Similarity Assessment through blocking and affordance assignment in Textual CBR

0.14 MB·English

by  R. Rajendra Prasath

Similarity Assessment through blocking and affordance
assignment in Textual CBR
R.RajendraPrasath⋆ and PinarO¨ztu¨rk
DepartmentofComputerandInformationScience(IDI)
NorwegianUniversityofScienceandTechnology(NTNU),
3 SemSælandsVei7-9,NO-7491,Trondheim,Norway,
1 {rajendra,pinar}@idi.ntnu.no,
0 http://www.idi.ntnu.no/˜rajendra;
2
http://www.idi.ntnu.no/people/pinar;

n
a
J

4 Abstract. It has been conceived that children learn new objects through their
affordances,thatis,theactionsthatcanbetakenonthem.Wesuggestthatweb

] pagesalsohaveaffordancesdefinedintermsoftheusers’informationneedthey
R
meet.Anassumptionoftheproposedapproachisthatdifferentpartsofatextmay
I
notbeequallyimportant/relevanttoagivenquery.Judgmentontherelevance
.
s ofawebdocumentrequires,therefore,athoroughlookintoitsparts,ratherthan
c
treating it as a monolithic content. Wepropose a method to extract and assign
[
affordancestotextsandthenusetheseaffordancestoretrievethecorresponding

1 web pages. The overall approach presented in the paper relies on case-based
v representationsthatbridgethequeriestotheaffordancesofwebdocuments.We
1
testedourmethodonthetourismdomainandtheresultsarepromising.
0
7
0
1 Introduction
.
1
0
3 WorldWideWeb(WWW)isamassivelydistributedanddecentralizedmediumfor
1 informationandservices,andalsooneofthemostegalitariandiscoveriesofmankindin
: moderntimes.However,theuseofthewebtechnologytoitsmaximumpossibleextent
v
i requires developmentof flexible and effective searching approaches. To this end, we
X
propose an approach in which the web can be searched through case representations
r that capture plausible connections between users’ queries and affordances of web
a
documents.
This work presents an approach to documentretrieval in the tourism domain, yet
theunderlyingresearchobjectiveistodevelopamethodforexplicationandcaptureof
theaffordanceofdocumentsthatareavailableontheWWW.Gibson[3]introducedthe
termaffordancetorefertotheopportunitiesforactionprovidedbyacertainobject.We
suggestthatwebdocumentsshouldsimilarlyhaveaffordancesthatrefertouse-purposes
ofdocuments.Themeaningofacontenttothequeryofauserliesinitsaffordance.The
questiontransformsthenintohowtheaffordanceofdocumentsareconveyedintextual
formatandcanbeextractedandrepresentedinareusableway.
⋆This work was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship
Programme.
Acentralideaofthepresentedmethodisthatadocumentmaycontaininformation
that matches different queries, each of which corresponds to an affordance. For
example, a web document about New Delhi may provide information about the non-
vegetarianrestaurants, the bazaars, as well as the transportationwithin the city while
themainfocusmaybeonshopping.Thiswouldmeanthatmostofthecontentrevolves
aroundbazaars,shoppingmalls,andtheshoppingnorms(i.e.,whether/wheretobargain
andwhen/wherenot).ThemainstrategyoftheapproachistodividetheWebpagesinto
textsegments,determineaffordanceofeachandusethesetodeterminetheinformation
needs the documents afford. This may be considered as a special type of tagging
technique,intheclassicalnaturallanguageprocessingterminology.
