Using a Logic Programming Framework to Control Database Query Dialogues in Natural Language

Database Query Dialogues in Natural Language

LuisQuintano

∗

,

IreneRodrigues

†

lj qs .uevora.pt iprdi.uevora.pt

*ServiçodeComputação

†

DepartamentodeInformáti a UniversidadedeÉvora,Portugal

Abstra t. Wepresentanaturallanguagequestion/answeringsystemto

interfa etheUniversityofÉvoradatabasesthatuses lari ationdialogs

inorderto larifyuserquestions.Itwasdevelopedinanintegratedlogi

programmingframework, basedon onstraint logi programmingusing

theGnuProlog(- x)language[2,11℄andtheISCOframework[1℄.Theuse

ofthisLPframeworkallowstheintegrationofProlog-likeinferen e

me h-anismswith lasses and inheritan e, onstraint solving algorithmsand

providesthe onne tionwithrelationaldatabases,su hasPostgreSQL.

Thissystem fo us onthequestions' pragmati analysis,to handle

am-biguity,and onane ient dialogueme hanism,whi h isable to pla e

relevantquestionsto larifytheuserintentionsinastraightforward

man-ner.Proper Nounsresolution and the pp-atta hmentproblem are also

handled.

Thispaperbrieypresentsthisinnovativesystemfo usingonitsability

to orre tlydeterminetheuserintentionthroughitsdialogue apability.

Keywords: Natural Language, Logi Programming,Information Systems,

DialogManagement,Databases

1 Overview

IIS-UE(UniversidadedeÉvoraIntegratedInformationSystem)gathersallkinds

ofinformation,relevantforstudents(enrolled ourses,grades, lasssummaries,

et .),for tea hers( oursesinformation,proje ts,studentsevaluation,personal

data,et .)andsta(datamanagement,statisti s,personaldata,et .).Several

appli ations were built around IIS-UE to deliver information to the s hool

ommunity, but sometimes that's not enough. To use these appli ations one

mustknowhowtheywork.Astudentmayknowwhatinformationhewantsbut

hedoesn'tknowhowtogetitfrom theexistentappli ations.

Tosolvetheseproblemsanaturallanguagequeryingappli ation(NL-Ue)was

developedoverIIS-UE 1

. Its pra ti alaim is to give to ours hool ommunity

aneasywayforretrievingstoredinformation[3℄[13℄.

1

wh-thisdata,NLmustmaptheuserquestiontoadatabasequerylanguageso that

theresultingpragmati interpretations anbeevaluated inthedatabases.

NL-Ue implementation is basedon onstraint logi programmingusing the

GnuProlog(- x)language[11℄andtheISCOframework[1℄.

The system ar hite ture is simple and module-based. The system has ve

distin t modules whi h are onne ted through well dened API's [Fig. 1℄. A

moredetaileddes riptionof thesystem anbefoundin [18℄[17℄.

Sintatic

Module

Semantic

Module

Pragmatic

Module

Evaluation

Module

Dialog

Manager

Fig.1.NL-UeAr hite ture

Inthispaperwedis usshowpragmati interpretationishandledbyNL-Ue,

but thefo us will be onits dialog apabilities and how this system is able to

larifytheuserintentionswithee tiveandpre isequestions.

Next(se tion2) somerelatedwork ispresentedto enhan ethis appli ation

relevan eonthedialogueandnaturallanguagedevelopment ontext.

Inse tion 3 thepragmati interpreteris des ribedalong with the strategy

thatwasusedtogeneratethepragmati evaluationrules(whi h ontroltheow

andthebehaviourofthepragmati interpreter).

Then (se tion 4) the dialogue me hanism is shown re urring to pra ti al

examples and nally some on lusions and future work are presented (se tion

6).

