Suggestion of Promising Result Types for XML Keyword Search

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1 Suggstion o Promising Rsult Typs or XML Kyword Sarch Jianxin Li, Chngi Liu and Rui Zhou Swinurn Univrsity o Tchnology Mlourn, Australia {jianxinli, cliu, Wi Wang Univrsity o Nw South Wals Sydny, Australia ABSTRACT Although kyword qury nals inxprincd usrs to asily sarch XML dataas with no spciic knowldg o complx structurd qury languags or XML data schmas, th amiguity o kyword qury may rsult in gnrating a grat numr o rsults that may classiid into dirnt typs. For usrs, ach rsult typ implis a possil sarch intntion. To improv th prormanc o kyword qury, it is dsiral to icintly work out th most rlvant rsult typ rom th rtrivd data. Svral rcnt rsarch works hav ocusd on this intrsting prolm y using data schma inormation or pur IR-styl statical inormation. Howvr, this prolm is still opn du to som rquirmnts. () Th data to rtrivd may not contain schma inormation; (2) Rlvant rsult typs should icintly computd or kyword qury valuation; (3) Th corrlation twn a rsult typ and a kyword qury should masurd y analyzing th distriution o rlvant valus and structurs within th rtrivd data. As w know, non o xisting work satisis th aov thr rquirmnts togthr. To addrss th prolm, w propos an stimation-asd approach to comput th promising rsult typs or a kyword qury, which can hlp a usr quickly narrow down to hr spciic inormation nd. To spd up th computation, w dsignd nw algorithms asd on th indxs to uilt. Finally, w prsnt a st o xprimntal rsults that valuat th proposd algorithms and show th potntial o this work. Kywords XML kyword qury, rsult typ suggstion. INTRODUCTION XML has volvd to th standard or data rprsntation and xchang on th Intrnt. Du to th structural lxiility and htrognity o XML data, it is diicult or a usr to issu a structurd qury to xprss hr sarch rqust. As such, kyword sarch has mrgd as a popular Prmission to mak digital or hard copis o all or part o this work or prsonal or classroom us is grantd without providd that copis ar not mad or distriutd or proit or commrcial advantag and that copis ar this notic and th ull citation on th irst pag. To copy othrwis, to rpulish, to post on srvrs or to rdistriut to lists, rquirs prior spciic prmission and/or a. EDBT 200, March 22 26, 200, Lausann, Switzrland. Copyright 200 ACM /0/ $0.00 paradigm or inormation rtrival ovr XML data [8, 6, 3, 2, 4, 9, 2]. On o th signiicant mrits o XML kyword sarch is its simplicity usrs do not nd to larn a complx qury languag (i.., XQury or XPath), or know th structur o th undrlying data. Howvr, this kind o simpl qury ormat may not prcis and can potntially rturn a larg numr o rsults that may classiid into dirnt typs, i.., th lal paths o rsults, among thm only a w typs ar intrsting to th usrs. To addrss this prolm, on possil way is to irst comput th qury rsults and thn rank thm. Two dirnt ranking unctions [4, 6] can applid in this approach. From th rankd list o rsults, th typ o th highst rankd rsult is usually slctd as th promising rsult typ or th qury. Oviously, utilizing on spciic rsult or dciding rsult typ may not a roust mthod. W liv that masuring th corrlation twn a rsult typ and th issud kyword qury should rly on th analysis o th whol st o rlvant inormation, rathr than on pic o it. Exampl. Considr a kyword qury intrst art issud on th sampl data in Figur. Most likly, it is intndd to ind studnts who ar intrstd in art; hnc th rsult typ should root/studnts/studnt. Ranking mthods proposd in [4, 6] will rcommnd rsult typ as root/ooks/ook/titl instad. This is caus that th two kywords appar in th sam ook titl dramatic art & intrst, which lads to th highst ranking scor or this spciic rsult. Th prolm with this rcommndation mthod is that it is solly dpndnt on th quality o on qury rsult. Anothr mthod [4] inrs th rturn nod typ y analyzing kyword match pattrns. Kywords ar classiid into two catgoris: thos that spciy sarch prdicats and thos that spciy rturn nods. Idntiication o a rturn nod rlis on nod catgoris: ntity nod, attriut nod and connction nod. For instanc, givn a nod, i it corrsponds to a *-nod in th DTD, thn th nod is an ntity nod; i it dos not corrspond to a *-nod and only has on child nod which is a valu,thn th nod is an attriut nod; othrwis, th nod is a connction nod. A prolm with this approach is that it cannot handl wll th cas whr a nod s tagnam longs to any o th thr catgoris at th sam tim. Exampl 2. Sinc th qury kyword intrst appars in oth lmnt nams and valus, [4] will dtrmin th trm intrst as a match o lmnt nams, rathr than a valu. Th suggstd rsult typ is hnc root/studnts/studnt.

2 root studnts ooks studnt studnt ook ook ook ook ook ook orn intrst orn intrst yar namyar titl yar titl titl yar titl yar titl dat "980" "dramatic art" "980" "strt art" "980" "980" "visual art atr 980" "980" "advrtising art" "dcorativ strt art" "dramatic art & intrst" Figur : Portion o data tr or a sampl o studnts rading intrsts XML dataas Anothr rcnt proposal rlvant to idntiying th rsult typ is XRal [3]. It utilizs th statistics o undrlying XML data, which summarizs th rlationships twn lmnt nods and all tokns in th la nods. Sinc nithr th rlationships among th lmnt nods nor th rlationships among valus ar considrd, th prcision o this approach may su-optimal. Exampl 3. Considr anothr kyword qury 980 art issud on th sampl data in Figur. Th intnsion is most likly to ind studnts who ar intrstd in art and wr orn in 980. Hnc, root/studnts/studnt should suggstd as th rsult typ. XRal will suggst root/ooks instad. This is mainly caus thir statistic inormation only considrs th rlationship twn th rsult typ and ach indpndnt kyword, not th comination o th qury kywords. In this papr, w propos a nw approach to ctivly and icintly idntiy th rsult typ or kyword sarch on XML data. Our ky ida is to considr all th kyword qury answrs corrsponding to dirnt rsult typs. This avoids th limitations o th prvious approachs. In ordr to sav th computation tim, w also dvlop a suggstion mthod asd on a nw summarization tchniqu, which taks into considration oth th valu and structural distriutions o kywords. In addition, our proposd approach dos not rquir a schma o th XML data. W also mploy an nhancd ranking unction to prdict th most rlvant rsult typ. Our contriutions in this papr can summarizd as ollows: W propos a nw mthod o prdicting th rsult typ or XML kyword quris. Our mthod mploys a ranking ormula that taks into considration th corrlation twn a rsult typ and th qury kywords asd on svral typs o statistic inormation. W dvlop a nw data structur to stimat accuratly th siz o kyword qury rsults; th data structur capturs oth th structural and th valu distriutions in th data concisly. W implmnt th proposd tchniqus in a kyword sarch ngin prototyp calld XBridg. Extnsiv xprimnts hav n conductd dmonstrating suprior ctivnss, icincy and scalaility o our mthod against prvious mthods. Th rst o th papr is organizd as ollows. Sction 2 provids som dinitions and introducs th XSKtch indx that will usd in this work. Sction 3 ormally dins th prolm o inding promising rsult typs or a kyword qury and illustrats th main ida o our approach. W dsign a gnral ranking unction in Sction 4 and propos a st o algorithms that can icintly work out th promising rsult typs in Sction 5. Sction 6 prsnts our xprimntal valuation. Sction 7 rviws th prvious work on XML kyword sarch. Sction 8 concluds th papr. 2. PRELIMINARY In this sction, w irst introduc som ncssary dinitions and thn th asics o th XSktch mthod [8]. Dinition. [XML Data Tr] An XML data tr is dind as T t = (V t, E t, r) whr V t is a init st o nods, rprsnting lmnts and attriuts o th data tr T t; E t is st o dirctd dgs whr ach dg (v,v 2) rprsnts th parnt-child rlationship twn th two nods v, v 2 V ; r is th root nod o th tr T t. W assum that all valus appar in th la nods. Givn an dg = (v, v 2), w din P(v 2) = v and v 2 Ch(v ). Dinition 2. [Kyword Qury] A kyword qury is a st o dirnt trms, dnotd y Q = {k, k 2,..., k n}. W considr th AND-smantics or th qury, i.., a qury rsult must contain at last on occurrnc o ach trm k i Q. For xampl, Figur rprsnts an XML data tr aout studnt and ook inormation. Q = {980, art} is a kyword qury that is issud y usrs. To prcisly stimat a kyword qury ovr XML data with a grat siz, w uild upon th idas o XSktch [8, 7], which is dsignd to stimat th slctivity o XML twigs. Dinition 3. [XSktch] An xpandd graph synopsis G(T t) = (V, E) or an XML data tr T is an dg-lald dirctd graph, whr V is a st o distinct tag nams that occur in V t; ach nod in v V corrsponds to a sust o data nods in V t (trmd th xtnt o v, or xtnt(v)) that hav th sam lal (dnotd y lal(v)); th lal or ach dg (u, v) E is dind as: () lal(u, v) = {B}, i v is B-stal w.r.t. u; (2) lal(u, v) = {F}, i u is F-stal w.r.t. v; (3) lal(u, v) = {F, B}, i oth () and (2) hold; (4) lal(u, v) = {}, othrwis.