Theuse ofCase BasedReasoning(CBR) inwebcontexthasattractedresearchers
for a while. For example, Limthan et al. [6] applied CBR in order to compose a
complex web service from heterogeneous web services residing in different parts of
the web, and [2] in order to search and select web services. Ha [4] investigated how
the webusagedata canbe usedto discovernavigationpatternswhich,inturn,can be
used to predict the user behavior. In this work, a web documenthas a corresponding
case representation capturing information that bridges queries to documents through
affordances.Queryexperiencesareused to adjustthe affordancesof documents.This
provides search-useful and re-usable information for future web searches. The key
rationale of the proposedapproachis based on two assumptions:(i) a web document
embraces a number of text blocks each of which can be connected to one or several
affordances,(ii)thewebcanbe searchedCBR-wise andrelevanceofadocumentcan
bejudgedonthebasisofitsaffordancealignmentwiththecurrentquerycase.Figure.1
illustratestheproposedapproachincomparisonwiththewebsearch.
Web Search Query / New Case WebCBR
[proposed approach]

○ Problem
○ Description
○
○
○
○
○
○ Problem Space
○
○
e ○ C
earch Spac Affordance Vector(AV) ase Base
S ○
○ AV AV AV Solution Space
○
○AV AV AV AV
○
○
○
○
○
○
○
Fig.1.BlockingandaffordanceassignmentapproachinWebCBR
The paper is organized as follows: Next section presents the idea behind the
web-affordancenotion. Section 3 explainsthe proposed case-based approachand the
case population. Section 4 presents the experimentalresults and discussions. Finally,
section5wrapsupwithconclusionsandfuturedirections.
2 The affordance-guided querying approach
A main rationale behind the proposed approach is the hypothesis that a document
may serve morethan one informationneed, thatis, it may havemultiple affordances.
This because different parts (here we consider them as blocks) of a document may
haveslightlydifferentfocuses.Wedefinedalistoftopicsinadvance(manuallyatthe
moment)each of which fulfillsan informationneedof the user. In tourism domain,a
user may need information related to accommodation at a particular place, easy and
economicaltransportoptions,placestoroamaround,time to travelfromoneplaceto
anotherplace,food/typesofrestaurants,historical/importantplacestovisit,shopping
atfamousplaces,soonandsoforth.Currentlywehavealistof18topicsforthetourism
domain.Eachwebpagemayaffordtooneorseveralofsuchinformationneeds.
Affordanceofatextsegment/blockisrepresentedasavectorofwhicheachelement
specifiestheextentthetextaffordsacertaininformationneedintheaffordancelistfor
the task domain. The affordance of a whole document is determined on the basis of
theaffordancevectors(AV)ofitsconstituentblocks.Thesizeofanaffordancevector,
consequently,ismintheexample(ie.,tourism)domain(herem=18).
The querying approach proposed in this paper relies on retrieving a number of
documentsrelevantto the queryusing informationretrieval(IR) techniques,and then
employinganaffordance-basedrankingonthisset.Intherestofthepaper,weusethe
followingnotation:
AV ={A ,A ,........,A } (1)
text 1 2 m
whereAVisavectorwhileAi isascalarrepresentingtheaffordancewithrespecttothe
ithelementinthelistofmaffordances.
3 Case-Based, Affordance-Guided Web
The objective of this work is, given a query, to retrieve the web documents that
meetthe user’sinformationneedin the best possible way,using a refinedassessment
ofthe documentguidedbyaffordances.Theunderlyingassumptionis thattheweb is
informedabouttheaffordanceofeachdocument,inacasebase.Thereisacaseforeach
documentofwhich theproblemdescriptionpartconsistsof a term-setthatrepresents
the document in a concise way, and the identification of the document it represents.
Theterm-setis,inasense,adimension-reducedversionofthewebpage.Thesolution
partinformsabouttheaffordanceofthedocumentindirectly,throughaffordancesofits
constituentblocks.Henceacaseisrepresentedasfollows:
C ={ProbDescription,Solution}
i
where ProbDescription consist of a term-set representation of the web page while
Solutionhastwocomponents:
Solution={AV,I}
AV is the affordancevector of size m (see Equation1) and representsthe affordances
ofthewebpage.AV,togetherwithI(whichistheidentificationofthecorresponding
document),constitutesthe solutionpartof a case.Thenextsection describeshowthe
casebaseispopulated.