2 Related work

Some systems an be found along the last years that tou h the problem of

glishnaturallanguagesenten estoSQLwithgraphanalysiste hniques.Tokens

(manually dened - low portability) represent dire tly attributes of database

elements and relations between them. Pre ise uses a tokenizer to identify all

possible omplete tokenizations of the question, onverting them to SQL with

asimple synta ti parser. The systemis limitedto arestri tedset of types of

questions.Inturn,othersystemsasAndroutsopoulos'Masque/SQLusesan

in-termediate representationbefore generating theSQL. This approa h is similar

to ourown, although dierent in thefollowed methodology. Androutsopoulos'

Masque/SQL[5℄[6℄systemisbasedonthepreviousMasque[4℄implementation,

whi hinturn wasbasedonFernandoPereira'sCHAT-80[22℄.

While Masque maps an english natural language question into Prolog to

query a de larative database, Masque/SQL extends it by interfa ing dire tly

withrelationaldatabases.Todothat,Masque/SQLneedssomemeta-datawhi h

hastobere onguredea htimethereisa hangeofworkingdomain.Tomanage

thismeta-dataMasque/SQLhasadomaineditorwhere:(1)thedomainentities

(represented on the databases) are des ribed (2) the set of expe ted words

to appear in the questions and it's logi al meaning - Prolog predi ates - are

des ribes(3)the onne tionbetweenea h predi ateandadatabasetable,view

or sele t is expli itly represented. On e again, portability is one of the main

problemsofthissystem.

AlthoughbenetingfromtheRDBMSSQLoptimization,Masque/SQLmay

sometimes generateredundantSQLqueries,de reasingit'se ien y.

Othersystemsuseexternalintegrationtoolsfora essinginformation

repos-itories. Oneofthemis Katz'sSTART [16℄whi huses Omnibase[15℄,asystem

for heterogeneousdatabase a ess whi h uses naturallanguageannotations to

des ribethedataand thekindofquestionsthat anbeanswered.Thissystem

usestheobje t-property-valuedatamodelandonlyworksfordire t questions

aboutobje tproperties.Theseannotationsaremanuallybuiltwhi hmakesthe

system'sportabilitydi ult.

While the mentioned appli ations are simple question/answering systems

that donot identifywrong interpretations, NL-Ue intendsto goastep further

addinga lari ationdialog apability.Clari ationdialogue theorywasvastly

analyzedby severalauthors[12℄[8℄and someworks anbefound where

lari- ationisapplied toopendomain ormorenarroweddomainquestionanswering

systems[21℄.

NL-Ue an beseenasaquestion/answeringdialoguesystem,identifying

er-roneousinterpretationsbymeansofa lari ationdialoguewiththeuser.

3 Pragmati interpretation

synta ti andsemanti analysis,theresultingrst-orderlogi predi ate(LPO)

representation(asseeninFig.2)willrea hasaninputforthepragmati module.

Contextualinformationisaddedat thisstage.

LPO predicates

ISCO expressions

SIIUE

repositories

Control

rules

ISCO

db schema

ISCO

acess layer

Fig.2.Pragmati Interpreter

NL-Ue appli ation ontext is not only the IIS-UE data but also its

stru -ture.This informationis added tothe interpretationme hanism throughaset

ofpragmati ontrolrules. Togeneratetheserules,NL-UeusesISCO, alogi al

tooland development framework that enables relationaldatabases hema

rep-resentationandfulla esstothestoreddata.Thisgenerationisautomati and

must be donepreviously so that the rules are available at interpretation time

(runtime).

3.1 Database representation/a ess framework

ISCO[1℄isalogi -baseddevelopmentframeworkwithitsrootsintheGnuProlog

language[11℄andisbeingdevelopedinUniversidadedeÉvoraComputer

Engi-neeringdepartment.ItsuseinNL-Ue'spragmati interpretationaddsthesystem

theabilitytointernallyrepresentanda esstheIIS-UErelationaldatabasesin

alogi -basedenvironment.

