Jpl presentation

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

AIST Meeting JPL, CA 2003


Semantic Web Overview

“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 bedisplayed (HTML) or syntactically (XML), but also by the meaning of themetadata.”

From W3C Semantic Web Activity Page

“The Semantic Web is an extension of the current web in whichinformation is given well-defined meaning, better enabling computersand people to work in cooperation.”

Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web, Scientific American, May 2001

http://www.scientificamerican.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21&catID=2


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 Schema

RDF

DAML,OIL,

DAML+OIL OWL Lite

RDF Schema

OWL DL

OWL Full

From “The 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 RDF and RDF Schema


Resource Description Framework (RDF) - I

Resource Description Framework (RDF) is a framework for describing and interchanging metadata (data describing the webresources).

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 Description Framework (RDF)- II 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.

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


The Dublin Core Definition Standard 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.


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

ResourceProperty

Property Value

Inspired from “The Semantic Web” technical report by Pierce


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.

Why bother to use RDF instead of XML?

http://www.w3c.org/1999/02/22-rdf-syntax-ns

http://purl.org/dc/elements/1.1

http://cgl.indiana.edu/people




RDF 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 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.

It resembles objected-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 RDF/RDFS


RDF\RDFS 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.


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 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.


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 “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.


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

From “The Semantic Web” technical report by Pierce

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.


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 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)

What is CBR?

Overview of Case-Based Reasoning

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?


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.


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 Process

ProblemRetrieve

Reuse

Revise

Retain

Proposed solutionConfirmed solution

Case-Base

The CBR 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 facilityCase management facilityGUI for user-system interactionFacilities 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 SolutionFeature

Feature

Feature = <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 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.




CCBR CASEBASE

Case

Feature 1Feature 2Feature 5

Case = <Problem, Solution>

Feature 1Feature 2Feature 3Feature 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 inference

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

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

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://www.daml.org.

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

References


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

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

http://www.daml.org/

http://www.daml.org/

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.


http://www.cbr-web.org/

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

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

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

mailto:[email protected]

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