hfoxwell dissertation

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A Web-based System for Representing,Retrieving, and Visualizing Analogies

A dissertation submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy at George Mason University

By

Harry J. FoxwellMaster of Science in Applied Statistics, Villanova University, 1978

Bachelor of Arts in Mathematics, Franklin & Marshall College, 1973

Director: Daniel A. Menasc, Professor of Computer Science

Spring 2003George Mason University

Fairfax, Virginia


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Copyright 2003 Harry J. Foxwell

All Rights Reserved


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DEDICATION

To my teachers who inspired and believed in me, andto my family whose love sustained and supported me.


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ACKNOWLEDGEMENTS

I thank my employer, Sun Microsystems, for financial support, and especially my formerand current managers, Robert McCartin and Paul Tatum, for encouragement andflexibility in scheduling time for my courses and research. Deborah Lapeyre and

Wendell Piez of Mulberry Technologies provided valuable advice in the use of XML andXSLT. Thanks to my doctoral committee, Nada Dabbagh, Peter Denning, Gheorghe

Tecuci, and especially to my advisor, Daniel A. Menasc, for his patience and

encouragement. Special thanks to Honglei Ruan for her excellent work on the AnalogyExpression editor. And ultimately, thanks to my wife Eileen. I could not have completed

this work without her love and support.


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TABLE OF CONTENTS

Page

Abstract................................................................................................................ix

1. Introduction...............................................................................................................1

1.1 Why are analogies important?.................................................................................31.2 What is an analogy? ................................................................................................4

1.3 Why we want to use the Web to access analogy expressions .................................5

1.4 What we want to do with analogies and why .........................................................81.4.1 Representation......................................................................................................9

1.4.2 Visualization......................................................................................................101.4.3 Storage/Retrieval................................................................................................11

1.5 The Focus of this Research...................................................................................121.6 Overview of Contributions ...................................................................................141.7 Dissertation Organization.....................................................................................15

2. Background .............................................................................................................182.1 Web technologies Used in Our research...............................................................18

2.1.1 XML...................................................................................................................182.1.2 XML Editors ......................................................................................................20

2.1.3 Java.....................................................................................................................212.1.4 Jakarta Tomcat ...................................................................................................212.1.5 Apache Cocoon..................................................................................................22

2.1.6 XML Parsers ......................................................................................................222.1.7 XSLT..................................................................................................................232.1.8 XPath..................................................................................................................24

2.1.9 SVG....................................................................................................................242.1.10 Querying XML Documents .............................................................................25

2.2 Related Research...................................................................................................262.2.1 Analogy research in Computer Science.............................................................272.2.2 Analogy Research in Cognitive Science............................................................30

2.2.3 Analogy Research in Education.........................................................................322.2.4 The Semantic Web and Knowledge Representation..........................................33

2.2.5 The MARVIN System.......................................................................................353. The Structure and Components of Analogies .........................................................373.1 Analogy Examples ................................................................................................41

3.2 The Limitations of Analogies ...............................................................................544. The Representation of Analogies............................................................................57


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4.1 Analogy Expression DTD Elements.....................................................................644.2 Creating an Analogy Expression XML Document...............................................72

4.3 Summary...............................................................................................................745. Visualizing Analogy Expressions ...........................................................................76

5.1 Visualization Stylesheets ......................................................................................815.2 Summary...............................................................................................................896. Storing, Retrieving, and Ranking Analogy Expressions ........................................90

6.1 Retrieval Queries...................................................................................................936.1.1 Keyword Match Queries ....................................................................................94

6.1.2 Keyword Generalization Queries.......................................................................966.2 Ordering Analogy Expression Query Result Sets.................................................996.3 Query Examples..................................................................................................103

6.4 Summary.............................................................................................................1047. MARVIN: A Prototype System for Representing, Retrieving,

and Visualizing Analogy Expressions ...........................................................105

7.1 Design Goals.......................................................................................................1057.2 System Architecture............................................................................................109

7.3 Creating MARVIN Analogy Expressions ..........................................................1217.3.1 The MARVIN Analogy Editor ........................................................................121

7.4 Summary.............................................................................................................1238. MARVIN System Evaluation...............................................................................1258.1 Formative Evaluation..........................................................................................126

8.1.1 Survey Results .................................................................................................1328.1.1.1 Discussion of User Survey Results ...............................................................135

8.1.1.2 Discussion of Author Survey Results ...........................................................1368.2 Performance and Scalability...............................................................................140

8.2.1 MARVIN Archive Performance ......................................................................1428.3 Summary.............................................................................................................1459. Conclusions and Further Research........................................................................147

References .................................................................................................................152Appendix...................................................................................................................166Curriculum Vitae.......................................................................................................186


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LIST OF FIGURES

Figure PageFigure 1.1 The MARVIN System Architecture........................................................9

Figure 3.1 The Eye/Camera Analogy .....................................................................42Figure 3.2 Galileos Solar System Analogy ...........................................................43Figure 3.3 The Rutherford Analogy........................................................................44

Figure 3.4 The Bohr Liquid Drop Model of Nuclear Fission.................................45Figure 3.5 Historical Analogy September 11 = Pearl Harbor..............................47

Figure 3.6(a) ConceptSetMap.................................................................................51Figure 3.6(b) PrimaryRelationStructureMap ..........................................................51Figure 3.6(c) RelationToConceptStructureMap .....................................................52

Figure 3.6(d) ConceptToRelationtructureMap .......................................................52Figure 3.6(e) RelationToRelationStrucureMap ......................................................53

Figure 3.7 Circulatory System Analogy .................................................................53Figure 4.1 Modified EBNF Definition of an Analogy Expression .........................62Figure 4.2 The Analogy Expression DTD..............................................................69

Figure 4.3 A Rutherford analogy expression XML document ...............................71Figure 4.4 A minimal Rutherford analogy expression XML document .............71

Figure 4.5 Creating a Rutherford XML file using the XML editor epcEdit ...........75

Figure 5.1 Tabular visualization of Rutherfords analogy......................................79Figure 5.2 Graphical visualization of the Galileo analogy .....................................80

Figure 5.3 Generic tabular visualization of an analogy..........................................85Figure 5.4 Process for creating and visualizing analogy expressions.....................87

Figure 5.5 The Rutherford Analogy visualization using concept links to WebKB89Figure 7.1 Integrating MARVIN into an Instructional or CBI Web Page ............108Figure 7.2 The MARVIN System Architecture....................................................110

Figure 7.3 Sequence diagram: analogy expression transformation,XML to HTML ...........................................................................112

Figure 7.4 Direct Web access of a MARVIN Analogy Expression .....................114Figure 7.5 Sequence Diagram for MARVIN archive query.................................115

Figure 7.6 The MARVIN User Interface..............................................................117Figure 7.7 A tabular visualization of the Rutherford Analogy .............................118Figure 7.8 A graphical visualization of the Galileo analogy ................................119

Figure 7.9 The MARVIN Archive Search interface.............................................120Figure 7.10 The MARVIN Analogy Expression Editor .......................................123Figure 8.1 The MARVIN User Evaluation Survey ..............................................130

Figure 8.2 The MARVIN Author Evaluation Survey...........................................131


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Figure 8.3 Question 1: Analogies help me understand complex topics................132Figure 8.4 Question 2: The MARVIN analogy visualizations will help me

understand the analogies that are presented to me.........................132Figure 8.5 Question 3: The tabular analogy visualizations help me

understand the analogies that are presented to me ........................133Figure 8.6 Question 4: The graphical analogy visualizations help me

understand the analogies and the target subject.............................133

Figure 8.7 Question 5: The ability to use the MARVIN system to look upexample analogies is a useful feature.............................................134

Figure 8.8 Question 6: The ability to use the MARVIN system to look upalternate analogies is a useful feature ............................................134

Figure 8.9 User Survey response summary..........................................................135

Figure 8.10 Question 1: Analogies are an important component of my instruction.136Figure 8.11 Question 2: I understand the components and structures

that can occur in analogies.............................................................137

Figure 8.12 Question 3: The MARVIN analogy visualizations will assist mystudents/readers in understanding the analogies that I present .......137

Figure 8.13 Question 4: The tabular visualizations will help my studentsunderstand the analogies and the target subject.............................138

Figure 8.14 Question 5: The graphical visualizations will help my studentsunderstand the analogies and the target subject.............................138

Figure 8.15 Question 6: The ability to use the MARVIN system to look up

example analogies is a useful feature.............................................139Figure 8.16 Question 7: The ability to use the MARVIN system to look up

alternate analogies is a useful feature ............................................139Figure 8.17 Average Query Execution Time (seconds)........................................144


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Abstract

A WEB-BASED SYSTEM FOR REPRESENTING, RETRIEVING, ANDVISUALIZING ANALOGIES

Harry J. Foxwell, Ph.D.