Figs. 3 and 4 show a fragment of one of IIS-UE's relational databases in

entity/relationandSQLrepresentation.Itpresentsthea tionoftea hing

(le - iona)whi hrelatesatea her(individuo),a ourse(dis iplina)anda urri ulum

( urso).

Thepro essofpragmati interpretationneedstoknowtheseandother

person

course

curriculum

teaches

id

name

code

year

name

id

code

name

id

1

1

1

P

M

N

Fig.3.ERfragmentrepresentation

Theequivalent ISCO representation(Fig. 5) mapsea h relation/tableto a

lassthat anthenbea essedthroughISCOpredi ates.AlthoughNL-Ueonly

usessele tpredi ates,ISCOalsosupportsinserts,updates anddeletes[1℄.

Thisde larativedes riptionof therelationaldatabases hemasis

automati- allygenerated.Basedonthis lassdes ription,ISCO(also)automati ally

gen-eratespredi atestoa essthestoreddata.Theseissuesin reasesde isivelythe

portabilitylevelof NL-Ue.

Thegoalofthepragmati interpreteristomaptheLPOpredi atestothese

ISCOgoalssothatIIS-UEdatabases anbedire tly queried.Forexample:

person(PERSON, PERSON_NAME, _),

tea hes(COURSE, 2005, PERSON, _),

ourse(333, COURSE_NAME, _).

olle tsallpersons (tea hers)that tea h the ourse withid 333in the year

of2005.

NL-Uealso benetstheadvantagesof the ontextualbran h ofGnuProlog:

GnuProlog- x. This is the ontext-based variant of the well known

GnuPro-log languagewhi h besides all the base features, also enables ontextual goal

evaluation[2℄.WhileGnuProloghasaatpredi atenamespa e,it's ontextual

variant overridesthis problem by dening evaluation units.Ea h unit has it's

ownnamespa ewhi hmakespossibletohavethesamegoaldenitionindistin t

units.Contextualgoals an be alled from other units by meansof anexpli it

ontextual all.

Thisfeatureis essentialfor ontrollingthepragmati interpretationpro ess

be ausethere arenumerousinterpretation possibilitiesto onsider.Contextual

evaluation will restri t these possibilities making thepro ess lighter and more

e ient.

3.2 Control rules

"id" integer default (nextval ('is__entity_id'))

primary key,

"name" text,

"gender" integer referen es "gender" ("id"));

reate table "tea hes" (

" ourse" integer referen es " ourse" ("id"),

"year" integer,

"tea her" integer referen es "person" ("id"),

" urri ulum" integer referen es " urri ulum" ("id"));

reate table " ourse" (

"id" integer default (nextval ('is__ ourse_id'))

primary key,

"name" text,

" ode" text);

reate table " urri ulm" (

"id" integer default (nextval ('is__ urri ulum_id'))

primary key,

" ode" integer unique,

"name" text,);

Fig.4.SQLfragmentrepresentation

information to theuserquestionanalysis.ISCO lasses,aswe'vealreadyseen,

represententities and/orrelations.Ea honeofthemwillhavedistin tkindsof

ontrolrules.

Ea hruleabdu tsaset ofISCO goalsandin rementallybuilds theev

alua-tion ontextoftheremainingpragmati interpretation.Thenalsetofabdu ted

ISCO goalswillthen beevaluated sothat asolution anbefound fortheuser

senten e/question.Oneevaluationunit isgeneratedforea h lass(IIS-UE

rela-tion).Withintheseunits vedistin ttypesof rules anbefound:

 entity a ess rules - one rule is generated so that the stored data an be

a essedbyNL-Ue for lassesthatrepresententities 2

 propernounrules-validatetheexisten eofentitieswithaspe i namein

aparti ular ontextofevaluation[Fig.7℄

 numberrules-identify entitiesthat haveapropertywithaspe i number

 relation rules - These rules establish wider onne tions between referents.

