated.

A case is ruled out only if there is a conflict between thecase 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 querycase.






CCBR CASEBASE

£ Cas £

Feature 1

Feature 2

Feature 5

£ Cas £ = <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




How does question ranking take place in CCBR?

Questions can be ranked based on their frequency factor

Questions can be ranked based on predefined inferencerules

Only distinguishing questions are to be ranked

Questions can be YES/NO questions, multiple choice

questions or questions with numerical answers.




W3C Semantic Web Activity Page. Available fromhttp://www.w3.org/2001/sw/.

T. Berners-Lee, J. Hendler, and O. Lassila, ³The Semantic Web.´Scientific American, May 2001.

Resource Description Framework (RDF)/W3C Semantic Web ActivityWeb Site: http://www.w3.org/RDF/.

D. Brickley and R. V. Guha (eds), ³RDF Vocabulary DescriptionLanguage 1.0: RDF Schema.´ W3C WorkingDraft 23 January 2003.

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

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

References




References CBR on the web

http://www.cbr-web.org

Case-Based Reasoning Resources

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

AI Topics - CBR

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

A mailing list including announcements, questions, and discussion about

CBR, managed by Ian Watson [email protected]

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

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


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