George Mason University, Spring 2003

Dissertation Director: Dr. Daniel A. Menasc

Analogies are essential in human cognition, reasoning, learning, communication, and

problem solving. They can have a profound and broad effect on how we view and

understand our world. In this dissertation we design, implement, and evaluate a Web-

based system for representing, retrieving, and visualizing human-conceived analogies

that provides a medium and a common language for analogy practitioners to share their

analogies. To accomplish this, we review the components of analogies, and develop a

general representation of their structure. We then develop a compact XML content

model of this representation for use in Web-based environments, and show that the model

is capable of represent ing a wide range of human-conceived analogies. We demonstrate,

using XSLT, several example methods for visualizing analogy expressions that use our

model. We demonstrate methods for storing and retrieving such expressions, and


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develop methods for ranking the retrieved expressions. We designed and implemented

the MARVIN (Markup for Analogy Representation and Visualization for the InterNet)

system to demonstrate these methods. A formative evaluation of the MARVIN system

by analogy authors and end users was conducted; both author evaluators and user

evaluators agreed that the MARVIN system analogy visualizations can assist them in

their use of analogies, and that the systems ability to retrieve analogies and alternates is

also of value.


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1. INTRODUCTION

How do we ever understand an yth ing? I th ink , by

usin g one or another k ind of analogy - th at is,

representing each n ew t hing as th ough it resembles

som ething we already k now.

- Marvin Minsky

When Meriwether Lewis and William Clark retu rn ed in 1806 from th eir

hist oric 18-month explora tion of wester n N ort h America, th e most significan t

item th ey brought back was not th e specimens of un known plant a nd an imal

species, nor the accoun ts of th e nat ive inh abitan ts of th at a rea. What t hey

brought back, indeed, th e prima ry pu rpose of their expedition, was a ma p, a

ma p of hit her to un kn own t err itory, a ma p for oth ers t o follow [VIAs, 1998].

Maps s how th e way, how to get from wher e you a re t o wher e you wa nt to go.

They represent key featu res and th eir relative locat ions, an d a llow you to

orient your self while traveling th rough un fam iliar t erritory. Lewis & Clar k's

ma p ha d a pr ofound influence on t he newly form ed United Sta tes. It guided

millions of sett lers a nd explorer s from t he familiar lan d of th e original

Ea stern colonies to th e new lan ds of th e West.


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Maps can be crea ted by explorer s for oth ers t o follow. But th ey can also serve

as r eminder s. They can d ocum ent significan t discoveries as well as blind

alleys an d wrong tu rn s. They can be a valua ble record of th e explora tion

process.

Analogies are like ma ps of un fam iliar kn owledge ter rit ories, crea ted by th e

explorers of th ose ter rit ories for oth ers t o follow. They sh ow you h ow to get

from what you kn ow to what you wan t t o know. Like geograph ical ma ps,

th ey represent key featu res -- concepts -- and th eir relat ionsh ips, and t hey

help you orient your self while you ar e lear ning. And just as Lewis and

Clark's map guided tr avelers from t he kn own to th e unk nown, analogies have

guided hu man s from existing ideas an d a ssumpt ions to new kn owledge.

When Ga lileo wan ted t o lead people from th e fam iliar Ea rt h-cent ered world

of Pt olemy to the u nfam iliar Sun -cent ered world of Copern icus, h e crea ted a

ma p -- an a na logy ma p -- showing how th e solar s yste m of plan ets wa s like

th e J ovian system of sat ellites th at he directly observed t hr ough his t elescope

[Galileo, 1632]. When Da rwin tr ied to lead people from t he creat ion-cent ered

world of sta t ic biologica l species t o th e dyn am ic world of biological evolut ion,

he creat ed his famous a na logical ma p showing how to get from t he familiar

process of an imal a nd pla nt var iat ion u nder domestication to th e process of

natural selection [Darwin, 1859].


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This dissertat ion first present s a map of what might be called Ana logy Lan d;

describing the point s of int erest a nd t he r out es you m ust tr avel to visit th em.

We then provide you with ma pma ker s tools -- wha t you need t o crea te a nd

sha re m aps of your own explora tions, an d to under sta nd t he a na logy ma ps of

oth er explorer s.

1.1 Why are a na logies import an t?

Ana logies pervade all hum an comm un icat ion a nd lear ning. They occur in an

extraordinar y scope an d variety, ra nging from t he simplest ratio form, such

as ha nd is t o ar m a s foot is t o leg, th rough exten ded an alogical essa ys

proposing tha t a comput er is like a brain [von N euma nn, 1958], the In tern et

economy is like the E nglan d ra ilroad boom of th e 1800s [Arth ur , 2002], an d

th e mind of an au tist ic is like a Web browser [Gran din, 2000].

Analogies are widely used when explain ing ideas, especially in ins tr uctional

cont exts . Us ing an alogies is one of the core pr ocesses of cognit ion [Forbus,

2001], and m ay be the primary process of all cognition and communication

[Hofst adt er, 2001]. Analogies ar e a k ey componen t of learn ing-by-example,

or case-based rea soning [Scha nk , 2000], an d a re qu ite comm on in science

educat ion [Glynn , 1997], [Pa ris, 2000]. And, alt hough a na logies ar e gener ally


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Ana logies assist in a cquiring new kn owledge by at tempt ing to map t he

structure of existing knowledge to new situa tions [Gent ner , 1983]. For

example, in a widely kn own a nd often cited an alogy, Er nest Rut her ford in

1910 proposed th at an un fam iliar idea th e str uctur e of th e at om, is like the

str uctu re of a pr esum ably fam iliar object th e solar syst em [Bohr ,

1922][Gent ner , 1983]. In th is exam ple the a tom is th e targetanalogof the

an alogy an d th e solar system is th e source analog.This ana logy suggests th at

we try tom ap

what we cur rent ly know about th e sour ce t he solar system's

par ts an d their interr elations, to th e tar get t he par ts of th e atom, allowing

us to mak e plausible inferen ces an d predictions a nd t o form hypotheses a bout

th e ta rget object. Some of th ese predictions a nd inferences may t ur n out t o

be wrong, but th e an alogy provides a useful str uctu re, or scaffold, for

genera ting and consider ing th em. [Roblyer an d Edwar ds, 2000][Bru ner ,

1986].

1.3 Why we wan t t o us e th e Web to access an alogy expressions

The World Wide Web (WWW) is a syst em for storing, r etr ieving, an d

visualizing inform at ion au th ored by people anywher e in th e world. It is

gener ally built upon widely accepted t echn ology st an dar ds, and consequ ent ly

its a ccessibility can extend globally. Em erging technologies such a s th e

Ext ensible Mar ku p La ngua ge (XML) [W3C, 2000] are n ow us ed t o impr ove


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an d r estru ctu re Web-based inform at ion by allowing th e separa tion of the

content from the mechanisms of its presentation, thereby allowing multiple

form s of access an d display on diverse devices usin g th e sam e sour ce dat e.

The Web and its t echn ologies const itut e an ideal environmen t for sh ar ing and

comm un icat ing h um an -conceived a na logies given it s u biquity, global r each,

and u niversal standards.

One of th e most im porta nt consequ ences of the Webs growth an d scope is th e

form at ion of comm un ities of pra ctice cent ered on persona l, social, an d

especially professiona l inter ests. The Web provides th e medium for

comm un icat ion, an d XML support s th e developmen t of a comm on lan gua ge

for each comm un ity. Busin esses, governm ent a gencies, an d academ ic

disciplines are u sing XML to develop lan gua ges for excha nging da ta , ideas,

an d document s within th eir respective commu nities. For exam ple,

ma th emat icians h ave agreed upon a common langua ge for r epresenting

ma th ema tical expressions on t he Web [W3C, 2001], chem ists u se Ch emical

Mar kup La ngua ge [Murr ay-Rust, 1998], and th e U .S. Departm ent of J ust ice

is developing Ju stice XML, a set of projects to ena ble repres ent at ion a nd

sharing of data on criminal activities, biometric information, and driving

records [USDOJ, 2002].


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Web-based comm un ities of pr actice cent ered on edu cat ion topics a nd issues

ha ve become a n especially effective aid to tea chers a nd st uden ts [Gordin an d

Gomez, 1996][Wenger, 1998]. Edu cat ion comm un ity professiona l ha ve also

used XML to define a comm on langu age for exchan ging inform at ion a nd da ta

about cur riculum st ru ctu re an d cont ent u sing the Learning Object Metada ta

Sta nda rd [IEEE, 2003].

We suggest t he need for a mecha nism t o represent, record, sha re, an d

ret rieve hu ma n-conceived an alogies in a st ru ctur ed an d globally accessible

form , for comm un ities of an alogy practitioners. Su ch web-based commu nit ies

ar e alrea dy form ing [Ruh l, 2002]. We th erefore r equire a compa ct a nd

general represent at ion for a na logies tha t su pport s th eir expression a nd

visualization using sta nda rd Web-based t echn ologies, and th at is relat ively

easy to un dersta nd, aut hor, an d extend. Our a pproach is to provide an

au gmenta tion of existing Web documen ts t ha t cont ain a na logies rat her t ha n

embedding cont extual mar kup within t hose docum ents. This approach

recognizes the im men se volume of existing HTML-based Web conten t t ha t

will persist for man y years, a nd pr ovides a mecha nism for cont ent au th ors to

include easily creat ed stru ctu red cont extual inform at ion a bout an alogies

described th eir Web docum ent s.