3.1 PopulationoftheCaseBase
Initially,casebasecontainsnocasesandcasesareconstructedincrementallyinthe
off-linemode.Tobuildthecasebase,weapplylink-to-textratiowhichisdefinedasthe
ratiobetweenthesizeofthetexttaggedwithhyperlinksandthetextwithouthyperlinks.
Problem description: A web page is first segmented into blocks and a textual
descriptionofeachblockb isextracted(afterremovingthemarkupsandstopwords)
i
using link-to-text ratio. If link-to-text ratio is more then, all hyperlinked text will be
skippedandlink-to-textratioisscaledinthenormalizedinterval[0-1].Thenfromeach
extractedblocktext,topk discriminativetermsareselected(afterstopwordremoval)
and added to ProbDescription in the problem descriptionof the case. This process is
describedinthe‘problempart’ofAlgorithm1.
Solution: The extracted text, say text , of the block b is processed to identify its
i i
affordancewith thehelp ofthe resourceterms(topicsand# termsconsideredin each
topic, are presented in the table 1). For each topic, the matching terms are identified,
the affordancewith respectto this topic is computedand the affordancevectorofthe
blockisupdated(seeComputeBlockAffordanceintheAlgorithm2)accordingly.The
AV of the document,in turn, is computedby ComputeDocAffordance,as described
inthe‘solutionpart’oftheAlgorithm1.Acaseisgeneratedbycouplingtheproblem
descriptionandthesolutionpartsandisaddedtothecasebase.
Algorithm1PopulationofCaseBase
Input: Listoftopic:L (fromtable1);
t
Awebdocument:d;
Procedure:
I:identificationofd;
ProblemPart:
1: InitializeprobDesctoNULL;
2: foreachblocktextdatado
3: removestopwordsandpunctuation;
4: filterouttopkwordsfromtheblocktextandaddittoprobDesc;
5: endfor
SolutionPart:
1: AV =ComputeDocumentAffordance(d)
d
2: Soln=<AV ,I >
d
CaseBase:
1: Addthenewcasehaving<probDesc,Soln>tothecasebase;
Output:TheCaseBase;
3.2 TheQueryingProcess
TwomainprocessesunderlyingthequeryingmethodaredescribedinAlgorithm3.
Thispartissimilartoinformationretrievalforthegivenquery,butretrievalisperformed
on a specific amountof extracted text from each block and the assigned affordances.
During the retrieval, the problem description part of the cases are matched with the
user’squeryandtopkcasesareretrieved.Thesearethenrankedusingthesolutionof
thecase,wheretheAVofthequeryandtheAVofcasesarecompared.AVofthequery
is computed on the basis of the terms (also called as ‘resource terms’) in each topic.
Aftereachsucharetrievalepisode,theAVofthetopknumberofcasesarerevisedand
modifiedinsuchawaythatitscurrentlyexperiencedrelevancetothequeryisproperly
reflectedinthecaserepresentation.
Algorithm2ProcedurestocomputeDocumentandBlockAffordanceVectors
Procedure:ComputeDocAffordance(d)
Input: Listoftopics-L ; Awebdocument-d;
t
Procedure:
1: Segmentdintoblocksb InitializeAV :=NULL;
i d
2: foreachblockb inddo
i
3: ComputeAVbi :=ComputeBlockAffordance(bi)
4: UpdateAV :=AV +AV
d d bi
5: endfor
6: returnAV
d
Output:TheAVofthegivendocumentb
i
——————————————————————————————————————
Procedure:ComputeBlockAffordance(b )
i
Input: Listoftopics-L (asintable1); AV -affordancevector;
t bi
Procedure:
1: foreachtopicj ∈Ltdo
2: Computethenumberofmatchingtermsinbiandtermsintopicj ∈Lt
3: UpdatethisscoreforthecorrespondingaffordanceinAV .