Typi ally they are the most numerous and they are the main responsible

forthe omplexityofpragmati interpretation.Theyarealsoresponsiblefor

xingthepp-atta hmentproblem[19℄[9℄.Therearefourkindsofsu hrules:

•

Selfrelationrules-generatedonlyforentities,theyintendtorelatetwo referentsthatreferthesametypeofentity.

id: serial. key.

name: text.

gender: gender.id.

lass tea hes.

ourse: ourse.id. index.

year: int. index.

tea her: person.id. index.

ourse: ourse.id. index.

lass ourse.

id: serial. key.

name: text.

ode: text.

lass urri ulum.

id: serial. key.

ode: int. unique.

name: text.

Fig.5.ISCOfragmentrepresentation

for ea h CLASS

build evaluation unit with:

entity a ess rules,

proper nouns rules,

number rules,

relation rules

external domain rules

end build

end for

Fig.6.Controlrulesgenerationalgorithm

•

Entity relation rules - For ea h entity (only), these rules asso iate its primary key withea h oneof its domain restri tedarguments (foreign

keys).

•

Argumentrelation rules -Generates arule for ea h pair ofdomain re-stri tedarguments, onsideringtheirrelationasapossibleinterpretation.

•

External relationrules- Establishwiderrelations notdire tly withina lassbutmentioningother lassesthatrefertherstoneasforeignkey.

 Externaldomainrules-Forea hargumentof lassC1thatrefersa lassC2

asforeignkey, aset ofrulesforC1interpretationis addedtoitsevaluation

unit.

These rulesdenition is generi (for anyappli ation ontext),notonly forthe

deter-referentstothepossibleinstantiations.

name(A, PATTERN, [℄)

:- he k_ lass_domain(person) :> item(A),

he k_domain(text, PATTERN) :> item(A),

add_to_ ontext([person(A, _, _)℄).

olle tsinA alltea herswhi hhave thestringPATTERNinitsname.This

ruleisappliedinsenten eslike:thetea her John...

Fig.7.Controlruleexampleforpropernouns: lassperson

4 Clari ation me hanism

The senten e pragmati interpretation may lead to multiple results, ree ting

thesenten eambiguityand/orthedatabasestru tureambiguity.Inthis asethe

systemneedsto larifytheuserintentionsbymeansofa( lari ation)dialog.

ToshowNL-Ue's usage andfo uson it'sdialogue/ lari ation me hanism,

let'sseeanexamplequestion 3

[Fig.8℄.

Quedo entes le ionamadis iplinade Gestão?

(Whi h tea hers le ture the Management ourse?)

Fig.8.Examplequestion

Thissenten ehasonesemanti interpretationandonepragmati

interpreta-tion [Fig.9℄whi h asso iatesallpossible tea hersof somemanagement ourse

withallpossiblemanagement ourses(thatexist withinIIS-UE) 4

.

Referent A is instantiated with all possible tea hers (that exist in IIS-UE)

be ausenootherrestri tionwasmadetoit,whilereferentBisinstantiatedwith

all oursesthat ontainthenameManagement.

Theevaluationmodulewilldeneasetofpossibleanswers.Ifnoambiguityis

foundthedialoguemanagerwilldire tlyanswertheuserwiththe(only)answer

3

Thesystemwasdevelopedfortheportugueselanguagebutquestionswillbe

trans-latedtoenglishforeasierunderstanding

4

this senten ehas more pragmati interpretations, some ofthem a little bit

tea her(A), tea hes(A, B), ourse(B), rel(B, C), name('Management',C) Pragmati instantiation: A in [47105..47787℄ - male/female - plural B in [188:194:247:259:346:352:486: 550:5 58:8 35:10 53:1 108:1 210:1 270: 1287:1293:1320:1355:1412:141 4:142 3..1 424:1 466: 1487: 1657: 1659 : 1688..1689:1752:1781:1849:18 68:19 08:1 999:2 010: 2069: 2151: 2191 : 2249..2252:2441:2479:2525:25 34:25 88:2 598:2 698: 2754: 3107: 3134 : 3152:3161:3189:3257:3331:339 3:350 2:35 84:35 93:3 696:3 865:3 886: 3918:3928:4013℄ - female - singular C = B