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1.4 What we want to do with a na logies an d why

In order to comm un icat e about a na logies using t he Web, we need to represent

th em in a st an dar d form at , and pr ovide tools to au th or, display, use, shar e,

an d reuse the an alogies. Ha ving recorded an alogies in a st an dar d form at

th en perm its t he st ora ge, retr ieval, an d compa rison of ana logies in

educat iona l an d other explana tory cont exts. The ability to reference mu ltiple

an alogies for a given ta rget h as been sh own t o increase stu dents

un dersta nding of th e tar get [Nott is and McFarlan d, 2001], and it h as been

recommended that teachers develop a repertoire of analogies [Thiele and

Treagust , 1994]. Our system is th erefore designed t o assist a na logy

pra ctitioners -- au th ors a nd u sers of an alogies providing a comm on

language for representing, visualizing, and retrieving analogies, providing

Web access to multiple alternate analogies, and providing an archive of

interesting and useful human-conceived analogies.

Cha pter 7 describes MARVIN (Mark up for Ana logy Represen ta tion an d

Visualizat ion for th e Int erNet), a system th at defines an XML-based

represent at ion for a na logies and demonst ra tes h ow it can be used t o

represent an d visualize an alogy expressions, an d t o store, retrieve, and sha re

such expressions in local an d Web-based a rchives. Figur e 1.1 shows th e

genera l architectu re of th e MARVIN system .


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Figure 1.1 The MARVIN System Architecture

1.4.1 Representation

We require a compact a nd int uitive representa tion of an alogies th at is easy to

au th or wit h gener ally available tools, and is capable of expressing mu ch of

th e range of an alogies that hu man s are able to generat e. Such an expression

mu st be consisten t with curr ent t heory and resear ch findings on a na logy

MARVINProgrammable

Proxy Server

Instructional URL

MARVIN URL:___ Help

MARVIN User Interface

MARVIN Search EngineQuery:_________

Results:

WWW RemoteInstructional/Content

Web Servers

Local

Instructional/ContentWeb Servers

Analogy Expression(s)

Network

User Clientand Browser

MARVIN Transformation & Search Engine

Analogy Expression DTD

XSLT Stylesheets

XML Transformation

Engine

XML Search

EngineXML AnalogyExpression Archive


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components a nd st ru ctu re, and m ust be programm at ically useful to permit

Web-based shar ing, storage, retr ieval, an d m an ipulation of the expressions.

The MARVIN syst em defines su ch an XML cont ent model for th e crea tion of

an alogy representa tions; with t his model, ana logy auth ors can creat e an alogy

expressions us ing XML editors or th e Analogy Express ion Edit or described in

Chapter 7.

1.4.2 Visualization

Visua lizat ion is a visual/spat ial display in which inform at ion is

comm un icat ed by th e spatial ar ra ngement of element s in the repr esenta tion

[Hegart y, 2002]. Such displays are cognit ive aids th at pr omote mem ory an d

information processing [Tversky, et al., 2002]; visualizations of analogies

facilita te learn ing and enha nce the u se of analogies in both inst ru ction an d

pr oblem solving [Cra ig, et a l., 2002], [Par is, 2000]. We requ ire a m eth od for

pr oducing mult iple visua lizat ion form s from our a na logy expression th at

separ at es th e process of producing the visua lizat ion from th e expression of

th e cont ent an d st ru ctu re of th e an alogy, and th at allows for display of th ese

visualizat ions u sing sta nda rd Web browser t echn ologies.

An an alogy can be underst ood when th e person hear ing or reading it k nows

someth ing about th e sour ce an alog and is a ble observe and ma p th e


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component s of th e sour ce to the ta rget. Un derst an ding of th e ana logy is

significan tly improved when t he relat iona l stru ctu res pr esent in t he an alogy

can be visualized in a t abu lar or gr aph ical form [Pa ris, 2000][Mat ocha ,

Cam p, and Hooper, 1998]. The MARVIN system pr ovides this capa bility

through the use of XSLT stylesheets, which transform analogy expressions

into visua lizations t ha t can be displayed using sta nda rd Web browsers.

1.4.3 Storage/Retrieval

We wish to make interesting and useful human-conceived analogies storable

an d retr ievable in a sta nda rd str uctured form at for comput at ion an d

expression. An ar chive of such expressions would perm it t he r ecordin g of

an alogies conceived du rin g th e development of problem solut ions in order to

document, replicate, and share the thought processes of the problem solvers

[Dunba r, 2001]..

For those who wish to use analogies for instructional or explanatory

pur poses, a sea rcha ble archive of an alogy express ions m ay be queried t o

locate an app ropriat e ana logy for t he topic un der discussion. Or, if a

proposed ana logy is not under stood by the learn er or reader , the a rchive ma y

be queried to locat e additiona l or a lterna te a na logies better suited t o the

learner's backgroun d knowledge. Edu cat ion r esearchers ha ve recomm ended


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th at tea chers develop a r epert oire of an alogies for t heir in str uction [Thiele

an d Treagust , 1994]; th ey have also foun d t ha t present ing multiple ana logies

for a given tar get a na log resulted in greater u nderst an ding of th e tar get

[Nott is and McFar land , 2001].

The a bility t o ret rieve several an alogies of possible inter est im plies the n eed

for a na logical ra nking m ethods th at ma y be used to order th e results of a

query. Such meth ods, discussed in Chapt er 6, perm it a learn er to select

can didate an alogies most a ppropriate to the lear ning task . The MARVIN

system was t herefore designed to implement st ora ge, retr ieval, an d ra nkin g

of an alogy expressions.

1.5 The F ocus of this Resear ch

Analogy resea rch h istorically ha s focused on several ba sic an d overla pping

areas understanding the cognitive processes of analogical reasoning

[Hofstadt er, 1995][Mar sha ll, 1999][Gent ner , 1989][Falken ha iner, 1989],

computer simulation of human analogical reasoning [French, 1995][Gentner,

1989][Forbus, Gentner, and Law, 1995], and using analogies in educational

sett ings [Glynn, 1997][Schan k, 2000][Par is, 2000]. While the compu ter

simulation research has yielded great insight into the cognitive foundation of

an alogical r easoning, it is often limited t o relat ively small, well defined, an d


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easily represen ted a rea s of kn owledge known as microdomains. CopyCat

[Hofstadt er a nd Mit chell, 1994] an d TableTop [Fren ch, 1995] ar e examples of

th is approach. As noted in [Forbus, 2001], such systems r epresent a nd t est

limited form s of an alogy-relat ed ta sks, an d such syst ems can not possibly

scale to ha ndle th e kinds of cognit ive processing th at hu ma n beings clear ly

do. The work pr esent ed here is focused on helping hu ma ns share and u se

analogies they have already conceived or discovered, rather than using

compu ter s t o discover a na logies or t o perform an alogical r easoning.

Hu ma n an alogical r easoning t ypically involves th e following pr ocesses

[Holyoak, et al., 2001]:

- recall a sour ce ana log

- ma p th e componen ts of th e sour ce to the t ar get

- generat e plausible inferences about t he t ar get

- evalua te t he inferences about th e ta rget

- accept (lear n/rem ember ) new kn owledge about t he t ar get

The system described in t his dissertat ion focuses on helping huma ns with th e

first t wo steps ret rieving source ana logs alrea dy perceived an d described by

oth ers, and visualizing the ma ppings between th e sour ce an d ta rget a na logs,

an d the final step remem bering th e an alogy. Genera ting and evalua ting


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inferences suggested by the analogy remains the responsibility of the analogy

au th or an d users. That is, we are not concern ed here with comput er -aided

an alogy gener at ion.

1.6 Overview of Cont ribu tions

The pr imar y contr ibution of our work is th e design a nd developmen t of

MARVIN (Mar kup for Analogy R epresentation and V isualization for th e

InterN et), a prototype Web-based system t ha t enables aut hors an d user s of

instr uctiona l cont ent to record, r etrieve, visua lize, an d quer y hu ma n-

conceived ana logy expressions. We demonst ra te t he usefulness of th e system

th rough a form at ive evalua tion pr ocess by cont ent experts an d au th ors, an d

by end user s. Additional cont ribut ions a ssociat ed with t he developmen t of

th e MARVIN syst em a re discussed below.

We developed a compact, genera l repr esent at ion of an alogy expressions,

usin g an XML conten t m odel for u niversa lity and Web-based a ccess, an d

demonst ra ted th rough exam ples the power of th is represent at ion t o express

an alogies from va ried doma ins, su ch as science, history, medicine, religion,

and literatu re.

We developed mu ltiple visualizat ion meth ods, such as ta bular an d gra phical


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displays, which ar e generat ed directly from th e XML an alogy represent at ions

usin g th e Ext ensible Stylesheet Lan gua ge (XSL) [W3C, 1999], an d

demonst ra ted a variety of example visua lizations.

