Update-Aware Accurate XML Element Retrieval

Transcription

1 Updat-Awar Accurat XML Elmnt Rtrival Atsushi Kyaki, Jun Miyazaki Graduat School of Information Scinc Nara Institut of Scinc and Tchnology Takayama, Ikoma Nara , Japan {atsushi-k, Knji Hatano Faculty of Cultur and Information Scinc Doshisha Univrsity 1-3 Tatara-Miyakodani Kyotanab Kyoto , Japan Goshiro Yamamoto, Takafumi Taktomi, Hirokazu Kato Graduat School of Information Scinc Nara Institut of Scinc and Tchnology Takayama, Ikoma Nara , Japan {goshiro, takafumi-t, Abstract In this papr, w propos a mthod for accuratly rtriving XML lmnts considring documnt updats. If documnt updats ar not handld in a sarch systm, usrs cannot obtain appropriat sarch rsults, which rducs th usfulnss of th sarch systm. W apply an incrmntal approach to updat an indx bcaus an rbuild-from-scratch approach taks longr tim. In addition, global wights, i.. th statistics computd with all documnts in th sarch systm, may not b accurat whn a fw numbr of documnts is indxd or whn global wights chang drastically. To solv ths problms, w propos to xtnd a function of incrmntal updats of indics to gnral XML lmnt rtrival systms, with filtrs to rduc th updat cost by liminating unimportant lmnts and trms. Morovr, w apply a mthod for intgrating path xprssion which stimats accurat global wights in trm calculation. Exprimntal rsults showd that our proposd mthod can b up to 25% fastr to updat indics than th simpl incrmntal updats and can improv th sarch accuracy by 4% with documnt st of static statistics. Th proposd mthod can also sarch accuratly, vn undr continuous changs in th statistics of th documnts. I. INTRODUCTION An information unit in XML lmnt rtrival is not a documnt but an lmnt of XML documnts. XML lmnt rtrival systms prsnt lmnts that contain dscriptions satisfying th information nds of usrs who thus do not hav to spnd tim sking rlvant dscriptions in ach documnt. Although xisting studis of XML lmnt rtrival hav attaind both ffctivnss and fficincy in qury procssing [20], [22], [13], [5], ths studis hav not considrd documnt updats. Wb documnts ar frquntly updatd; i.. insrtd, dltd, or modifid. In particular, Wikipdia articls ar updatd 4000 to 8000 tims pr hour 1. Information rtrival systms ar xpctd to prsnt sarch rsults basd on th latst contnt on th Wb, spcially as nw topics ar addd to documnts. Without handling updats, a sarch systm cannot find nwly insrtd documnts, and it ranks documnts basd on obsolt information, which rducs th ffctivnss. Thus, w add a function for handling documnt updats to th xisting tchniqus for XML lmnt rtrival. Thr ar two goals for this study as follows: 1) rflcting documnt udats to a sarch systm as soon as possibl, and 1 2) computing trm wights accuratly by stimating accurat global wights. To achiv th first goal, two rquirmnts ar ndd as follows: building an indx which can b updatd incrmntally according to documnt updats, and highly fficint indxing. Th mainstram approach for updating an indx is to construct a nw indx priodically from scratch whil discarding th xisting on. It may tak a long tim to rtriv updatd documnts if constructing a nw indx is costly. Incrmntal updats ar rquird to shortn this dlay. Our proposd systm has a function of incrmntal updats of th indx. W bliv that this is th first study focusd on fast incrmntal updats of indics in ffctiv and fficint XML lmnt rtrival systms. Although Googl supports fast incrmntal updats with ffctiv and fficint qury procssing, its approach diffrs from ours. Googl analyss th link information of Wb pags to find important pags, whras our study utilizs txt information. W can apply our approach to othr structurd documnts apart from th Wb, vn if ths do not hav link information. Th proposd systm can insrt, dlt, and modify th indx by xpanding xisting an indx structur. Th updat fficincy of th indx is low bcaus a numbr of updat targts is tratd as updat targts in XML lmnt rtrival compard with documnt rtrival. Thus, w propos two kinds of filtrs for rducing updat cost by liminating unnccsary updat targts. Concrnd with th scond goal, trm wights cannot b calculatd accuratly in som situations such as: whn a sarch systm dos not stor sufficint amount of documnts, and whn nw topic mrg drastically. This is bcaus som kinds of statistics usd in calculating a trm wight ar global wights that ar aggrgat statistics drivd from all documnts in a documnt st. Thus, global wights ar difficult to calculat in th som situations mntiond abov. W nd a mthod to approximat accurat global wights with an insufficint numbr of documnts.