Fig.9.Semanti /Pragmati interpretationresults

found.Butifmorethanonepossibleanswerisfound,thenthedialoguemanager

entersina lari ationpro ess[Fig.10℄.

while not a terminal ondition:

olle ts properties for ea h referent

pro eeds with heuristi evaluation

hooses the best property

questions the user / re eives the answer

restrains solutions to the question result

end while

Fig.10.DialogueManager: lari ationalgorithm

Aterminal onditionwillberea hedifonlyone answerisa hieved.

Afterthe evaluation pro ess,possible solutionswill begroupedby referent

(inourexamplewehavethree,referringtotea hers,Managementand ourse).

Thisgroupingisdoneto olle tthepropertiesofea hreferent.Thebestproperty

willbe hosento makeanewquestion.

Inwh-questions(whi h is the ase)thereferentwhi h is target of theuser

question(inthis ase,referentA)isex ludedfromthesetofreferentstoanalyze.

It makesnosenseto makequestions referringto what theuser wantsto know.

Forea h (relevant)referent,dierentkindsofpropertieswill be olle ted:

- if a referent refersto a student, it may have properties as its name, its

studentnumber,itsgender,et .

 Indire tproperties:identiesasreferentproperties,attributesof lassesthat

referthereferent lassasaforeignkey-ifatea herle turesaspe i ourse,

thenthe a tof tea hing it may be onsidered asaproperty ofthe tea her

referent.

name('Control Management'), name('Computer Management'),

name('Personal Management'), belongs_to('So iology department'),

belongs_to('E onomi s department'), le tured_hours(3.5),

le tured_to(Computer Engineering urri ulum'), et .

Fig.11.PropertiesSet

After olle tingthereferentproperties[Fig.11℄ theywillbeevaluated with

theaimtondthebetterone.Heuristi evaluationisusedtoweightthequality

of ea h property found. The evaluation of a property is a linear sumof three

distin t riteria[Fig.12℄.

∀

properties p: weight(p) = w1(p) + w2(p) + w3(p) where

 w1(p) - evaluates the ability that the property has to split equally the

referentssetbasedonthetotalnumberofreferents(Rt)andthenumber

ofthosewhi hhavetheproperty(Rp):w1(p) = 1 - Rp/Rt.

 w2(p)-evaluatesthesemanti potentialofthepropertypreferringtextual

(T(p))tonumeri ones:If T(p) then w2(p) = 0.5 else w2(p) = 0.1.

 w3(p)-evaluatestheprobabilitythattheuserknowsthiskindofproperty,

preferringmoregeneri properties.Itassumestheuserhasmoreknowledge

of on eptual properties(C(p)),thanspe i ones:If C(p) then w3(p)

= 0.5 else w3(p) = 0.1.

Fig.12.Propertiesweight heuristi s

Afterthepropertiesevaluation,ea honeofthemisasso iatedwithaspe i

weightthat ree tsitspotentialtogeneratearelevantquestion[Fig.13℄. 5

For example the properties belongs_to('Management department') and

belongs_to('Mathemati s department') are semanti ally equal (weight 2)

5

in re-weight(le tured_hours(2), 0.987), weight(belongs_to('E onomi s

department'), 0.696), weight(belongs_to(Mathemati s department'),

0.696), et .

Fig.13.Propertiesevaluation

and referto the same on ept - department (weight 3) but the rst one asa

greater ability to split the referents set whi h gives it a larger weight. While

the property belongs_to('Management department') is semanti ally ri her

(weight2), the property le tured_hours(2)has agreater ability to split the

referentsets,makingbothpropertiestohavethesameweight.