We developed Web-based met hods for s tora ge an d r etr ieval of ana logy

expressions, a nd demonst ra ted examples of retr ieving altern at e an alogies

an d ra nking th e retr ieved expressions.

The above contr ibutions use our m odel of th e component s an d str uctu re of

an alogies. This model is consist ent wit h both comm on usa ge an d form al

char acter izations of an alogies from cognitive science r esear ch, from

educat iona l pra ctice and r esear ch, an d from work on Web-based k nowledge

representation.

1.7 Dissert at ion Organ izat ion

The r ema inder of th is disserta tion is orga nized as follows:

Chapt er 2 discusses t he var ious Web techn ologies used in th e design an d

implementation of the MARVIN system, and reviews prior research on

an alogies by compu ter scient ists, cognitive scient ists, an d educat ion

researchers.


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Cha pter 3 reviews and defines t he componen ts of an alogies, discuss es th e

basic structure of analogies, and introduces a graphical representation for

severa l key st ru ctu res comm only found in ana logies. This cha pter a lso

present s a nd discusses several exam ple ana logies from various doma ins.

Chapt er 4 discusses the n eed for a genera l-pur pose repr esenta tion for

analogies, discusses design goals for such representations, presents a formal

definition for analogy expressions, and implements that definition using an

XML cont ent model.

Chapt er 5 discusses h ow a na logies th at ar e expressed using our XML cont ent

model may be visualized in severa l form s us ing Web-bas ed t ools su ch as XSL

Tra nsform at ions (XSLT) [W3C, 1999] an d S cala ble Vector Gra phics (SVG)

[W3C, 2001], discuss es design goals for such visua lizat ions, a nd presen ts

several example visualizations.

Chapt er 6 discusses t he st ora ge and retr ieval of an alogy expressions, a nd

intr oduces and demonstr at es meth ods for r an king the r esults of queries of

an alogy expression ar chives.

Cha pter 7 discuss es th e design goals of th e MARVIN syst em for r ecordin g,


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retrieving, and visualizing analogy expressions that use our XML model and

stylesheets, an d describes the components a nd system ar chitecture.

Per form an ce char acter istics of th e MARVIN syst em a re a lso discuss ed. The

Analogy Expression Editor, used to aid authors in creating XML analogy

expressions, is a lso described.

Chapt er 8 pr esents t he r esults of form at ive evaluations of the MARVIN

system by cont ent a ut hors and individual users.

Chapt er 9 provides a su mma ry of th e dissert at ion, an d discusses fut ur e

resear ch su ggested by this work in th e area of an alogy represent at ion,

visua lization, an d retr ieval.


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2. Background

2.1 Web Technologies Used in Our Resear ch

The work described in th is dissert at ion em ploys several techn ologies

developed by th e World Wide Web Consort ium (W3C) th at were designed t o

provide stru ctu re a nd m eaning t o Web-based cont ent an d t o provide

progra mming langua ges and int erfaces to access tha t cont ent. These

techn ologies include XML (Ext ensible Mar ku p La ngu age), XSLT (Extensible

Stylesheet Langua ge for Tran sform at ions), and related tools an d lan guages

such as Xerces, XPath, Xalan, Apache Tomcat, Jakarta Lucene, Apache

Cocoon, a nd H TML. The following sections pr ovide overviews of th ese

techn ologies. The section 2.2 of th is cha pter reviews resea rch concern ing

analogies.

2.1.1 XML

XML (Exten sible Mar ku p Lan gua ge) is a met a-lan gua ge designed to provide

a universal format for structured documents and data on the Web [W3C,

2000]. Virtu ally all Web pages are cur rent ly writt en an d form at ted using


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HTML (HyperText Mar ku p Lan gua ge), which was specifically designed for

presen ta tion and display of Web cont ent [W3C, 1999a]. But H TML ha s no

capa bility for at ta ching meaning or inter preta tion to the cont ent. Using

XML, content authors can create special-purpose descriptive languages that

can be used to ta g cont ent st ru ctu re an d componen ts. Such langu ages

enable commu nities of practitioners to use a common lan guage th rough th e

Web for effective sha rin g of data an d ideas.

XML ta gs ar e un ique, case-sen sitive labels for sections of cont ent , delimited

by angle brackets. Tags are used to encapsu late cont ent elements a nd give

th em mean ing. This enables sear ch engines and other Web tools to reduce

am biguit y in the sear ch space. F or exam ple, ta gging th e word Brown in a

Web document listing personal information, using

Brown or Brown

permits a search engine or oth er pr ogram to distinguish between Brown th e

na me and Brown th e color.

The XML specificat ion d escribes str ict ru les for th e ta gging of cont ent in

order to simplify docum ent pa rsin g an d to eliminat e am biguit y. Cont ent

element s have sta rt -ta gs and end-t ags; an XML docum ent is well form ed if

element s delimited by start -ta gs and end-ta gs nest pr operly with in each

oth er (th at is, text is not per mitt ed, while


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text is properly nest ed). All XML docum ent s mu st be well

formed.

XML ta gs ma y be defined with in t he docum ent cont ent file itself, or m ay be

defined in an as sociat ed file usin g th e XML DTD (Documen t Type Definition)

specificat ion [W3C 2000] or th e XML Schema specificat ion [W3C, 2001]. A

validXML file uses only th e ta gs defined in its DTD or Schema file, and m ust

also be well form ed.

2.1.2 XML Editors

The MARVIN s ystem r equires t he creat ion of XML files to represen t

an alogies. Ther e are nu mer ous comm ercial an d open-sour ce XML editors,

su ch as XML-Spy [XML-Spy, 2002], MS XML NoteP ad , Morphon [Morp hon,

2002], an d epcEDIT [epcEDIT, 2002] to na me only a few. Such editors en able

the creation of valid, well-formed XML files that conform to a DTD or

schema . Ana logy au th ors u sing th e MARVIN system can elect to use such

editors to crea te a na logy expression files, but u sing such editors r equires

kn owledge of XML st ru ctu re an d synta x. We th erefore designed and

implement ed an Ana logy Expression E ditor, writ ten in J ava, for u se with t he

MARVIN system, wh ich perm its t he crea tion a nd modificat ion of XML files

th at conform to our cont ent m odel, without r equiring the au th or t o know any


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XML. This editor is described fur th er in Ch apt er 7.

2.1.3 Ja va

The J ava program ming lan gua ge [J oy and Gosling, 2000] is ideal for Web-

based a pplicat ions because of its r ich n etwork APIs a nd it s ability to run on

diverse opera tin g system s and ar chit ectu res. Most of th e techn ologies used

in th is dissert at ion u se J ava directly or indirectly, becau se th ey are writt en

as J ava applications or as J avaservlets

[Horstm an n an d Cornell, 2000][Sun,

2003]. Servlets ar e used to extend t he capa bilities of Web servers by enabling

progra mmer s t o produce intera ctive, dyna mic Web cont ent based on u ser

actions and dat a cont ent. Servlets run within a containerthat m anages the

servlets int era ction with t he ser vers opera tin g system an d Web server.

Servlet cont ain ers ar e typical componen ts of comm ercial an d open-sour ce

Web and a pplicat ion ser vers, and can also be implemented as sta nd -alone

Web ser vices.

2.1.4 J aka rt a Tomcat

J aka rt a Tomcat is an open-sour ce J ava ser vlet cont ainer th at is the official

reference implemen ta tion for J ava Ser vlets [Apache, 2002]. It can be

int egrat ed with Web servers su ch a s Apache [Apache, 2002], or r un as a

sta nd-alone Web service. The MARVIN prototype system described in


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Chapt er 7 is implement ed using J aka rt a Tomcat t o ru n th e XML

transformation and search servlets.

2.1.5 Apache Cocoon

Apache Cocoon [Apache, 2002] is an XML Web publishing framework that

ru ns as a servlet within Apache Tomcat. It ena bles th e development ,

ma na gement , and gener at ion of dyna mic Web cont ent from XML sour ce

docum ents. It permits th e separa tion of th erepresentation

of Web content

from t he processing necessa ry to gener at e mult iple form s of display. The

MARVIN system uses Cocoon t o tra nsform XML repr esent at ions of ana logies

into a variety of visualizat ion form s, an d t o int erface with th e ret rieval and

text sear ch servlets.

2.1.6 XML Pa rser s

XML document s must be read an d int erpret ed according to th e XML

specificat ion. A program t ha t perform s th is task is called an XMLparser. A

par ser th at enforces a docum ent 's complian ce with a DTD or schema is called

a validating par ser. Ther e ar e several comm ercial an d open-sour ce

validat ing XML par sers ava ilable. For th e work described in th is

dissert at ion, we use the Xerces Java Parser [Apache, 2001], which supports

the XML 1.0 recommendation [W3C, 2000]; the Apache Cocoon servlet used


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in th e MARVIN system usesXerces to parse the XML analogy expressions.