3 root F/B F/B studnts () ooks () F/B F/B studnt (2) ook (6) F/B B B B F/B B orn(2) intrst (2) yar (5)nam () titl (5)dat () H (studnt): H(ook): corn c intrst CS studnt hstudnt 2 00% cyar cyar cnam ctitl CS ook CSook h ook 6 /6 h ook 6 4/6 Figur 2: Exampl o xpandd graph synopsis cdat ctitl CS ook h ook For xampl, Figur 2 displays th xpandd graph synopsis o th XML data tr in Figur. In XSktch, count(v) rcords th numr o lmnts that map to v in th XML data, i.., th siz o v s xtnt. For instanc, count(studnt) = 2 and count(ook) = 6 in Figur 2. It also capturs th localizd ackward- and orward-staility conditions across synopsis nods o an XML data tr. A nod u is B(ackward)- stal (F(orward)-stal) with rspct to a parnt (rsp., child) nod v in th synopsis, i all data lmnts in xtnt(u) hav at last on parnt (rsp., child) lmnt in xtnt(v). For instanc, vry studnt lmnt has two child lmnts orn and intrst. At th sam tim, oth orn and intrst lmnts can rach up to thir parnt studnt lmnts. So th parnt-child dg twn studnt and orn (or intrst) is markd as F/B. Intuitivly, B-staility guarants that all lmnts in u dscnd rom v and, thror, count(u) is an xact stimat or th xprssion v/u ; similarly, F-staility nsurs that all lmnts in u rach at last on lmnt in v and, thror, count(u) is an xact stimat or u[v] whr v is takn as a prdicat o u. To improv th prcision o stimation, XSktch capturs two kinds o statistical inormation: structural and valu distriution inormation. Sinc th work [7] ocuss on th discussion o th rlationship twn th prcision o th stimation and th spac udgt o uilding th indx XSktch, it only summarizs part o th distriution inormation so that th spac udgt can mt. To choos th signiicant inormation, it rlis on many assumptions. For instanc, som valus, not all valus, on dirnt paths ar slctd and thn th corrlations among th slctd valus ar considrd. Radrs ar rrrd to [8, 7] or mor dtails. 3. OVERVIEW OF OUR WORK Sinc it is common or XML data to contain nods with th sam lmnt nam in dirnt contxts, w us lal paths to dnot nod typs. A lal path is a squnc o lmnt nams that appar in th path rom th root to th nod in qustion. Dinition 4. [Rsult Typs] Givn a kyword qury Q = {k, k 2,..., k n} and an XML data tr T = (V t, E t, r), rsult typs o Q ovr T is a list o distinct lal paths that start rom th root nod r and stop at connctd nods. Each connctd nod or its dscndant nods should contain at last on instanc o th givn trms {k, k 2,..., k n}. Considr Exampl 3 again. Thr ar two rsult typs or th kyword qury 980 art : root/studnts/studnt and root/ooks/ook. Givn a st o rsult typs and a scoring unction, w din th promising rsult typ as th on that has th maxi- 6 /6 Figur 3: Exampl o structural distriution mum scor. For xampl, root/studnts/studnt is th promising rsult typ according to our proposd ranking unction (S Equation (3) in Sction 4). Th concptual valuation procss to dtrmin th promising rsult typ rom a st o candidats is as ollows: w irst comput all qury rsults individually and classiy thm asd on thir typs. Thn ach typ aggrgats th local scor o ach individual rsult in th sam group togthr whr th scor is computd y a proposd ranking unction. Finally, th typ with th highst scor is chosn as th promising rsult typ. Oviously, it is tim-consuming whn th siz o th data to sarchd is larg. Thror, w would lik to propos an icint approach that can achiv th similar targt (i.., locating th promising rsult typ) with much lowr cost (i.., y stimating th scor o ach typ). In this papr, w concntrat on th prcision o th stimation without considring th usag o spac. This is caus w ind that uilding an XSktch-lik indx only rquirs a small prcntag o spac compard to th data [8]. Thror, w can rin th indx to th point whr all dgs in G long to on o th thr typs: {F, B, F/B} (S Dinition 3). Mor spciically, w kp splitting th corrsponding nod sts or th dgs that cannot satisy th stal conditions until on typ o stailitis is achivd. In addition, w utiliz a tr-styl synopsis T, rathr than a graph-styl synopsis G, to maintain th prcis structural and valu distriution inormation, which can rduc mor stimation rrors. In th rst o th papr, w us T to rprsnt th tr synopsis o th XML data. Dinition 5. [Structural Distriution] Considr an intrnal lmnt nod u V t and its child nods {v i} V t, th structural distriution H(u) o u consists o a st o () distinct distriution typs (C v, C v2,...,cs u), whr C vi is th count o th child nods in u, which hav th sam tag nam with v i, and CS u is th count o th siling nods o u, which hav th sam tag nam with u; and (2) its corrsponding distriution rat h u, which is th prcntag o xtnt(u), which contains th sam st o child nods and holds th sam distriution typ. For xampl, Figur 3 displays th structural distriution o th intrnal lmnt nods studnt and ook o th XML data tr in Figur. Considr irst th studnt lmnt nod, it has on orn and on intrst child nod, i.., C orn = and C intrst =. And it has a siling nod with th

4 F(orn): E 980' F(intrst E E 'dramatic 'art ' ' ): E 'art ' CS orn CS intrst CS intrst ' % ' art' 00% ' dramatic','art' ' /2 F(yar): E' 980 F(titl): E' art' E 'dramatic ' ' E 'art' CS yar CS titl CS titl 980' 3/5 ' art' 4/5 'dramatic','art' /5 its typ should (E dramatic, E art, CSintrst)=(,, ) whr CS intrst is caus thr is no siling nod that is lald as intrst. As on out o two intrst lmnt nods has oth dramatic and art tokns, its total rat is dramatic, art =/2. All ths inormation can prcomputd and cachd or stimating th siz o a kyword qury. shard path root studnts shard path 2 studnt root ooks ook shard path 3 root ooks ook Figur 4: Exampl o valu distriution prdicat prdicat 2 orn intrst yar titl "980" "art" "980" "art" titl prdicat 3 "980" "art" sam tagnam studnt undr th parnt nod studnts, i.., CS studnt = 2. As such, its distriution typ is (,, 2). Bcaus oth studnt lmnt nods hav th sam distriution typ, th distriution rat h studnt is 00%. For th ook lmnt nod, it has six siling nods with