bi
4: endfor
5: returnAV
bi
Output:TheaffordancevectorAV oftheblockb
bi i
4 Experimental Results
4.1 WebCorpus
Theeffectivenessoftheproposedmethodisanalyzedthroughexperimentalresults
onacorpuscontainingthewebpagesmostlyrelatedtothetouristplacesinIndia.The
tourismwebpageswerecollectedbyapplyingthecrawlingprocessaccordingtoasetof
policiesthatfilterthesupplementaryfiles.Wehaveomittedthewebpageshavingonly
hyperlinks, images, advertisements and graphical layouts (like the index page of the
Algorithm3SimilarityAssessmentthroughblockingandaffordanceassignment
Input: Aqueryhavingnterms:q={qt1,qt2,··· ,qtn}
Casebasehavingmcases:{c1,c2,··· ,cm}
L -Listoftopics;
t
AV -thequeryaffordancevector
q
AV -theaffordancevectorofacase.
c
Procedure:
1: Retrievetopkcasesusing
sim(q,cj)= X sim(ti,tj)
ti∈q;
tj∈cj;
matchingterms
whereqisthequery;c isthecaseandt arethematchingtermsintheproblempartofc
j j j
2: foreachretrievedcasec do
k
3: computethequeryaffordancevectorAV withrespecttothetopiclistL ;
q t
4: getAV fromthesolutionofc ;
c k
5: computesim(AVq,AVc)usingcosinemetric;(seeequation5)
6: endfor
7: returntherankedlistoftopkcaseswithrespecttosim(AVq,AVc)
Output:Therankedlistofcasessortedbytheirsimilarityscores
mostofthesites).Additionallyweskippedthepagescontainingredirectoptions,less
significant textual description, only copyright information, etc. The remaining pages
are collected to form a raw web corpus. Then preprocessingtasks were performedto
generatethe case base havingproblemdescriptionand solution partsthroughcontent
andstructureminingwithfocusedinformationextraction.
4.2 Preprocessing
We have applied focused content filtering which performs the structural mining
on each collected web page. This structural mining, based on table OR paragraph
OR div tags, decomposes the given web page into blocks. Then for each block, we
applied the link-to-textratio to distinguish contentnoise and contenttext description.
We performduplicatesentenceseliminationboth at the phrase leveland at the whole
sentencelevelinordertoavoidrepeatingsentencesinthesolutionpartsofeachblock
text.Theextractedtextcontent,iftheyarerepresentedinhexcode,areconvertedinto
unicode.Somultilingualcontentusinghexrepresentationcanalsobeprocessed(except
forthepagesusingcertainproprietaryfonts/encodings).Wehaveretainedtheheadings
andparagraphmarkerswithselectedtopk termsfortheproblemdescription.Butdue
tolinktotextratio,someoftheheadingsmighthavebeenremoved.Specificpatterns
arehardlyseenforeliminatingtheunlinkednoisefromsuchpages.
Amongthetotalnumberof112,522webdocuments[1315seedURLswerecrawled
tothedepth3],14,033webpages,containingbothtourismandnontourismpagesbut
having sufficienttextualdata (after filtering the web pageshaving spam contentslike
unwanted,restricted contents, adultcontents, etc), were selected for our experiments.
Wehavemanuallycraftedthelistof18affordancesrelatedtotourismdomainincluding
theaffordancemiscellaneous.
Affordances #TermsAffordances #Terms
Accommodation 59 Retreats 59
Attractions 59 Shopping 59
Beaches 59 Spirituality 59
Deserts 59 Sports 66
HealthCare 60 ThemeParks 59
Heritage 59 TourPackages 59
HillStations 59 Transport 61
Landscapes 59 Wildlife 59
Nature 59 Miscellaneous Rest
Table1.Listofpredefinedaffordanceswithnumberoftermsineachaffordance
4.3 Queries/NewCases
Wehaveconsidered25tourismqueriesinEnglishlanguageusedinCrossLingual
Information Access (CLIA) Project1 - a large project on cross-lingual information
accesssystemsforIndianlanguages,thatisbeingfundedbytheGovernmentofIndia,
and being executed by a consortium of several academic institutions and industrial
partners[7]. Each query is presented in three forms: title - the actual query, desc -
theexpandedqueryandnarr-thenarrationofthequery.Atpresent,wehaveattempted
withtitle,descparts.Hereweconsideredeachquery(title/desc)asanewcase.