2.1.7 XSLT

XSLT (Extensible Stylesheet Language for Transformations) is a language

for t ra nsform ing XML document s int o oth er form s, including PostScript ,

Adobe PDF, HTML, Java, and alternate XML representations [W3C, 2001].

XSLT is also a specificat ion for su ch t ra nsform at ions. It is par tia lly

implemen ted in some browsers su ch a s [Mozilla, 2002], but th ese

implement at ions ar e still imma tu re an d buggy. A more matu re an d complete

XSLT pr ocessor is Xa lan [Apache, 2002]. The Xalan p rocessor oper at es on a

parsed XML document and transforms it according to instructions contained

in a stylesheetfile. These inst ru ctions , called templates, specify

tr an sform at ions t o be applied to selected n ode element s of th e par sed XML

docum ent [Kay, 2001].

The XSLT program min g model is not pr ocedur al, driven by th e program code.

Rath er, it is event-orient ed, driven by the da ta , in th is case by th e XML

docum ent . When t he XSLT processor reads th e pars ed XML docum ent , it

detects document node mat ch event s a nd processes th e node data according

to the templat e defined for t hat node.


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XSLT templa tes m ay be th ought of as in dependen tly selecta ble processing

instr uctions t ha t bind to a n ode in th e XML docum ent when a ma tch is

encoun ter ed, ana logous t o th e way messenger RNA binds to a segment of

DNA dur ing cell r epr oduction [Piez, 2002], [Foxwell, 2002]. Like th e

messen ger RNA provides inst ru ctions for const ru ctin g a specific protein, t he

tem plat e provides inst ru ctions for const ru cting a sp ecific componen t of an

outpu t docum ent . The Apache Cocoon ser vlet used in th e MARVIN syst em

uses theXalan

XSLT pr ocessor.

2.1.8 XPa th

XPa th is an XML langu age specificat ion for r eferen cing n odes of a p ar sed

XML docum ent [W3C, 1999b]. It s synt ax is similar t o th at for compu ter file

system directories, an d perm its r eference to a documen t's element a nd

at tr ibute nodes, an d to th eir parent a nd child nodes. XSLT templates cont ain

XPath references to document nodes, and the instructions for processing the

nodes. XSLT processors such as Xala n ma ke use of XPa th wh en referen cing

XML docum ent nodes for t he t empla tes t o process.

2.1.9 SVG

SVG (Scalable Vector Gra phics) is a lan guage for describing t wo-dimensiona l

gra phics in XML [W3C, 2002], allowing t he genera tion of lines, curves,

ima ges, an d text from an XML docum ent specificat ion. SVG provides a


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compact a nd porta ble means for generating r esizable and searchable Web

based ima ges [Eisenberg, 2002]. Browsers su ch a s MS IE an d Mozilla ar e

beginn ing to support t he display of SVG gra phics directly, but a s with XSLT,

th ese are st ill early implement at ions an d do not completely support t he full

SVG specificat ion. However, t her e ar e tools for convert ing SVG gra phics to

browser-displaya ble GIF or J PG form at gr ap hics. The Cocoon servlet used in

th e MARVIN system includes an SVG-to-J PG tr an sform er; th e SVG ana logy

visualizat ions pr oduced by th e MARVIN system can t hu s be convert ed to

JPG format and displayed on any graphics-capable Web browser.

2.1.10 Quer ying XML Docum ent s

Ther e ar e various tools un der developmen t for sea rching th e cont ent s of XML

docum ent s. XQuer y [W3C, 2002] is an XML quer y lan gua ge cur ren tly under

developmen t by W3C, but t he sp ecificat ion for t his la ngua ge is still in

Working Dra ft st at us, an d at th is time there a re few complete

implementations. On the other han d, there are text search engines tha t can

ma ke us e of XML mar ku p in docum ent s, providing the a bility to perform a

str uctur ed sear ch for text within XML ta gged fields. One such str uctu re -

awa re search engine is Ja kar ta Lucene [Apache, 2002], written in J ava, an d

implement ed as a J ava servlet. Lucene allows queries based on XML tag

cont ent; th e MARVIN system uses Lucene a s its sear ch a nd r etrieval


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component.

2.2 Relat ed Research

Resear ch concern ing ana logies occur s in man y disciplines. Compu ter

scient ists in th e fields of Art ificial Int elligence and Ma chin e Lear nin g build

systems that attempt to model analogical reasoning; cognitive scientists also

build such systems to investigate the underlying mental processes involved in

memory, ana logy perception, a nd an alogical r easoning; and educat ors stu dy

th e use, an d abus e, of an alogies in tea chin g. Becau se the compu ter is often

used as a sur rogat e for st udying and modeling the h uma n m ind, there is

significan t overlap between cognitive science resea rch a nd compu ter science

resea rch int o th e work ings of an alogies.

Additionally, historians analyze and debate the usefulness of historical

an alogies [Neu sta dt, 1988][Rour ke an d Taylor, 1995], and legal scholar s an d

practitioners make extensive use of analogies in legal arguments [Ashley,

1991]. Analogies ar e also quit e comm on in jour na lism and edit oria l writing,

although their overuse and oversimplification of important ideas has been

criticized [Clark, 2002].

In sh ort , ana logies ar e foun d in n early all area s of hu ma n commu nicat ion


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an d lear ning, an d ar e widely stu died in th e scientific an d social disciplines.

Commu nities of analogy practitioners an d r esearchers use th e Web

exten sively to sha re examples, ideas, and resear ch resu lts. The following

sections review an alogy resea rch in Compu ter Science, Cognitive Science, and

Education.

2.2.1 Ana logy Research in Compu ter Science

Among the prominent ear ly resear chers in comput er learning an d reasoning

by ana logy is Pa tr ick Winst on of th e Art ificial Int elligence Labora tory at MIT

[Winst on, 1980]. He designed a LISP-based Fra me Representat ion La ngua ge

(FRL) system derived from Minsk y's kn owledge repr esent at ion frames

[Minsk y, 1985]. FRL was a pplied to finding a na logous st ory plots in

litera tu re sam ples th at were already expressed in concept/relation st ru ctu res.

It used weight ed ma tching criteria to compa re concepts a nd relat ions, giving

high weight s to th ose th at were of particular import an ce to plot st ru ctu re.

While Winst on wa s a ble to find su b-plot a na logies between sections of

Shakespeare's Hamleta ndMacbeth , for examp le, this resea rch focus ed on

finding ana logies th at were already kn own a nd in limited kn owledge

domains.


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Much a na logy resear ch explores r elatively small, well-defined, a nd easily

represent ed ar eas of knowledge kn own a s microdomains. The ANALOGY

pr ogra m [Evan s, 1968], for example, exam ined spa tia l ana logies am ong

geomet ric sha pes. Decades later , microdomains such as Copycat[Mitchell,

1999], Tabletop [Fr ench, 1995], an d IDA [Wolverton, 1994] continue to yield

insight int o th e cognit ive processes t ha t cau se th e perception of ana logies.

Copycat, for example, examines analogies between character strings, and

Tabletopgenera tes a ction an alogies using comm on objects on a kitchen t able.

Wolverton'sIDA sought to generat e engineering design an alogies, and

focused pr ima rily on finding a n efficient algorit hm for sear chin g a pr edefined

problem space ontology.

Some at tem pts a t compu ter modeling of an alogy retr ieval use concept

indexing, spreading activation, and network-matching approaches [Collins

an d Loftu s, 1975]. These techniqu es were u seful in findin g sem ant ically close

an alogies (target an d source ana logs tak en from t he sa me specialized

kn owledge domain), but t hey were not scalable to finding sema nt ically

distan t an alogies across mu ltiple large kn owledge doma ins (the t ype hum ans

ar e good at creat ing). [Wolvert on, 1994] proposed a r efinemen t of spr eadin g

activat ion, called Kn owledge-Directed Spr eading Activat ion, consist ing of

multiple network-ma tching search agents th at would persist wh en concept


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ma p fragments were m at ched a nd would reduce or st op activat ion wh en n o

fragmen t ma tches were found. Hofst adt er an d Mitchells Copycatprogram

[Mitchell, 1999] used a s imilar a gent-based appr oach, with softwa re a gents

perform ing mu ltiple, ran dom searches t hr ough th e source knowledge a rchive;

each a gent would gain or lose resources for cont inu ed sear ching a ccordin g to

its su ccess in findin g can didat e concept ma tches.

Becau se ana logies are per ceptions t ha t n eed to be tested a gainst r eality,

hu ma n int erpret at ion a nd expertise should be used to validate an alogies

pr oposed by a comput er. The Disciple syst em [Tecuci 1998], for exam ple,

suggests problem solut ions derived using a na logy to a hu ma n expert , who

accepts or rejects t hem a nd pr ovides an explan at ion for t he decision. The

meth od used in Disciple illustr at es an import an t idea in searching for

an alogies: repla cing a concept in a k nowledge expression with a

generalization of th e concept. Hofsta dter calls th is variablization, and

Mitchell calls it conceptual slippage [Hofsta dter , 1995]. A key cha ra cter istic

of th is process is tha t t he relations am ong th e concepts in t he kn owledge

expression are preser ved. [Gent ner , 1983] calls this preser vat ion of

relational str uctur e th e systematicity principle .