Having weighted the quality of the properties, they are grouped by type

(belongs_to, le tured_hours, name, le tured_to, et .).Ea h group

in-herits the best weight of its members and the best group is hosen to build

the question (and the possible answers). Properties are grouped be ause the

lari ation pro ess onsists of questions with alternative answers, being the

alternativestheelementsofthe hosengroup.Ifthenumberof alternatives

ex- eeds a previously determined limit, this property is ignored and the system

re ursivelygetsthenextone.Inourexamplethe hosengroupis omposedby

allthebelongs_toproperties[Fig.15℄.

"Management ourse" belongs to:

1) Edu ation department 2) So iology department 3) Agri ultural department 4) Management department USER: 3 Fig.14.Clari ation#1

Besidesspe ifyingoneofthepossiblealternatives,theuserhasonlyoneother

possibility whi h is to say ? meaningI don'tknow. In this asethesystem

willre ursivelygetthenextbestproperty and makeanewquestion.

If the user answers one of the alternatives (1-6) the system will restrain

and re-evaluate the possible solutions(a ording to the usersanswer) and the

lari ationalgorithmreturnstoitsbeginning.After re-evaluatingthepossible

solutions,the hosenpropertyreferredto thenumberofle tured hours.Asthe

user didn't knew the answer, thesystem re urred to the nextbest property

1) 2 hours/week

2) 3 hours/week

USER: ?

"Management ourse" is le tured to:

1) Biophysi s urri ulum

2) Agri ulture Engineering urri ulum

3) Computer Engineering urri ulum

USER: 2

Answer

Tea her: Fran is o João Santos Silva

Course: Water resour es management

Fig.15.Clari ation#2

5 Evaluation

A dialogue/ lari ation systemto queryintegrated databasesin Natural

Lan-guageshould beevaluatedfromdierentaspe ts:

 Portugueselanguage overage:Thesynta ti analysisisdoneusingVISL[7℄

whi hisoneofthebestportuguesefullparsersforportuguese.Thesemanti

overageis restraintby thedatabasesvo abulary(tables, attributesnames

and entity names) and a synonym di tionary that an beupdated by the

users.Thedialoguesystemis abletoanswerYes/Noand Whquestions.

 The pertinen e of the userquestion pragmati interpretation: This aspe t

is ree ted in the system lari ation dialogues and answers. Our system

enablestheinferen eofsomerelationsthat learlynaturallanguageforbids

dueto the rulesfor pragmati interpretation of pp-senten es. However,we

anstate that normally this does not onstitutes aproblem forour users,

sin eontheaveragetheyareabletoobtaintheiranswerin3dialoguesteps.

 Theheuristi fun tion qualityfor hoosing thequestionto larifyuser

sen-ten e:Theevaluationofthisaspe tmustbedoneusingtheresultsofagroup

test,whi hisn'tdoneyet.Probablytheuseofsomeotherheuristi smaybe

moree ient.Thisisanaspe tthat mustbeanalyzedinthefuture.

fun tions lead the system in 70%of the questions to rea h an answer after 3

questions/answersorless.

Theresultspresentedintable1refertotestsmadewiththeIIS-UEmain

re-lationalrepository,whi h ontains159tableswithanaverageof2281rows/table.

The previously built ontrol rules as end to thenumber of 1400 whi h makes

an average of 8 rules for ea h lass. Results are based on distin t senten es 6

representativeofpossiblequestionstoNL-Ue.Nospe i treatmentis givento

questionsaskedmultiple timeswithslightvariationsinitsstru ture. 7

Senten e ContextCPUTimeRealTimeClari ation

DoesMarytea hesDatabases?

no 0.878 4.970

yes

yes 0.640 3.285

DoesMaryHigginstea hesthe

Informati s urri ulum?

no 0.910 5.111

no

yes 0.488 3.164

Whi htea herstea hthe

Management ourse?

no 5.864 22.324

yes

yes 3.544 14.225

Whi harethePhysi stea hers?

no 6.321 20.970

yes

yes 4.216 12.960

Table 1.Results

Timesshown(inse onds)refertothetimethesystemtakesfromtheinputof

theusertotheanswer(ortotherst lari ationquestion 8

).Furtherevaluation

must be done onsidering realtests be auseits onditionedto the lari ation

pro essandtotheuserssubje tivity.