ook as thir tag nams, i.., CS ook = 6. For th irst ook lmnt nod, it contains on yar nod and on nam nod, hnc its distriution typ is (,, 6) and its rat is /6 caus no othr ook contain th sam st o child nods and satisy th sam distriution typ. Similarly, w can work out th typ and rat or th last ook lmnt nod as (,, 6) and /6. For th rst our ook nods, all o thm contain th sam st o child nods and hold th sam distriution typ, hnc th rat o th nw typ is 4/6. All ths inormation can prcomputd and cachd or stimating th rsult siz o a kyword qury. Atr w summariz th structural distriution inormation H, w start to xtract th valu distriution asd on H rom th XML data. Dinition 6. [Valu Distriution] Givn a la nod v V t and its corrsponding siz xtnt(v) in th tr synopsis T, th valu distriution F(v) o v consists o a st o () distinct distriution typs (E tokn, E tokn2,..., CS v) whr E tokni is (or tru) i th inormativ tokn tokn i xists in th la nod v and CS v is th count o th siling nods o v whr th siling nods hav th sam tag nam with th nod v; and (2) its corrsponding distriution rat is th prcntag o xtnt(v), which contains th sam st o distinguishd inormativ tokns and holds th sam distriution typ. For xampl, Figur 4 displays th valu distriution o th la nods orn, intrst, yar and titl o th XML data tr in Figur. Considr th orn lmnt nods: oth o thm in xtnt(orn) contain th tokn 980, i.., E 980 = ; and nithr o thm has siling nods, i.., CS orn =. In addition, w also know that th siz o xtnt(orn) is 2. As such, th valu distriution typ o F(orn) is (, ) and its total rat is 980 = 00%. Similarly, w can work out th valu distriutions o intrst-art, yar-980 and titl-art, rspctivly. Not that th siz o xtnt(yar) (or xtnt(titl)) is 5 and thr o thm contain th tokn 980 (or art ). Furthrmor, w can xtnd th valu distriution to multipl tokns. For a la nod intrst and two tokns { dramatic, art }, w ind that only th irst intrst lmnt nod contains th two tokns togthr. Thror, Figur 5: Exampl o gnratd qury tmplats 3. Our Main Ida Th ida o our approach is as ollows. Givn a kyword qury, w irst dtrmin th corrsponding distinct paths w.r.t. ach trm in th qury, and gnrat th qury tmplats in which th givn trms ar kpt. Thn, w can otain all rsult typs whr ach typ is a distinct lal path rom th root nod to th lowst connctd nod on its qury tmplat. Hr, th connctd nod has th similar smantics to th concpt o LCA (Lowst Common Ancstor) or kyword quris ovr XML data. Atr that, w stimat th qury tmplats ovr th xpandd tr synopsis with th hlp o distriution inormation, and comput th scor or ach rsult typ y calling a ranking unction. Finally, th rsult typ with th highst scor will suggstd as th promising rsult typ to th qury. Now lt s rily introduc th procdur o our approach y taking Exampl 3 as an xampl. For th givn kyword qury 980 art, w can gnrat two qury tmplats y mrging thir distinct lal paths, as shown in Figur 5. Firstly, w stimat th shard paths ovr th tr synopsis in Figur 2 using th orward- and ackward-staility inormation. Th siz o root/studnts/studnt is 2 whil th siz o root/ooks/ook is 6. Atr that, w gin to invok th stimation o th corrsponding prdicats o th two shard paths. For th prdicat o th irst shard lal path, i.., prdicat, w hav 2 h studnt c orn c intrst 980 art = 2 = 2. For th prdicat o th scond shard lal path, i.., prdicat 2, w hav 6 h ook c yar c titl 980 art = =.92. Sinc th total scor o root/studnts/studnt is largr than that o root/ooks/ook, th ormr will rcommndd as th promising rsult typ to th usrs. From this xampl, w can s that our approach utilizs th comination o th rlvant nods to masur th corrlations twn ach rsult typ and th givn kyword qury. 4. SCORING FUNCTION In this sction, w dvlop our scoring unctions or rsult typ suggstion or XML kyword quris. 4. Scoring a Rturn Typ Candidat

5 Considr a kyword qury Q = {k, k 2,..., k n}. It might corrsponds to dirnt intrprtations. For xampl, considr th qury 980 art in Figur, two possil intrprtations (also calld qury tmplats):. /root/studnts/studnt[orn 980 ][intrst art ], and 2. /root/ooks[ook/yar 980 ][ook/titl art ]. whr is a shorthand or tsting i th trm is containd in a nod. Th rturn typs o th aov two intrprtations ar /root/studnts/studnt and /root/ooks, rspctivly. Gnrally, a rturn typ corrsponds svral qury tmplats. For xampl, th qury tmplat /root/ooks[ook/titl 980 ][ook/titl art ] also contriuts to th lattr rturn typ. Givn any intrprtion o a kyword qury Q, considr on o its rsult R = {N, n, n 2,..., n n}. whr ach n i is a la nod and contains th trm k i, and nod N is th lowst common ancstor (LCA) o n,..., n n. W considr th ollowing scoring unctions to comput th scor o R. Ranking Function : This typ o ranking unction only considrs th trm inormation,.g., th TF-IDF mthod or its variations. Th standard TF-IDF is rom th inormation rtrival ild and considrs oth trm rquncy (i.., how many tims a trm appars in a documnt) and invrs trm rquncy (i.. invrs o how many documnts contain th trm). In ordr to apply it to th typical XML dataas scnarios, w mak th ollowing adaptation: () w assum trm rquncy t is always qual to as in [7], and (2) w us invrs lmnt rquncy, which is dind as th total numr o lmnt in th XML data tr ovr th numr o lmnts that contains th tokn in th sutr rootd at th lmnt in qustion. Finally, w can otain th wight o ach nod n i as wight(n i) = log 2 ( + t ) log 2 i = log 2 i whr t = and th inal scor o R as lmnt lvl. Howvr, as it pnalizs ach trm wight wight(k i) indpndntly using Distanc(N, n i), it may avor th trms with short distanc valus. For instanc, considr a kyword qury Q = {k, k 2} and on o its rsults R = {N, n, n 2} in Figur 6(a). Th scor o R can calculatd: Scor 2(R, Q) = 5 + =. In this cas, n is 0 2 mor intrsting than n 2 as th wight o k is much largr than that o k 2, yt thy hav th sam contriution to th inal scor. Thror, w should rduc th pnalty o th trm, which ar rought y its corrsponding long distanc. Anothr prolm with this