Anoth er resea rch effort in th e modeling of hu ma n r easoning is th e Cyc

Pr oject [Lenat a nd Gu ha , 1990]. Cyc is a large, gener al pur pose kn owledge


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The pr esenta tion an d visualizat ion of ana logies for instr uctiona l pur poses

ha ve been sh own t o significan tly enha nce learn ing and ret ention, part icular ly

for n ovel or complex topics [Mat ocha , Cam p, an d Hooper , 1998], but

educat ors r ecognize the limita tions an d dan gers of misleadin g ana logies.

Some have at tem pted t o develop met rics for a na logical validity [Nott is an d

McFar land, 2001], while most h ave empha sized th e need, during t he

an alogical rea soning process, to indicate wh ere a na logies brea k down [Her r,

2001].

2.2.4 The Semantic Web and Knowledge Representation

At presen t, most of th e document s on t he World Wide Web are visua l and

textua l, mark ed with H TML tags for br owser display form at ting an d for ea se

of na vigat ion am ong docum ent s. Tim Bern ers-Lee, credited with inven tin g

th e Web, envisions a richer form of cont ent he calls th e Sem antic W eb

[Bern ers-Lee, Hen dler, and Lass ila, 2001], [W3C, 2002]. In t he Sema nt ic

Web, every object (word or im age) in a docum ent is labeled with it s mea nin g

an d context, a nd is linked t o rela ted objects a ccording to th e pur pose of th e

documen t. Such labeling and linking, using XML metada ta , may event ua lly

enable intelligent softwa re a gents t o access, int erpret , and tr an sform all Web

cont ent for other softwar e agents a s well as for h uma n u sers.


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RDF (Resour ce Description F ra mework) is a fram ework th at supports t he

Sema nt ic Web project for describing an d excha nging da ta about objects

repr esent ed on t he Web [W3C, 2003]. It describes all Web cont ent in ter ms of

resour ces (object locat ion), propert ies (aut hor, t itle, doma in), and associat ions

am ong resour ces, and is focused on providing compu ter -searcha ble meta da ta

for identifying and locating resources. It presu mes, however, th at event ua lly

all Web cont ent will be re-crea ted or at least a nn otat ed using RDF codes; th e

process an d sta nda rds for t his effort ar e still under development , and th ere is

concern th at au th ors of Web cont ent will find th e required ma rku p to enable

th e Sema nt ic Web vision t oo bur densome [Suter , 2003].

Topic Maps [TopicMaps.Org, 2001] are a related Web technology explicitly

designed t o represent a nd display associations a mong terms within a

docum ent, perm itting a cont ent au th or to link pairs or groups of concepts an d

to describe the na tu re of th e relationsh ips among the concepts. Topic ma ps

an d Seman tic Web technologies both require subst an tial ma rku p within a

Web docum ent, h owever, an d th e result ing concept link st ru ctu re is genera lly

unique for each docum ent . In t his dissert at ion, we focus on an alogies only,

an d present a general appr oach to representing th e concepts a nd r elations

th at compose an a na logy, providing a compact m ar kup str uctur e for


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represent ing a wide ran ge of an alogies, and expressing tha t st ru ctu re as a

linkattachmentto existing Web cont ent. We will show in Cha pter 5 th at t his

app roach allows for r epresen ta tions of an alogical concept r elationsh ips th at

can be tra nsform ed into severa l form s u sing XSL stylesheet s, including

ta bular st ru ctu res, graphical visualizat ions, an d other kn owledge

representations including Topic Maps.

As noted in [Dunn , 2002], th e effort to provide useful an d gener al cont ext an d

mea nin g ma rk up for Web docum ent s is an enormous t ask , still too difficult

for aver age user s. The resu lt of th is difficult y, along with t he decent ra lized

na tu re of th e Web, encour ages comm un ities of pra ctitioner s to tak e a simpler

appr oach, developing th eir own ontologies and m eta dat a [Sta ab, 2002].

2.2.5 The MARVIN System

The MARVIN syst em described in th is dissert at ion is designed to provide a

Web-based, common-language environment for describing and sharing

an alogies alrea dy conceived by tea chers, scient ists, journ alists, doctors, an d

other a na logy pra ctit ioner s. It is ther efore directed at comm un ities of

analogy users, especially, but not exclusively, educators. And while it is not

explicitly designed for int era ction with t he a na logical rea soning engines used


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in cognitive science an d compu ter science, the an alogies produced by th ose

systems can be represent ed and stored using th e MARVIN system. Thus

MARVIN ma y be us eful t o resear chers in compu ter science, cognitive science,

an d educat ion, by providing an environm ent for captu ring an d sha ring

interest ing or u nu sua l ana logies, wheth er produced by hu man s or by

machines.


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3. The Structure and Components of Analogies

What is considered to be analogy or analogical reasoning varies substantially

am ong pra ctitioners an d r esearchers in various fields, par ticular ly educat ion,

cognitive science, an d comput er science. While most resea rcher s agree th at

th e mapping ofrelational str uctur es is th e defining cha ra cterist ic of

an alogies, some u se th e term analogy more broadly to include simple

ma ppings of concepts or of similar propert ies. We include her e several

examples from th e an alogy literat ur e to illustr at e an d develop a term inology

for t he st ru ctu re an d components a na logies. We revisit t hese examples in

Chap ters 4, 5, and 6 to illust ra te repr esentat ion, visua lization, an d retr ieval,

respectively. Additional examples ma y be foun d on th e aut hor's website

[Foxwell, 2002] an d in th e Appendix.

The word an alogy as used in both general langua ge and in t he r esearch

litera tu re refers t o a perceived level of similar ity or sa men ess between t he

observed pr opert ies, concepts, a nd relat ions of two knowledge domain s, one

assu med to be known, the oth er par tially known or u nkn own.. A somewhat

rest rictive definition of an alogy by Gentn er in [Vosnia dou an d Or tony, 1989]


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th at empha sizes the importan ce of relations, sta tes:

...an analogy is a mapping of knowledge from one

doma in (the base) into another (th e ta rget), which

conveys th at a system of relat ions t ha t h olds among

the base objects also holds among the target

objects ...in int erpr eting a n a na logy, people seek to

put th e objects of the base in one-to-one

corr esponden ce with t he objects in t he t ar get...

Th e objects in t he a bove definition ma y be words , soun ds, images, pr ocesses,

or other symbols representing perceived concepts, and the relations among

them .

In t his dissertat ion, we define an an alogy as

a set of proposed similarity ma ppings between an

un kn own set of concepts a nd r elations (th e target

analog)and a known set of concepts an d r elations

(the source an alog), used for inst ru ctional or

explanatory purposes.

The definit ion ofconceptdepend s on t he cont ext for its use. A concept m ay be


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th e sour ce to th ose of the t ar get. U sing the Ruth erford an alogy, for example,

we can th en form (an d test ) a h ypoth esis tha t electr ical at tr action causes the

electr on t o orbit t he nu cleus in th e same way th at gravitat iona l att ra ction

causes th e planet t o revolve ar oun d th e sun [Wilson, et al., 2001]. Tha t is,

th e proposed ana logy preserves in the t ar get the h igher order causal

relat ionsh ip perceived in th e sour ce.

3.1 Ana logy Exam ples

In addit ion t o th e Ruth erford a na logy discussed above, we now exam ine

severa l additiona l exam ples to illustr at e th e types of an alogy str uctur es tha t

can occur . Note tha t our goal is to describe the a nalogy as its au thor presents

it, without at tempt ing to evaluate t he corr ectn ess or completeness of th e

proposed comparisons.

The Altoona List of Medical Ana logies [Ruh l, 2002] lists a simple a na logy

compa rin g the eye to a camer a in order to explain cert ain types of vision

problems to patients. It first establishes a ma pping of th e part s of th e eye to

th ose of a camera , and th en explains t ha t a cat ar act in th e eye is like a fogged

lens in a camera . Cont inuing with t he an alogy, it explains th at a detached

retina is like a cam era with wrinkled film. Once th is ana logy is established,

the eye doctor can continue, perhaps with additional analogies, explaining


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th e necessar y procedur es for tr eat ing th e condit ions. As presen ted, this

an alogy is pr imar ily a m apping of known concepts camera par ts an d th eir

presu ma bly un derstood functions, t o unfamiliar concepts eye anat omy a nd

vision impairm ents. Figure 3.1 shows a repr esenta tion of th is mapping.

Figur e 3.1. The Eye/Cam era Ana logy

Upon observing the moons of J upit er, Galileo form ed an an alogy between th e

solar system and t he J ovian system, proposing tha t t he relationship of th e

planets t o th e Sun wa s th e same a s th at of th e moons t o J upiter [Galileo,

Target Source

cornea

pupil

iris

retina

lens

aperture

diaphragm

film

cataract

detached retina

fogged lens

wrinkled film


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associated with a sour ce relation, an d a similar relational str ucture is

proposed in th e ta rget a na log, as sh own in F igur e 3.3.