4.4 EvaluationMethodology
In the experiments, we compared the effectiveness of the retrieval using Lucene2
and the proposed approach. In Lucene, the similarity scoring function[5] is derieved
fromitsconceptualformulaasfollows:
sim(q,d):=coord(q,d)·queryNorm(q) (2)
· (tf(t∈d)·idf(t)2·t.getBoost()·norm(t,d))
X
t∈q
wheretf(t ∈ d)isthetermfrequency-thenumberoftimetoccursind;idf(t)isthe
inversedocumentfrequencyoftheterm;coord(q,d) isthescorefactorbasedonhow
many of the query terms are found in the specified document;queryNorm(q) is the
normalizing factor used to make scores between queries comparable (does not affect
the document ranking); t.getBoost() is the field boost and norm(t,d) encapsulates
a few (indexing time) boost and length factors. Here norm values is encoded during
index time and decoded during search time. Thus encoding/decodingcomes with the
precisionloss-thatmeansdecode(encode(x)) = xisnotguaranteed.Luceneallows
1http://www.clia.iitb.ac.in/clia-beta-ext/
2http://lucene.apache.org/java/docs/
theuserstocustomizeitsscoringformulabychangingtheboostfactorsforcalculating
the score of similarity between the query q and the document d. Lucene[5] sorts the
retrievedresultsbasedoneithertheirrelevanceorindexorderforthegivenquery.Here
wesorttheretrievedresultsbasedontheirrelevancetothegivenquery.
Intheproposedapproach,weweighttheAVofthecaseinthesimilarwayto[1,8]:
Weightedaffordanceofacase(W ):
ci
w
ci (3)
m w2
qPi=1 ci
wherew istheweightoftheaffordanceiintheAVofthecase.
ci
WeightedaffordanceofaQuery(newcase)(W ):
qi
w
qi (4)
m w2
qPi=1 qi
wherew istheweightoftheaffordanceintheAVofthequery.
qi
Similaritybetweenthequery(newcase)andthecaseiscomputedby:
sim(q ,c ):=sim(AV ,AV ):= W ×W (5)
i i qi ci X qi ci
matchingfeatures
Duringtheestimationofcaseaffordancevectors,thevaluesoftheelementsinthe
vector increase with the numberof matchingterms between the solution partand the
newcase.Insuchsituations,wecouldapplyaffordancevectorlengthnormalization.To
length normalize the elements of the affordancevector AV , for the case c having m
c
affordances: {A ,A ,··· ,A }, to the unit vector, we do the following: AV :=
t1 t2 tm c
AV /|AV | where denominator denotes the Euclidean length of the vector of the
c c
affordancet in c. In the mean time, we will take care of the effect of normalization
i
factorindecreasingthechancesofretrievalofthedocument.
1 Effect of Rank Aggregation
Lucene Proposed
0.9
0.8
0.7
>- noitagerggA000...456
knaR0.3
0.2
0.1
0
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9Q10Q11Q12Q13Q14Q15Q16Q17Q18Q19Q20Q21Q22Q23Q24Q25
Query ->
Fig.2.Effectofrankaggregationofluceneretrievalvstheproposedapproach
Effect of the Proposed approach on Selected Queries
1.2 Lucene Proposed
1
0.8
arity0.6
mil
Si0.4
0.2
0
2092 16222 3962 16217 18939 17181 14094 10417 5784
Queries
Fig.3.Effectoftheproposedapproachonafewselectedqueries
Weperformrankaggregation:Givenanewcase,retrievetopcandidatecasesusing
the problem description. Then similarity estimates of the affordance vector (solution
part) of each of the candidate cases with the affordance vector of the query are
computed.Finallytherankofalltopkcasesareaggregatedwithrespecttotheiractual
similarity scoresand the results are compared.The figure 2 showsthe effectsof rank
aggregationforthe25tourismqueries[title/descpartsareconsideredhere].Lucene
retrieval score is influenced by index time boot factors and applies tf - idf tradeoff
with overallcontentofthe textualdescription.In the proposedapproach,the pointof
focusistheaffordanceassignmentwithrespecttotheblocktextbasedonitsmaximum
affordance.The queries Q12, Q13 and Q14 are propernames representingthe places
and each extracted block text, that speaks about these places specifically, contributes
totheoverallaffordance.SimilarlyQ21andQ22focusonthespecificevent/hotelin
theparticularplace.Thisgivescombinedaffordancescorewithwiththepropernames.