ranking unction is that it cannot dirntiat a tightly-coupld rsult rom a looslycoupld on. An xampl is givn in Figur 6(). According to Equation (2), two givn qury tmplats will hav th sam scor. Oviously, th tmplat on th right sid should ttr than th on on th lt sid. 0 n... N n2 2 n contains k wight (k )= 5 n2 contains wight (k 2 )= (a) Havy Pnalty n N n2 n 3 n N n2 n 3 () Insnsitiv to th Structur Figur 6: Prolms with Ranking Function 2 Ranking Function 3: Motivatd y th aov analyss, w propos a nw ranking unction that mracs th principls o th aov two unctions, i.., it is proportional to th total wights o th trms as Function dos; At th sam tim, it also taks into account th cts o structurs o XML. Mor spciically, it ovrcoms th two issus idntiid rom th Ranking Function 2. Scor (R, Q) = n wight(n i) () i= Lt s considr Exampl 3 whr th qury is {980 art} ovr th XML data tr in Figur. Basd on th ormula, or th irst studnt lmnt in Figur w hav wight 980 = log 2 (27/3) = and wight art = log 2 (27/5) = Th scor o th irst studnt w.r.t th qury {980 art} is thror =.794. Th ovious prolm with this scroing unction is that it ovrlooks th structur o th rsult. Ranking Function 2: Th scond ranking unction considrs oth trms and structurs o th rsult. n wight(n i) Scor 2(R, Q) = (2) dist(n, n i) whr Distanc(N, n i) is th lngth o th path rom N to n i in th XML data tr. Th intuition is that trms appar ar rom th root o th rsult sutr should pnalizd. This unction usually works wll to masur th rlvanc o a rsult at th i= Scor 3(R,Q) = { n i= wight(ki), i dist(n, n i) = 0 ni= wight(k i ) ( n i= dist(n,ni ) θ) γ, Othrwis. (3) whr θ is th total tims o th dgs that ar rpatd on th path rom N to ach n i in th XML data tr; γ is a paramtr to alanc th impact o th structur to th scor (its dault valu is 2); and dist is dind as dist(n, n i) = { dist(n, n i), dist(n, n i) avg-dpth avg-dpth + (dist(n, n i) avg-dpth) η, ls whr avg-dpth is th avrag dpth o th XML data tr; and η [0, ] is a tunal paramtr. Exampl 4. Considr th thr XML documnts in Figur 7 and a kyword qury Q = {k, k 2}. Thr ar thr qury tmplats and thy corrspond to dirnt rturn typs. Dnot any o th qury rsults or th thr qury tmplats as r, r 2, and r 3, rspctivly, and assum th wights or oth kywords ar and γ = 2, w calculat th

6 r x k k r y k k k k k z r k k Exampl 5. Continuing th thr rturn typs in Figur 7, thy hav 2, 9, and 4 qury rsults in th corrsponding documnts, rspctivly. Thror, avg-rsults is = 3 5. Givn that ach rturn typ in this xampl has only on qury tmplats and th act that all rsults in th sam qury tmplats hav th sam scor, w can calculat th scor or ach rsult typs as: (a) Documnt d r x () Documnt d 2 r y (c) Documnt d 3 r z Scor(r/x/) = 2 2 = Scor(r/y) = 8 5 = Scor(r/z/) = 2 4 = 2 k (d) A k () A 2 k () A 3 Finally, th top-scoring rturn typ, r/z/, will chosn as th promising rturn typ. Figur 7: Thr Qury Tmplats or th Qury ovr Thr Documnts whr Thir Corrsponding Rturn Typs ar Indicatd in Dottd Boxs. scors o th individual rsults as: Scor 3(r, Q) = + ( + ) γ = 2 Scor 3(r 2, Q) = + (2 + 2) γ = 8 Scor 3(r 3, Q) = Scor 3(r, Q) In th rst o th papr, w will us th Equation (3) as our scoring unction. 4.2 Aggrgating Scors W know that using only on (or a w) qury rsults to prdict th promising rsult typ may not a good ida, as illustratd in Sction. Instad, w would lik to aggrgat th scor o all qury rsults in ach rturn typ to dtrmin th promising rsult typ. This involvs summing up scors o all rsults o all qury tmplats associatd with ach rturn typ. Howvr, a tchnical issu is that dirnt rturn typs usually corrspond to vry dirnt numrs o rsults, hnc this mthod will invitaly iasd towards rturn typs with many qury rsults. Thror, it is dsiral to considr at most top-k rsults or ach rturn typ whn prorming th scor aggrgation. W suggst that a rasonal way to dtrmin K is to st it to th avrag numr o rsults pr rturn typ. Considr m rsult typ candidats RTC i ( i m) xist in th rtrivd data and ach typ candidat RTC i has c(rtc i) qury rsults. Th avrag rturn typ qury rsult is avg-rsults = m m c(rtc i) Not that i th numr o qury rsults o a rturn typ is lss than avg-rsults, w will us all its rsults to aggrgat th scors. Not that in cas a rturn typ has svral associatd qury tmplats, th numrs o rsults o all associatd qury tmplats nd to summd up. i= 5. ALGORITHMS OF FINDING PROMISING RESULT TYPES In this sction, w irst introduc a gnral kyword sarch algorithm that uss th actual kyword sarch rsults to work out th promising rsult typs. Thn w propos a mor icint algorithm that utilizs th olin summarizd statistics to prdict th promising rsult typs. 5. Invrtd Nod List asd Algorithm (INL) Th asic ida o INL is to irst rtriv all rlvant nod lists w.r.t. a kyword qury, and thn mrg th nod lists rom th shortst on, i.., th nod with th lowst documnt rquncis will procssd irst. To incras th icincy, w dploy th Dwy numr schm to ncod th nods or-hand, as it nals us to icintly comput th lowst common ancstor nod givn a st o nods idntiid y thir Dwy cods. During th computation, w rcord th shard paths as th rsult typ candidats. For ach typ candidat, w maintain a data structur (S Tal ) whr w tak th individual rsult scor as a ky and th numr o this kind o rsults ar addd togthr as a valu. Furthrmor, th tal is always sortd y th individual rsult scor in a dscnding ordr. At th sam tim, w also rcord th total numr o qury rsults and th numr o distinct rsult typs, which ar latr usd to comput th thrshold avg-rsults. Atr w procss all th nod lists, w calculat th scor or ach rturn typ candidat y using its data structur and th thrshold avgrsults. Th typ candidats with th highst scors will slctd as th promising rsult typs. Tal : Data structur or a typ candidat RTC i Individual Scor #Rsults This approach oviously will incur sustantial amount o computation as it nds to accss and mrg all th occurrncs o kywords. Th complxity o INL is O( n i= Li ) A mor dtaild discussion is providd in Sction 6..