Figure 3.3. The Rutherford Analogy

We see a similar t ype of structur e in a nother exam ple, th e Bohr Liquid Drop

model of Nu clear F ission [Koushia ppas a nd Cohen, 1999]. In t his an alogy,

we also see th e ma pping of a cau sal relat ionship when th e Coulomb

repu lsion bet ween t he t wo ha lves of a deform ed liquid drop is greater th an

th e surface tension between th e two ha lves tha t r elationsh ip causes th e drop

to split. The an alogy proposes th at th e same t ype of cau sal relat ionsh ip holds

Target Source

electrical

attraction

gravitational

attraction

electron

orbit

nucleus

planet

orbit

sun

causes causes


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for th e nu cleus tha t when th e Coulomb r epulsion between two ha lves of a

deformed nucleus exceeds the binding energy between the halves, this causes

th e nu cleus t o split. Moreover, t he form ula e for t he calculat ion of th e forces

ar e also proposed to be ana logous. In t his example, shown in Figure 3.4, we

see th e str uctu re of a r elation (Coulomb repu lsion greater th an surface

ten sion) mapped t o a concept (fission). This is similar to th e str uctu re we sa w

in t he Rut herford an alogy, but with th e directiona lity of th e higher order

relation reversed.

Figur e 3.4. The Bohr Liquid Drop Model of Nuclear Fission

Target Source

nuclear

fission

droplet

fission

binding energy

greater than

Coulomb

repulsion

Coulomb

repulsion

greater than

surface tension

causes causes


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Historical an alogies can be creat ed an d u sed by politicians an d jour na lists to

sway public opinion or t o suggest t he inevitability of a decision or course of

action. The September 11, 2001 at ta ck on th e World Trade Center a nd

Pent agon h as been compar ed to th e J apa nese Navys at ta ck on P earl Ha rbor

in 1941 [Cox, 2002]. Without evalu at ing th e merit s of th e ana logy, we see

th at th e ana logy can be part ially expressed as a higher order relational map.

The t ar get consists of an action (al Qaeda at ta cks WTC) cau sing (or implying

a des ired decision) an action (US declares wa r on t err orist s); th e sour ce is

similar ly stru ctu red (J apa nese Navy att acks Pear l Har bor) cau sing (or

implying a desired decision) an action (US declar es war on Ja pan ). Figur e

3.5 illustr at es this map.


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Figure 3.5. Historical an alogy September 11 = Pear l Ha rbor

We observe that although analogies may be quite elaborate or complex

[Art hu r, 2002], [Gra ndin , 2000], th ey may be decomposed int o a s ma ll

nu mber of str uctur ed component t ypes such a s th ose illustr at ed above with in

th e ta rget a na log, each of which ma ps t o an identically stru ctu red component

with in t he sour ce an alog. We now provide in wh at follows t he definitions of

each of five types of componen ts t ha t can a ppear in eith er t he source an alog

or ta rget an alog:

Definition 1 (Concept Set ): A ConceptSetC = {c1, c2, , cn} is a n ord ered set of

one or m ore concepts. Note that th e order of t h e ele m en t s l is t ed in a

Target Source

World Trade Center

attack

al Qaeda

Pearl Harbor

attack

Japanese Navy

Caused/justified Caused/justified

terrorists

declare war

United States

Japan

declare war

United States


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ConceptSet is significan t sin ce we ma p correspondin g concepts in t he t ar get

an d source. In t he Ruth erford an alogy, for example, a possible tar get

ConceptSet is {electr on, n ucleus}, an d a corr esponding s our ce ConceptSet is

{plan et, su n}.

Definition 2 (Prima ryRelat ionSt ru ctu re) : A PrimaryRelationStructure P =

(Ca ,R , Cb) associat es ConceptSet s Ca and Cb th rough zero or m ore r elat ions in

th e list of relat ions R . We say zero or more becau se r elat ions can be implied

in an an alogy ra th er th an explicitly na med.

In its m ost common form, a Pr imar yRelationSt ru ctu re consists of two

concept s associat ed by a single rela tion a s in ({plane t}, revolves, {su n}). Note

th at th ere ma y be multiple relations between t he ConceptSets (e.g., sun is

larger th an a planet, sun is m ore m assive tha n a planet, sun is hotter than a

planet, etc.), and t ha t t here m ay be more t ha n one element in each concept

set (th e concept set cont ainin g the sin gle element planet could be r eplaced by

the set {Mercury, Venu s, Earth, Mars, Ju piter, Sat urn, Uranus, N eptun e,

Pluto}, for example.

Definition 3 (ConceptToRelationStructure): A ConceptToRelationStructure is

a t uple of th e form (C, R , P ) wher e C is a ConceptSet ass ociat ed by zero or


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more relations in th e list R to the PrimaryRelationStructure P .

Definition 4 (RelationToConceptStructure): A RelationToConceptStructure is

a tuple of the form (P , R , C) where P is a PrimaryRelationStructure

ass ociat ed by zero or m ore r elations in t he list R to theConceptSet C.

A RelationToConceptStructure and a ConceptToRelationStructure associates

rela tions with concepts or concepts wit h relat ions, r espectively, depending on

th e directionality of th e higher order rela tion being specified. Higher order

rela tions can in clude cau salit y, implicat ion, an d sequencing, for exam ple. In

th e sour ce an alog of th e Rut her ford an alogy, for exa mple, we observe th e

ConceptToRelationSt ru ctu re gra vita tiona l a tt ra ction causes the planet t o

revolve ar oun d th e sun.

Definition 5 (RelationToRelationStructure): A RelationToRelationSt ru ctu re is

a t uple of th e form (P ,R ,P ) th at a ssociates a Pr imaryRelationSt ru ctu re to

an oth er P rima ryRelat ionSt ru ctu re th rough zero or m ore relat ions in list R .

There a re t hu s five types of maps, each of which m aps a str uctur e (e.g.,

Pr imaryRelat ionSt ru ctu re) in the ta rget an alog to a str uctur e of th e same

type in th e sour ce an alog: Figur es 3.6 (a) 3.6 (e) illust ra te t he five types of


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ma ps discussed a bove.

Consider th e ana logy th at compa res th e hum an circulat ory system to home

plum bing, used by physician s to explain car diovascular diseases t o medical

pat ients [Ruh l, 1999]. The tar get concepts heart , blood, and blood vessels are

ma pped to th e sour ce concepts pum p, water, and pipes , respectively, forming

a ConceptSetMap. When explaining congestive hear t failur e to a patient , the

physician first describes th e similar ity of the concepts of th e circulat ory

system t o th ose of a h ome plum bing system, implicitly using t he

ConceptSetMap, an d th en describes how the concepts ar e relat ed, creat ing

an d explaining relational stru ctu res and ma ps. For exam ple, th e physician

explains th at a clogged ar tery can cause th e blood to back up in to th e lungs

similar to the wa y tha t a clogged drain can cause the water to back up a nd

overflow, ther eby mappin g the cau sal r elat ion of th e sour ce an alog to th at of

th e ta rget. Thus, we see th at th is an alogy, as it is present ed by its auth or,

ma y be decomposed int o several ConceptSet Maps a nd a

Pr imar yRelationSt ru ctu reMaps. Note th at we can creat e mu ltiple

ConceptSet Maps, gr ouped by type of concept, a s sh own in Figur e 3.7.


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Target Source

c1

c2

c1

c2

r1, r

2,

c1

c2

r1, r

2,

c1

c2

c1

c2

r1, r

2,

c1

c2

r1, r

2,

Figur e 3.6 (c). RelationToConceptSt ru ctu reMa p

Target Source

c1

c2

c1

c2

r1, r2,

c1

c2

r1, r

2,

c1

c2

c1

c2

r1, r2,

c1

c2

r1, r

2,

Figur e 3.6 (d). ConceptToRelat ionSt ru ctu reMa p


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Target Source

c1

c2

r1, r2,

c1

c2

c1

c2

r1, r2,

c1

c2

r1, r2, r1, r

2,

c1

c2

r1, r

2,

c1

c2

c1

c2

r1, r

2,

c1

c2

Figure 3.6 (e). RelationToRelationStructureMap

Figure 3.7. Circulatory System Analogy

Target Source

heart

weak heart

causes

congestive heart failure

blood

blood vessels

blood pressure

plaque

pump

weak pump

causes

backup or overflow

water

pipes

water pressure

scale/deposits


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This an alogy and it s MARVIN visua lizat ion ma y be foun d on t he au th or's

Web site [Foxwell, 2002], along with add itiona l an alogy examples t ak en from

a va riet y of kn owledge doma ins.