Ea hsenten ewas testedwith andwithout ontextualevaluation. Its use

-with GnuProlog- x- isrelevant in the pragmati interpretation module,

mini-mizingits omplexitybyredu ingthesear hspa e.Itleadstoagainofe ien y

in theorderof80%in more omplexsenten esand 50%in moresimplerones

- putime.Yes/noquestions(se ondandthird)havealower pupro essingtime

thanwh.Clari ationwasneededin threeofthesenten es.

6

senten esinenglishmaynotpresentthesamestru tureor omplexitythanin

por-tuguese

7

possiblefuturedevelopmentwhi hwouldrequirethere ordingofthesenten es

anal-ysisforfuturereferen e

8

Thisin ludes: synta ti andsemanti analysis;the pragmati interpretation (ea h

senten emayhavemorethanoneinterpretation)andnallytheevaluationofea h

senten einterpretation.Duringthistheevaluation,thedis oursereferentsthatare

onstraintvariablesrestrainedtoasetofdatabaseentities,arevalidatedbytesting

the truth value of the senten e onditions, that are database relations. This way,

thepro esstimeofaquerysenten ewilldependonthenumberofdatabaseentities

Oursystemaimstobeanaturallanguageinterpretationsystemthatusesspe i

toolsforin reasinge ien yandgettingresultsinrealtime.

Thispapershowshowpragmati interpretationof omplexsenten es anbe

handled through a set of ontextual ontrol rules whi h guide the pro ess of

interpretation, in reasingit'se ien y andwithoutthelossofanyresults.

The use of the ISCO development framework gives NL-Ue a high level of

portabilityin a essingandqueryingdierentsets ofdatabases.

The ontextualevaluationstrategy(availablethroughtheuseof

Gnuprolog- x) is applied in the ontrol rule generation and usage, making thepragmati

evaluationpro esspra ti alandmoree ienton ethesear hspa eissmaller.

NL-Ueisaquestion/answeringsystem apableofa lari ationdialogwhen

needed.Itisabletoidentify theusersneedsandtoextra tthedesired

informa-tionfromrelationaldatabases.

The use of a dedi ated development framework and its ability to des ribe

and a ess distin t data sour es in ahomogeneous wayin reased the systems

portabilityrate.

Contextualevaluationgivespragmati interpretationamorelinearand

sim-pletreatmentde reasingits omputational omplexity,whi h anbede isivein

systemswithlargeinformationrepositories.

NL-Ue supports yes/no and wh-questions. Besides, it in ludes a

lari a-tion me hanismin whi h thesystem dialogueswith the userwhen thedesired

informationisambiguous.

The lari ation/dialogue module uses referent properties to nd relevant

questions trying to rea h an answer the faster it ans. Heuristi evaluation is

usedto ensurethequalityofthequestions.

Havingdevelopedthe oreme hanism,thenextstepswillbeto onrmthe

orre tnessofthefollowedmethodology.Forthat,thesystemmustbeevaluated

onsidering:

 itsusefulness(fortheusers)

 the orre tnessofpragmati rulesgeneration

 thetypesofquestionstreated

 theheuristi fun tion quality

Thesystem will be available to thepubli throughUniversidade deÉvora:

http://www.uevora.pt

Referen es

Palamidessi, editor, ICLP, volume 2916 of Le ture Notes in Computer S ien e,

pages128147.Springer,2003.

3. SalvadorPintoAbreu. ALogi -basedInformationSystem.InEnri oPontelliand

Vitor Santos-Costa, editors, 2

nd

International Workshop on Pra ti al Aspe ts of

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