7 5.2 Statistic Distriution Inormation-asd Algorithm (SDI) In this sction, w irst show th procdurs o uilding structural and valu distriution indxs. Thn w gnrat a st o qury tmplats or a kyword qury. Finally, an icint algorithm is proposd to stimat th constructd qury tmplats asd on th pr-uilt distriution indxs Building Structural Distriution Algorithm Building structural distriution indx input: An XML data tr T t output: An xpandd tr synopsis T with th distriution rat H(v) o ach synopsis nod v V : initiat an mpty tr synopsis T and an mpty st H(); 2: r t th root nod o T t; 3: Q.nquu(r t) 4: whil Q is not mpty do 5: v Q.dquu() 6: Ch v v.childrn(); 7: or all nod v c Ch v do 8: i T contains dg (v, v c) at th currnt lvl thn 9: IncrasCount(v, v c); 0: ls : AddNwEdg(v, v c, T ); 2: nd i 3: Q.nquu(v c) 4: nd or 5: nd whil 6: T MarkFBStaility(T, T t); 7: GnratDistriution(T, T t); 8: rturn (T, H); In this sction, w introduc th procdur o summarizing th structural inormation o an XML data tr; this hlps to improv th prcision o th stimation as it considrs th corrlations among th outgoing dgs o th tr nods. It consists o thr main stps: gnrating th intrmdiat tr synopsis T rom an XML data tr; marking th synopsis nods o T with Forward- and Backward-staility conditions; and making th statistics aout th distriution o outgoing dgs across th synopsis nods o th XML data tr. Gnrating tr synopsis Th asic ida hr is similar to th radth-irst sarch (BFS) ovr a tr that gins at th root nod and xplors all its nighoring nods. Thn or ach o ths nods, it xplors thir unxplord nighoring nods until all nods in an XML tr ar xplord. During th tr travrsal, th structur o th data tr is astractd y using AddNwEdg(v, v c, T ) and th xtnt inormation or ach synopsis nod is otaind y using IncrasCount(v, v c). Onc th travrsal is compltd, th intrmdiat tr synopsis T o th XML data tr T t is uilt ully. Th dtaild procdur is providd rom Lins - Lin 5 in Algorithm. Atr that, w nd to mark th staility or ach dg ovr th tr synopsis y using th unction MarkFBStaility() and thn call GnratDistriution() to produc th structural distriution typs and thir rats. Marking dgs with MarkFBStaility() To captur th localizd Forward- and Backward-staility conditions across th synopsis nods o an XML data tr, it is rquird to travrs th intrmdiat tr synopsis T and chck th staility o ach synopsis dg(v, v 2). For th dg, w irst r- Algorithm 2 Function GnratDistriution(T, T t) input: Th xpandd tr synopsis T and th XML tr T t output: Th xpandd tr synopsis T with th structural distriution inormation H : or all intrnal synopsis nod v V do 2: Initializ H(v, h) = 0, (v, h) 3: or all instanc v t xtnt(v) do 4: sisiz GtSilingByTagnam(v t) ; 5: h SummarizChildDist(v t); 6: h.appnd(sisiz); 7: H(v, h) H(v, h) + xtnt(v) ; 8: nd or 9: nd or 0: rturn th structural distriution inormation H; triv th corrsponding groups, xtnt(v ) and xtnt(v 2), o nods that ar lald as v and v 2, rspctivly. Thn w nd to compar th two sts o nods, which lads to thr cass. Cas : dg (v, v 2) satisis F-Staility caus vry instanc nod o v in xtnt(v ) has at last on child instanc nod o v 2 in xtnt(v 2), which mans that w can walk rom v to v 2 in a orward stp. Similarly, Cas 2 mans that w can walk rom v 2 to v in a ackward stp. I nithr conditions holds, it is rquird to call a spcial opration Split(xtnt(v 2)) that partitions th group nods xtnt(v 2) into som Maximal sugroups. All th sugroup nods should kp th Forward- and Backward-staility with th nods xtnt(v ). Hr, th word Maximal mans that givn any two sugroups xtnt (v 2) and xtnt 2(v 2), i w mov a nod rom xtnt (v 2) (or xtnt 2(v 2)) to xtnt 2(v 2) (or xtnt (v 2)), th rsulting group nods xtnt 2(v 2) (or xtnt (v 2)) and th nods xtnt(v ) do not satisy ithr Forward- or Backward-staility. Summarizing dgs distriution: To summariz th outgoing dgs distriution or an intrnal nod v, it is rquird to ind out its child nods Ch(v), its siling nods that hav th sam tagnam with v, and th siz xtnt(v) o its xtnt, rspctivly. And thn w summariz th count C vi rom th child nods v i Ch(v) and th count CS v rom its siling nods, which is usd to gnrat a distriution typ. Hnc, th rat o this distriution could incrasd y / xtnt(v). Th dtaild procdur is illustratd in Algorithm 2 whr Function GtSilingByTagnam() is usd to calculat th numr o th siling nods that shar th sam tag nam with th givn intrnal nod and Function SummarizChildDist() is usd to comput th numr o th child nods with dirnt tag nams Building Valu Distriution Indx In this sction, w introduc th procdur o driving th valu distriution or ach synopsis nod v at th la lvl o xpandd tr synopsis T. Each instanc nod v t xtnt(v) may contain a singl tokn,.g., yar or multipl tokns,.g., titl (S Figur ). I w uild an indx to rcord all possil cominations o th trms in th whol documnt, th indx would too larg to practical. Nvrthlss, rcnt studis [2, 9] suggst that th typical kyword qury lngth is within two to our tokns and that th possiility o longr quris dcrass gratly with th qury lngth. In addition, w also ind that or a kyword qury, th possiility o all its tokns that ar locatd at on spciic instanc nod is typically low. That is to say, vn i a kyword qury contains mor than our tokns, only a w

8 Algorithm 3 Building th valu distriution indx input: An XML tr T t and its xpandd tr synopsis T output: th distriution rat F(v) whr l = r/.../v is a path o T and v is a la-lvl synopsis nod : or all la synopsis nod v V do 2: Initializ F(v, l) = 0, (v, l) 3: or all v t xtnt(v) do 4: sisiz v t.parnt.gtchildnodsbytagnam(v t) ; 5: tokns FiltrGtTrms(v t); 6: L GnComination(tokns, sisiz); 7: or all tokn cominations l L do 8: F(v, l) F(v, l) + xtnt(v) 9: nd or 0: nd or : nd or 2: rturn th valu distriution inormation F; o thm long to th sam la nod. Thror, or ach instanc la nod v t, w uild our kinds o cominations: - Com, 2-Com, 3-Com, 4-Com, whr -Com rprsnts ach singl tokn in th v t s valu contnt; i-com rprsnts all cominations that hav i tokns (2 i 4). As shown in Algorithm 3, or ach la synopsis nod v V, w can otain a sugroup o instanc nods xtnt(v) V t and driv th valu distriution y procssing ach instanc nod v t xtnt(v). Firstly, w xtract inormativ tokns rom th valu contnt o v t y calling th unction FiltrGtTrms(v t) whr th contnt is split into tokns and non-inormativ tokns ar iltrd out y a stop-word list that can ditd or importd y th DB dsignr. Thn, w calculat th count o v t s siling nods that hav th sam tag nam with v t. Finally, th unction GnComination() is invokd to gnrat all sust o siz no largr than our rom tokns in lmnts. For ach comination l, w appnd th count o v t s siling nods that hav th sam tag nam with v t to th nd o th comination l. Onc a nw comination l is gnratd, w insrt it into th valu distriution st F(v) i l xists orhand or updat th rat o F(v,l) Finding Rsult Typ Candidats To prdict th promising rsult typ, w hav to ind all rsult typ candidats irst. Typically mtadata is ar lss than th data itsl. Thror, th numr o distinct lal paths is ar lss than that o th path instancs. Givn a kyword qury Q = {k, k 2,..., k n}, w can quickly locat th distinct lal paths or ach k i and thn work out th rsult typ candidats y applying a tight mrg opration to ths paths. By a tight join, w man that or any two paths, thy ar connctd with th longst common prix. Firstly, w rtriv th distinct lal paths or ach kyword y accssing th indx Tokn2Path. Thn w chck th numr o th givn kywords. I th qury only contains on trm, w tak all distinct lal paths as th rsult typ candidats whr w dirctly gnrat th qury tmplats y comining ach lal path and th trm togthr. And w tak th wight o th trm as th scor o a singl rsult. I a usr s qury contains mor than on trm and ach o thm corrsponds to a list o lal paths, w nd to considr all cominations o th lal paths that com rom th dirnt path lists. This task is dividd into two stps, which is shown in Algorithm 4. In Stp : Lin 2 - Lin 7, w mrg th two shortst lists o lal paths that Algorithm 4 Collcting rsult typ candidats & prdicats input: A tokn-to-path indx Tokn2Path and a kyword qury Q = {k, k 2,..., k n} output: A st o qury tmplats Φ organizd as a hash map rom shardpath to a list o qury tmplats t i, whr ach t i is in th orm o (scor,prdicats) : Initializ Φ[x] =, x; 2: or all qury kyword k i do 3: LP ki th st o lal paths k i appars in th XML data (via Tokn2Path indx) 4: nd or 5: i Q = thn 6: or all lal path p LP k do 7: sp p; prds ; scor CalcScor(sp, prds) 8: Φ[sp] Φ[sp] (scor, prds) 9: nd or 0: ls : lt q, q 2,..., q n a prmutation o k, k 2,..., k n such that LP ki is in incrasing ordr o lngth; 2: or all lal path u LP k do 3: or all lal path v LP do 4: (sp,prds, scor) MrgPaths(u, v, q, q 2 ); 5: Φ[sp] Φ[sp] (scor, prds) 6: nd or 7: nd or 8: or j = 3 to n do 9: or all lal path p LP kj do 20: AddPath(p, q j, Φ); 2: nd or 22: nd or 23: nd i 24: rturn Φ; corrspond to two o th givn kywords whr a unction MrgPaths(p, p 2, k, k 2) is dsignd to comin any two paths p LP k and p 2 LP. It starts rom th comparison o th irst nod o ach path. I th two nods hav th sam lal, w thn continu to compar th nxt nod. Th comparison is don rcursivly until w ind th nods that hav dirnt lals or on o th path dos not hav th nxt nod. At this point, w dnot th part that can shard y th two paths as th shard path sp whil th rst o th two paths as th prdicats prds that taks th two kywords k and k 2. At th sam tim, th wight o th prdicat is rcordd as scor. All o th output will takn as th intrmdiat qury tmplats Φ. In Stp 2: Lin 8 - Lin 22, w mrg th nxt shortst list o lal paths into th aov intrmdiat qury tmplats Φ i th qury contains thr or mor kywords, which is implmntd y a unction AddPath(p, k j, Φ). In othr words, or ach tmplat t i Φ, w irst compar path p and sp o t i. I p is qual to sp, thn w only mrg th nw kyword into th prdicat prds o t i; I p covrs sp, thn w nd to do th comparison o th rst o p and prds; Othrwis, w will gnrat a nw shard path and a nw prdicat caus p and sp only shar partially. In all thr conditions, th wight o th nw prdicat should updatd and th nw kyword k j should also insrtd into th updatd prdicat Mthod SDI Our SDI algorithm is asd on Statistical Distriution Inormation stord in th tr synopss introducd in Sctions 5.2. and Th ky ida is to us th synopsis to stimat th scor or ach candidat qury tmplats rathr than actually xcuting it.