3.2 The Limita tions of Ana logies

Analogies ar e like cha insa ws powerful and u seful tools th at can injur e you

if you misu se them . Like incorr ect ma ps, bad ana logies can lea d you awa y

from your dest ina tion or cau se you t o become en tir ely lost. The over-relian ce

on limited a na logical m odels can impa ir discovery an d t he form at ion of useful

hypothes es. The ear ly hist ory of science includes ma ny inst an ces of

misleading and inaccur at e ana logies. F or example, alchemists were

const ra ined in th eir underst an ding of ma tt er an d chemistr y by their

adh erence to ana logies between elements an d anima l or hu man

char acter istics [Gent ner a nd J eziorsky, 1993]. Kepler's reliance on a divinely

orda ined, geomet rically perfect m odel of the u niverse led him init ially to an

err oneous explan at ion of th e orbit s of th e planet s [Kepler, 1596]. Only when

he r elucta nt ly abandoned tha t model was h e able to conceive a more accur at e

explana tion of the plan ets orbit s.

Pr actitioners of an alogical explan at ions r ecognize the da ngers of misleadin g

an alogies. A doctor wh o frequ ent ly uses a na logies to explain m edical


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concepts n otes t ha t his pat ients can tr an sfer ina ppropriate kn owledge from

th e sour ce to th e ta rget of an an alogy, and suggests th at good a na logies

should be visual, should illustrate the necessary concepts, use a familiar

source, an d should be clear a nd sh ort [Ruh l, 1999]. Additiona lly, those

explaining or teaching an idea should consider whether an analogy is even

necessar y. Ana logies should be used primar ily when th e idea being tau ght is

new and is har d for th e learn er to un dersta nd. The ana logy's limita tions

should be discussed, an d dependence on th e an alogy reduced as th e learner

progresses in un dersta nding the tar get.

The u se of hist orical an alogies to guide decisions on th e u se of milita ry force

ha s been widely crit icized by his toria ns [Record, 2002]. The lessons of

hist ory can become obsolete or irr elevan t a s t ime pr ogresses a nd as p olitical

an d social cond itions differ from the sour ce event . The so-called Munich

an alogy tha t a ppeasement of aggression by totalitarian sta tes leads to

more aggression h as been invoked a s a n instr uctive model for milita ry a nd

foreign policy decisions by several countries, although the models success

an d a pplicability ha s been quest ioned [Record, 1998].

Analogy pra ctit ioner s do indeed r ecognize the limitat ions of ana logies, a nd

usu ally include a fina l recomm ended st ep in th e an alogical rea soning process


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to indicat e wher e th e an alogy break s down [Klein a nd Milligan , 2002],

[Glynn , 1991], alth ough t hey note th at th is should genera lly be done by th e

proposer of th e an alogy. This suggests t ha t th e process ofevaluating the

an alogy is separate from th e depiction of the an alogy itself, as we discuss in

Chapter 4.

Even th ose who suggest avoiding an alogies can ha ve difficult y explain ing

complex ideas with out usin g ana logy. For examp le, in crit icizing th e use of

an alogies in t eaching compu ter concepts, [Hala sz and Mora n, 1982] end u p

simply us ing different an alogies replacing a filing cabinet model of file

system s with a sup posedly more gener al an d less an alogous tr ee model.

Ir onically, even t he t itle of th eir pap er, An alogies Considered Harm ful ,is

pur posely ana logous t o th e title of a m ore famous pa per, GOTO St atem ent

Considered Harmful [Dijkstra, 1968].

In spite of th eir dangers and m isuses, an alogies remain an import an t a nd

widely used t ool for comm un icat ion a nd lear nin g. Our resea rch focuses on

how to represent and visualize some of the enormous range of human-created

an alogies. The fina l evalu at ion of an an alogys us efuln ess, however, is th e

responsibility of its pr oposer a nd u sers.


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4. The Repr esent at ion of Ana logies

As discussed in Cha pter 3, an a na logy ma y be described as a set of ma ps.

Ea ch ma p pairs t he concepts and r elationa l stru ctu res of a t ar get ana log to

corr esponding componen ts of a sour ce an alog. The corr esponden ce of concept

an d r elation components is determined by the an alogy aut hors perception of

similar ity of propert y, form, or fun ction, or by a hypothesis of similarity.

Our goal her e is to use t his char acter ization of an alogies to creat e an XML

content model [W3C, 2000] capable of describing a wide variety of analogies.

The design of such a m odel for an alogies must meet s everal genera l crit eria

an d mu st a lso meet t he needs of cont ent a ut hors who select or creat e

an alogies, cont ent reader s who learn using th e an alogies, and progra m

developers wh o use t he m odel to crea te n ew ways to use t he a na logies.

A representation is a symbolic surr ogate for an object th at facilita tes h um an

expression a bout th e object an d serves as a medium for compu ta tions wit h

th at object [Sowa, 2000]. Note tha t we distin guish between an analogy the

hu ma ns perception of sameness, a nd an analogy expression the


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represent at ion of th at perception. The represent at ion is necessar ily a limited

an d imperfect model of th e complet e hu ma n per ception, however. Our goal is

to define a symbolic repr esent at ion of an alogies th at capt ur es a wide scope

an d variety of hu ma n-conceived an alogies. Becau se th e term an alogy ha s a

broad ra nge of interpr etat ions, our represent at ion mu st r eflect both t he

comm on usa ge of th e ter m, as well as t he m ore form al us age in r esear ch

fields su ch a s Cognitive Science, Artificial In telligence, an d E ducat ion.

Specifically, it mu st be able t o express both simple similar ity of propert ies

an d concepts, and m ust preserve relat iona l stru ctu res th at ar e the core of

an alogy perceptions [Gent ner , 1983]. Becau se ana logies are rem embered,

reused, a nd extended over time t o genera te n ew inferences [Hofsta dter,

2001][Kean e an d Costello, 2001], our repr esent at ion m ust also be exten dable.

Tha t is, it mu st be flexible enough t o perm it t he a ddition of new a na logy

components as needed by the author, user, or developer.

Aut hors of instr uctiona l or explan at ory cont ent frequent ly use an alogies t o

assist the student or reader in un derst an ding new ideas. An an alogy

representation must therefore be compact yet expressive enough to allow the

au th or t o record t he essential components of th e ana logy, and must also

perm it some indication of th e validity of th e proposed compa risons. In our

an alogy expression, we t herefore u se simple words an d sh ort phr ases a s


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expression prim itives along with expressions th at describe th e str uctu re of

th e relations am ong th e primitives. Becau se we us e XML to define an alogy

expressions, au th ors can u se XML editors or other tools to assist t hem in

producing valid XML files. Cha pter 7 describes an an alogy express ion editor

creat ed for th is pur pose.

We wan t to provide access t o ana logy express ions usin g fam iliar , Web-based

tools and techn ologies such as br owsers. Moreover, we need to separ at e the

representation of the structure of the analogy expression from its

visua lizat ion. XML was designed specifically for th is pur pose [W3C, 2002],

allowing m ult iple form s of visua lizat ion ba sed on a comm on repr esent at ion

model.

Researchers need programmatic access to knowledge expressions in order to

st ore, retr ieve, an d man ipulat e them . The use of XML ena bles developers to

access analogy expressions using standard, Web programming tools and

met hods such a s XML par sers , XSLT stylesheet s [W3C, 2001] an d pr ocessors,

J ava program s, HTML, an d SVG [W3C, 2001] to visua lize th ese expressions

in a var iety of form s. Our represent at ion is implemented as a n XML DTD

(Docum ent Type Definition), permit tin g a compa ct, comm on form at for

describing, searching, and transforming analogy expressions.


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Term inology used t o describe the component s an d str uctu re of an alogies

should be compa ct yet descript ive. In designing the DTD we st ress th e

distinction between th e tar get an alog components an d th e sour ce an alog

components by giving th em separ at e but similar n am es. We also observe

th at in order to map only identical an alogy str uctur es, we must enu mera te

th e possible stru ctu res r at her t ha n u sing more compact or recursive element

definit ions t ha t would allow ma pping of un like componen ts .

Knowledge representations defined using XML may be expressed using either

DTDs or schema s. Like XML DTDs, XML Schem as a re u sed to define th e

str uctu re, cont ent , an d seman tics of XML docum ent s [W3C, 2001]. Schema s

provide for str ong dat a t yping and va lidation, explicit cardin ality cont rols,

an d const ra ints on at tr ibute values [W3C, 2001]. But for r epresenta tions

th at do not r equire str ong dat a t yping or cardina lity cont rols, DTDs ar e

sufficient a nd sim pler [Mertz, 2001]. Tools for creat ing, valida tin g, an d

tr an sform ing DTD-based XML files are m at ur e an d widely ava ilable [Cover,

2002] while th e XML schem a st an dar d an d tools ar e still evolving [Gar shol,

2002]. Becau se th e an alogy express ions d iscuss ed in t his docum ent consist

exclusively a r elatively small nu mber of text-based element s with an y

numberof repea ted componen ts , a DTD was developed. XML ana logy

expressions ba sed on our DTD may be t ra nsform ed as needed into oth er


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form s usin g XSL stylesheet s or oth er t ools, including t ra nsform ing th e DTD

int o an XML schema