Hencethisleadstoabetterperformanceformostofthequeries(particularlyforQ12,
Q13,Q21andQ21).
Next we considered a few sample queries whose similarities are effectively
computed through blocking and affordance assignment(fig. 3). For example, in Q1
(Query: sunderbans national park), the document with ID - 2092, ranked 14 in
Lucene retrievaland the proposed method has broughtit to rank 2. Here affordances
relatedtowildlife,heritageandattractionsarecapturedwhereastheaffordancesrelated
to nature is hardly captured. This is due to the fact that park under the affordance
nature contributedless to theoverallaffordancethan towildlife. In anotherexample,
for the query Elephant Safari in Kaziranga, the document,with ID: 10417, having
the dominating term of “safari”, has been brought to the top in lucene where as its
affordancevaluescoreveryless.Atthesametime,forthequeryGoddessMeenakshi
Temple, theproposedapproachcapturedthe document,with ID:5784,whoseblocks
describedifferenttopicsrelatedtoMeenakshitempleinMadurai,TamilNadu,India.
Eventhoughtheperformanceoftheproposedsystemispromising,theeffectofthe
noiseandtheaccuracyoffilteringapproachalongwiththelistofresourcetermsplay
avitalroleintheeffectiveretrievalofcases.Theeffectofspampageswillreducethe
chancesofretrievingtherelevantdocumentthroughboostingtheirscoresbyprojecting
therelatedthemes.Owingtopaucityofresources,wehavelimitedourspamfilteringto
filter pages containing adult content along with tourism related textual content. This
effectively reflects in the retrieved results with the proposed approach. This is our
preliminary attempt with manually crafted term list for each identified (predefined)
affordance related to the tourism domain. Developing an automated process for the
affordanceidentificationirrespectiveofthedomainmaybeattemptedinthefuture.
5 Conclusion
We presented an approach for achieving an effective case retrieval through the
similarityassessmentbasedonblockingandidentifyingaffordancesofwebdocuments.
Affordanceprovidesavisualcluetothecaseidentification.Traditionalmethodsdealing
with textual contenttry to apply similarity metrics collectively for the heterogeneous
blocks of text together presented in the same web content. This reduces the chances
of the user expectedcontents(solutions).The proposedapproachsolves this issue by
applying page segmentation through blocks, identifying valid text from these blocks,
scoringeachoftheblockswithcertainaffordancescoresandthenapplyingsimilarity
metricstoachievetheeffectivecaseretrieval.Theactualperformanceoftheproposed
approachcanbeseenwiththesimilarityscorescomputedusingqueryaffordancevector
andcaseaffordancevectors.Thepreliminaryresultsshowthattheproposedapproach
wouldbepromisingforidentifyingthespecificblocktextastherelevantsolution.
Acknowledgement
Wegratefullyacknowledge:Prof.SudeshnaSarkar(IITKharagpur)forinitiatingthiswork;
theeffortsofMr.ParinandMr.Gyan(IIT,Kharagpur)ingettingthelistofseedurlsforcrawling
andMr.DavidKabath(NTNU)inpreparingthelistof(tourismrelated)keywords.Alsoauthors
thanktheanonymousreviewersfortheirinsightswhichimprovedthequalityofthepaper
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