9 Algorithm 5 Computing rsults with SDI indx input: A st o qury tmplats Φ organizd as a hash map rom shardpath to a list o qury tmplats t i, whr ach t i is in th orm o (scor,prdicats) output: Top-K most promising rsult typs : or all shard lal path sp Φ do 2: t i (sp, scor,prdicats) Φ[sp]; 3: st EvaluatSinglPath(sp); 4: v th nd nod o sp; 5: H.nquu(v); 6: or all prdicat prd prdicats do 7: whil H is not mpty do 8: v H.dquu(); 9: i v is a la nod thn 0: l DistStyl(v.contnt(), v); : st st VDRat(F, v, l); 2: ls 3: h DistStyl(Ch(v), v); 4: st st SDRat(H, v, h) {C vc v c Ch(v)}; 5: or all v c Ch(v) do 6: H.nquu(v c); 7: nd or 8: nd i 9: nd whil 20: nd or 2: i st 0 thn 22: rscor[sp] rscor[sp] + st scor(t i ); 23: ls 24: Modiy th currnt qury tmplat t i y moving th last nod in its shard path to th prdicats; 25: goto Lin 2 26: nd i 27: nd or 28: rturn th top-k sp in th rscor; distriution rat o h in v y using th inormation in H. To pro F or H, anothr unction DistStyl() is dsiral to produc th distriution typs l or h rom th prdicat tr whr th nods in distriution typs ar sortd y thir tag nams. Using th distriution typ and rat, w calculat th xpansion numr o th approximat answrs y multiplying th count C vc whr v c Ch(v) and C vc. Thn th computational valu will cachd with th corrsponding scor o th spciic prdicat in Tal. Similarly, w rpatdly procss or othr qury tmplats and rcord thir rsults in Tal. Finally, w calculat th aggrgatd scor or ach rsult typ candidat and prdict th promising ons whr avg-sampl can computd during th stimation. 6. EXPERIMENTS To vriy th ctivnss and icincy o our proposd approach, w implmntd th XBridg systm which includs th INL and SDI algorithms, and compard it with XRal [3]. All algorithms wr implmntd in Java and run on a 3.0GHz Intl Pntium 4 machin with GB RAM running Windows XP. W do not considr XSk [4], which was shown to outprormd y XRal [3]. 6. Datast and Quris Tal 2: Statistics o th Datasts Datast #Elmnts Max Dpth Avg Dpth NASA 476, UWM 66, DBLP 3332, Givn a qury tmplat that contains a shard lal path, i.., a rsult typ, and a list o prdicats, w irst stimat th path and gt th approximat siz as th numr o th sarch rsults. Thn w stimat th corrsponding prdicats whr w tak into account th counts C vi, CS v and thir distriution rats. By multiplying th maximal numr, th counts, and th rats togthr, w can otain th approximat numr o th rsults that match with on cas in th qury tmplat. I th stimat is zro (.g., th structural or valu distriution synopsis indicats no match or a particular qury tmplat), w considr a variant o th currnt qury tmplat y moving th connctd nod up to its parnt nod (Lin 24). Th dtaild procdur is givn in Algorithm 5. W considr all rsult typ candidat sp Φ, and w rtriv th corrsponding qury tmplat consisting o a list o prdicats prdicats and th scor o th tmplat scor. And thn w stimat th siz o th currnt qury tmplat without considring any o its prdicats using th unction EvaluatSinglPath(); th unction considrs th F- and B- staility across th nods o th path. Atr that, w gin to stimat th prdicats in prdicats. Evry prdicat is travrsd and procssd as a small tr and its root nod is th nd nod o RTC. At th sam tim, th scor o ach prdicat is cachd as in Tal. I th xplord nod v in th prdicat tr is a la nod, w call th unction VDRat(F, v, l) to comput th valu distriution rat o l in v y using th statistic inormation in F. I th xplord nod v is not a la nod, i.., it is an intrnal nod, w call th unction SDRat(H, v, h) to otain th structural To tst th applicaility o ach approach, w choos th ral datasts: Nasa 23MB, UWM 2MB and DBLP 27MB rom Washington XML Data Rpository []. Th critrion o slction is asd on th dirnt dpths and sizs o th datasts. I th dpth o a documnt is small, which mans that th documnt is too lat, it is asy or a kyword qury to choos th root o th documnt or th nods at th highr lvl as its promising rsult typs. In this cas, th rsult typs ar straightorward. Most o tim, th largr th avrag dpth o a datast is, th mor complx th structur o th datast may com. It is highly possil or this kind o datast that contains multipl rsult typs or a kyword qury. Thror, w slct thr datasts that hav th dirnt maximal dpths and avrag dpths, which is shown in Tal 2. To xtnsivly dmonstrat th prormanc o ach mthod, w randomly slct 6 kyword quris with lss than 4 trms or ach datast, which is shown in Tal 3. Ths quris ar chosn with dirnt rquncis. Furthrmor, w put two nois trms diprsion and Opticaly in th kyword quris that should gnrat mpty rsults. 6.2 Quality o Suggstion To masur th quality o th suggstd promising rsult typs, w valuat all quris in Tal 3 ovr th corrsponding datasts. Th suggstd promising rsult typs or ach kyword qury ar shown in Tal 4. In addition, i two suggstd promising rsult typs hold th ancstor-dscndant rlations, w only show th typs at th lowr lvl, i.., th mor spciic rsult typs.

10 Tal 3: Kyword Quris or Each Datast Quris NASA UWM DBLP Q i {magnitud} {lvl} {valuation} Q 2i {photographic} {archology} {ojct orintd} Q 3i {photographic magnitud} {Najoom} {Frank Manola} Q 4i {rotation diprsion} {individual suprvision} {concpts applications} Q 5i {cap photographic} {uilding tchnologis} {multimdia data typ} Q 6i {Opticaly propr motion} {approvd prormanc organization} {Frank dataas 983} Tal 4: Promising Rsult Typs or Each Qury Datast Quris INL XRal SDI NASA Q {para, titl, dinition} {talhad} {para, titl, dinition} Q 2 {para, titl, dinition} {talhad} {titl, para, dinition} Q 3 {ilds} {talhad} {para, dscriptions} Q 4 {} {} {} Q 5 {ilds} {talhad} {ilds, sourc} Q 6 {} {} {} UWM Q 2 {rstrictions, titl, commnts} {lvl} {rstrictions, titl, commnts} Q 22 {titl} {titl} {titl} Q 32 {instructor} {sction listing} {instructor} Q 42 {root} {titl} {root} Q 52 {titl} {titl} {titl} Q 62 {rstrictions} {rstrictions} {rstrictions} DBLP Q 3 {titl} {titl} {titl} Q 23 {titl} {titl} {titl} Q 33 {author} {author} {author} Q 43 {titl} {titl} {titl} Q 53 {titl} {titl} {titl} Q 63 {procdings} {procdings} {procdings} From th rsults ovr NASA in Tal 4, w ind that XRal only ocuss on on nod at th highr lvl whil INL and SDI can rach to th mor dtaild nods. For th kyword qury Q, th usrs cannot guss th ral maning rom th rsult typ talhad suggstd y XRal, ut thy can asily classiy thir sarch intntions rom {para, titl, dinition} rcommndd y INL and SDI whr para rprsnts a paragraph o dscription, titl rprsnts a titl o journal and dinition rprsnts a dinition o a tal talhad. For th kyword qury Q 3, INL taks ilds as th promising rsult typ, which is th child nod o talhad that is suggstd y XRal. Howvr, SDI rcommnds two mor spciic typs para, dscription that ar th dscndant nods o ilds. For anothr two kyword quris Q 4 and Q 6, no suggstions ar gnratd y all approachs caus o th givn two nois kywords {diprsion, Optically}. In this papr, w don t considr th procssing o th splling rrors. From th aov rsults, w ind that th usrs can slct a ttr and mor maningul rsult typ rom a suggstion o SDI, INL than that o XRal. From th rsults ovr UWM in Tal 4, w ind that or th kyword quris Q 2, Q 32 and Q 42, XRal gnrats dirnt promising rsult typs rom th othr approachs and or th othr quris, th sam suggstions ar otaind. For Q 2, XRal works out th tagnam lvl whil th othr approachs work out mor maningul sarch intntions,.g., th titl, th rstrictions and th commnts o a cours. For Q 32, INL and SDI can dirctly suggst Najoom as an instructor, rathr than its ancstor nod s tagnam sction listing. For Q 42, XRal can gt a ttr rsult typ than INL and SDI, which illustrats that th prcision o our mthods coms lowr whn th statistic inormation only coms rom a w rlvant nods. This is caus iv individual nods and thr suprvision nods ar distriutd ovr th dirnt titl nods whr only on titl nod contains th two trms individual and suprvision at th sam tim. In this cas, INL and SDI may suggst a nod typ at th uppr lvl as th rsult typ. Howvr, i w dlt th trm individual rom th spciic titl nod, XRal still prdicts th sam rsult typ - titl. Oviously, no titl nod satisis th qury Q 42. Thror, XRal may suggst wrong rsult typs caus it dos not considr th structural corrlations twn th givn trms. From th rsults ovr DBLP in Tal 4, w ind that XRal, INL, and SDI can produc th sam suggstions or th iv kyword quris. This is caus th structur o DBLP datast is lat. Thror, th givn kyword quris ar vry asy to usd to locat th corrsponding maningul rsult typs. From th thr sts o xprimntal rsults and discussions, w can ind that SDI and INL can work wll in th datasts with th complx or simpl structurs. That is to say, thy can prdict th rsult typs with th mor dtaild smantics, which can guid usrs to ind thir intrstd inormation asily. Howvr, XRal can only work wll in th datast with th rlativ simpl structur. 6.3 Procssing Tim

11 To valuat th icincy o our algorithms, w masur th rspons tims that all thr approachs ar rquird to ind th top 3 promising rsult typs or th quris in Tal 3. As w can s, SDI can achiv gratr icincy than oth XRal and INL in all conditions. In addition, w ind that in most cass, XRal nds shortr tim to procss th corrsponding kyword quris than INL. Rspons Tim (s) Q2 Q22 Q32 Q42 Q52 Q62 Kyword Quris o NASA INL XRal Figur 8: Rspons Tim ovr NASA Datast Rspons Tim (s) Q4 Q24 Q34 Q44 Q54 Q64 Kyword Quris o UWM SDI INL XRal Figur 9: Rspons Tim ovr UWM Datast Rspons Tim (s) Q3 Q23 Q33 Q43 Q53 Q63 Kyword Quris o DBLP SDI INL XRal Figur 0: Rspons Tim ovr DBLP Datast From Figur 8, w ind that SDI can work out th promising rsult typs in sconds. To do th sam tasks, INL rquirs sconds and XRal rquirs sconds or so. Thror, SDI can rduc th rspons tim gratly. In addition, w s that INL nds narly 9 sconds to procss Q 32 caus oth kywords {photographic, magnitud} hav vry highr rquncis in NASA, which rsults in mor computations than that o th othr kywords. Thror, w can say that INL is asy to actd y th siz o th datast. Howvr, XRal and SDI ar rlativly stal with rgards to th siz o th datast. From Figur 9, w ind that all thr mthods can dtrmin th promising rsult typs within scond. This is caus UWM is a documnt with th small siz. But SDI is still much ttr than XRal and INL. From Figur 0, w ind that SDI can rcommnd th promising rsult typs within scond. XRal nds 8-0 sconds to dtrmin thir rsult typs. But INL prorms vry ad and nds 30 sconds or so to do th sam tasks, which is not accptal or th usrs to wait or th suggstions or thy continu to do thir kyword sarch. SDI From th xprimntal rsults and discussions, w can conclud that SDI is a stal approach to suggst th promising rsult typs to th usrs. In addition, SDI can inish th task o rcommndation within short tim, which is practical and accptal or th usrs to wait. Although XRal is also rlativly stal, it will nd up to 0 sconds somtims, which is asil ut not accptal in practic. For INL, howvr, th rspons tims is vry slow in most cass and it is actd y th sizs o th datasts, which is inasil to takn as a rcommndd approach in practic. 7. RELATED WORK To mak th rturnd rsults mor maningul, thr ar svral approachs proposd in th litratur: suprvisd rsult dinition, ranking rsults, and prdicting rsult typs. It is natural in many applications or domain xprts or dataas administrators to provid guidancs to kyword quris. [8, ] considrd th prolm o idntiying rturn nods. Both o thm rquir schma inormation. In addition, [8] rquirs a systm administrator to split th schma graph into pics, calld Targt Schma Sgmnts (TSS) or sarch rsult prsntation. Précis [] rquirs usrs or a systm administrator to spciy a wight o ach dg in th schma graph, and thn ach usr nds to spciy a dgr constraint and cardinality constraint in th schma to dtrmin th rturn nods. [6] is hlpul to inr or gnrat th guidanc automatically. It considrd th prolm o dirntiating sarch rsults or kyword sarch on structurd data. By dining th dirntiaility o qury rsults and quantiying th dgr o dirnc, a limitd numr o valid aturs in a rsult can drivd, which can usd to maximally dirntiat this rsult rom th othrs. Anothr approach is to allow dirnt intrprtations o th qury, ut strivs to rank th mor rlvant rsults highr in th list o qury rsults. XKyword [8] proposd to rank th rsults according to th distanc twn dirnt kywords in th documnt. XRANK [6] xtndd Googl s PagRank to XML lmnt lvl, to rank among th LCA rsults, which taks into account rsult spciicity, kyword proximity and hyprlink awarnss togthr. XSEarch [4] mployd a ranking schm that considrs actors such as distanc, trm rquncy, and documnt rquncy. [20] proposd cohrncy ranking(cr), a domain- and dataas dsign-indpndnt ranking mthod that is asd on an xtnsion o th concpt o mutual inormation. Yt anothr approach is to prdict th most proal rsult typs or th kyword qury and rturn rsults only rlatd to this intrprtation. XSk [4] proposd to gnrat th rturn nods which can xplicitly inrrd y kyword match pattrn and th concpt o ntitis in XML data. It rquirs to comput th rsults irst and thn driv th typs o rturn nods rom th rsult st. Bsids, this approach rlis on th concpt o ntity and considrs a nod typ t in DTD as an ntity i t is * -annotatd in DTD. [3] adoptd th statistics o undrlying XML data to idntiy th rturn nod typs whr th statistic inormation coms rom th numr o nods that contain th givn kywords as ithr valus or tag nams in thir sutrs and shar th sam path. Thr ar many works on gnrating maningul qury rsults or XML kyword sarch y inrring th smantics rom various prspctivs. [6, 3, 2, 4, 9] proposd to irst rtriv th rlvant nods matching with vry singl

12 kyword rom th data sourc and thn comput LCAs or SLCAs o th nods as th rsults to rturnd. XRANK [6] and Schma-Fr XQury [3] dvlopd stack-asd algorithms to comput LCAs as th rsults. [2] introducd th Indxd Lookup Eagr algorithm whn th kywords appar with signiicantly dirnt rquncis and th Scan Eagr algorithm whn th kywords hav similar rquncis. [4, 5] took th similar approachs as [2]. But thy ocusd on th discussions how to inr RETURN clauss or kyword quris w.r.t. XML data. [0] discovrd that th iltring mchanism in MaxMatch algorithm in [5] was not suicint and it committd th als positiv prolm and rdundancy prolm. To ovrcom th prolms, [0] proposd a nw iltring mchanism asd on th concpt o Rlaxd Tighst Fragmnt (RTF) as th asic rsult typ. [9] dsignd a MS approach to comput SLCAs or kyword quris in multipl ways. [2] took th Valual LCA (VLCA) as rsults y avoiding th als positiv and als ngativ o LCA and SLCA. [22] proposd an icint algorithm calld Indxd Stack to ind answrs to kyword quris asd on XRank s smantics to LCA, namd Exclusiv Lowst Common Ancstor ELCA. Basd on th ELCA smantics, th rsult o a kyword qury is th st o nods that contain at last on occurrnc o all o th qury kywords ithr in thir lals or in th lals o thir dscndant nods, atr xcluding th occurrncs o th kywords in th sutrs that alrady contain at last on occurrnc o all th qury kywords. In addition, thr ar othr rlatd works that procss kyword sarch y intgrating kywords into structurd quris. [5] proposd a nw qury languag XML-QL in which th structur o th qury and kywords ar sparatd. But th usrs ar rquird to spciy partial structurs as prdicats in XML-QL languag. [3] introducd a mthod to md kywords into XQury to procss kyword sarch. Unlik most xisting approachs, our mthod addrsss th prolm o automatically prdicting promising sarch intntion or XML kyword quris y considring th valu and structural distriutions o th data, rathr than rlying on sujctiv actors (.g., usrs intractiv oprations and assignd wights). Furthrmor, it works wll in th asnc o dataas schmas. 8. CONCLUSIONS In this papr, w hav proposd an icint and ctiv mthod to discovr th promising rsult typs or a kyword qury ovr XML data sourcs, with th aim to hlp usrs disamiguat possil intrprtations o th qury. Th proposd mthod is asd on a nw ranking mthod taking into considration th qury rsults or dirnt intrprtations and slcting on that has th maximum scor. An icint algorithm asd on statistics synopsis uild on th XML data has n dvlopd which achivs high prcisions with much astr xcution spd. Exprimntal rsults hav dmonstratd that our proposd approach has good prormanc - high prcision and low rspons tim, or kyword quris ovr svral ral XML datasts. 9. REFERENCES [] Washington XML Data Rpository. [2] A. T. Arampatzis and J. Kamps. A study o qury lngth. In SIGIR, pags 8 82, [3] Z. Bao, T. W. Ling, B. Chn, and J. Lu. Ectiv xml kyword sarch with rlvanc orintd ranking. In ICDE, pags , [4] S. Cohn, J. Mamou, Y. Kanza, and Y. Sagiv. XSEarch: A Smantic Sarch Engin or XML. In VLDB, pags 45 56, [5] D. Florscu, D. Kossmann, and I. Manolscu. Intgrating kyword sarch into XML qury procssing. Computr Ntworks, 33(-6):9 35, [6] L. Guo, F. Shao, C. Botv, and J. Shanmugasundaram. XRANK: Rankd Kyword Sarch ovr XML Documnts. In SIGMOD Conrnc, pags 6 27, [7] M. Hadjilthriou, A. Chandl, N. Koudas, and D. Srivastava. Fast indxs and algorithms or st similarity slction quris. In ICDE, pags , [8] V. Hristidis, Y. Papakonstantinou, and A. Balmin. Kyword Proximity Sarch on XML Graphs. In ICDE, pags , [9] iprospct. iprospct natural so kyword lngth study (nuvmr 2004). Tchnical rport, iprospct, [0] L. Kong, R. Gillron, and A. Lmay. Rtriving maningul rlaxd tightst ragmnts or xml kyword sarch. In EDBT, pags , [] G. Koutrika, A. Simitsis, and Y. E. Ioannidis. Précis: Th ssnc o a qury answr. In ICDE, pags 69 78, [2] G. Li, J. Fng, J. Wang, and L. Zhou. Ectiv kyword sarch or valual lcas ovr xml documnts. In CIKM, pags 3 40, [3] Y. Li, C. Yu, and H. V. Jagadish. Schma-Fr XQury. In VLDB, pags 72 83, [4] Z. Liu and Y. Chn. Idntiying maningul rturn inormation or xml kyword sarch. In SIGMOD Conrnc, pags , [5] Z. Liu and Y. Chn. Rasoning and idntiying rlvant matchs or xml kyword sarch. PVLDB, ():92 932, [6] Z. Liu, P. Sun, and Y. Chn. Structurd sarch rsult dirntiation. PVLDB, 2():33 324, [7] N. Polyzotis and M. N. Garoalakis. Structur and valu synopss or xml data graphs. In VLDB, pags , [8] N. Polyzotis, M. N. Garoalakis, and Y. E. Ioannidis. Slctivity stimation or xml twigs. In ICDE, pags , [9] C. Sun, C. Y. Chan, and A. K. Gonka. Multiway slca-asd kyword sarch in xml data. In WWW, pags , [20] A. Trmhchy and M. Winsltt. Ectiv, dsign-indpndnt xml kyword sarch. In CIKM, pags 07 6, [2] Y. Xu and Y. Papakonstantinou. Eicint Kyword Sarch or Smallst LCAs in XML Dataass. In SIGMOD Conrnc, pags , [22] Y. Xu and Y. Papakonstantinou. Eicint lca asd kyword sarch in xml data. In EDBT, pags , 2008.

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