2 DID:1 <articl> <p>bill Gats is </p> <body> <sc>early lif </sc> <sc>windows </sc> <sc>books </sc> </body> </articl> Fig. 1. XML documnt DID:2 <articl> <sc>stv Jobs </sc> <body> <h2>businss lif </h2> <sc>appl computr </sc> </body> </articl> DID: 1, EID: 1 PE: /articl Bill Gats is Early lif Windows Books DID: 1, EID: 3 PE: /articl/body Early lif Windows Books DID: 1, EID: 2 PE: /articl/p Bill Gats is DID: 1, EID: 4 PE: /articl/body/sc Early lif DID: 1, EID: 5 PE: /articl/body/sc Windows DID:1, EID: 6 PE: /articl/body/sc Books DID:2, EID: 1 PE: /articl Stv Jobs Businss lif Appl computr DID:2, EID: 3 PE: /articl/body Businss lif Appl computr DID:2, EID: 2 PE: /articl/sc Stv Jobs DID:2, EID: 4 PE: /articl/body/h2 Businss lif DID:2, EID: 5 PE: /articl/body/sc Appl computr DID:1 DID:2 Fig. 3. XML lmnt articl EID:1 articl EID:1 p EID:2 sc EID:2 body EID:3 body EID:3 Bill Gats is... Stv Jobs... sc EID:4 sc EID:5 sc EID:6 h2 EID:4 sc EID:5 Early lif... Windows... Books... Businss lif... Appl computr... Fig. 2. XML tr Authors add structurs to a documnt:.g. chaptrs, sctions, and paragraphs. W utiliz ths structurs to idntify th bst matrial for satisfying th information nds for usrs. Som structurs ar maninglss, so lmnts dfind by thos structurs ar inappropriat as sarch rsults, and som xisting studis [4], [3] includ attmpts to liminat ths. Suppos that a usr sks information from Documnt 1 about Early lif..., Windows..., and Books. XML lmnt rtrival systms try to prsnt th usr Elmnt 3 of Documnt 1, sbcaus that lmnt contains all of th information that th usr nds and no xtra information. W valuatd th ffctivnss and fficincy of our approachs through xprimnts with two cass: th static statistics cas in which topics rarly chang, and th dynamic cas in which nw topics ar addd frquntly. In th rmaindr of this papr, Sctions II and III dscrib th basic concpt of XML lmnt rtrival and th rlatd studis, rspctivly. Sction IV discusss fast incrmntal updats of indics. Sction V rmarks accurat XML lmnt rtrival with considring documnt updats. Finally, Sction VI discusss th xprimntal valuations. Sction VII concluds this papr. II. XML ELEMENT RETRIEVAL In this sction, w dscrib th concpts of XML lmnts and quris in XML lmnt rtrival. A. XML Elmnt W giv spcific xampls in Figurs 1 3 to dfin XML lmnts. Figur 1 illustrats XML documnts. Each documnt is assignd a documnt idntifir (DID). Figur 2 dpicts trs abstractd from Figur 1. An XML documnt can b prsntd by a tr structur, which hlps to undrstand th structur of th documnt. Each lmnt is assignd an lmnt idntifir (EID), which is assignd in documnt ordr. W can idntify an lmnt using its DID and EID. A pair of start and nd tags rprsnts an XML lmnt nod within an XML tr, and th nstd structur of XML lmnts rprsnts ancstor dscndant rlationships. Each lmnt in Figur 3 is th txt that compriss a st of txt nods within th XML tr in Figur 2. W dscrib th path xprssion (PE) of ach lmnt. B. Quris for XML Documnts Thr ar two ways for xprssing an information nd in a qury: through kywords and through documnt structur. A qury ntirly composd of qury kywords is calld a contntonly (CO) qury, whras a qury composd of pairs of qury kywords and a constraint on th documnt structur is calld a contnt-and-structur (CAS) qury. CO quris ar usd just as in traditional information rtrival for txt documnts. Usrs can submit CO quris vn if thy do not know th structurs of th documnts that ar rtrivd. In contrast, CAS quris utiliz on of th most significant faturs of structurd documnts: i.. documnt structur. With a CAS qury, a usr can obtain spcific rsults with rgard to granularity and contnt. W giv a spcific xampl of a CO qury and a CAS qury. Ths ar xprssd in th narrow xtndd XPath I (NEXI) [23] qury languag. A CO qury //*[about(., "Windows")] mans that th candidat sarch rsults ar lmnts containing Windows. Elmnts 1, 3, and 5 of Documnt 1 can b sarch rsults in Figur 3. A CAS qury: //articl[about(.,"stv")]//sc [about(., "Appl")] is mor complx. Lt us focus on th first half of th qury, //articl[about(., "Stv")], which mans that candidats for this part ar lmnts that contain Stv and whos path xprssions nd with an articl tag. Th scond half of th qury, //sc[about(., "Appl")], mans that candidat sarch rsults ar lmnts that contain Appl and whos path xprssions nd with a sc tag. Th sarch rsults ar lmnts that satisfy th lattr constraint and whos ancstor lmnts satisfy th formr constraint. Th only lmnt

3 satisfying th qury constraints is Elmnt 5 of Documnt 1 in Figur 3. III. RELATED STUDIES Hr w xplain ffctiv and fficint XML rtrival. W also discuss studis focusd on updats in sarch systms. A. Effctiv and Efficint XML Sarchs 1) Effctiv XML Sarchs: Th most important goal of XML lmnt rtrival is highly accurat sarchs. Th mainstram approach to xtracting rlvant lmnts is as follows: first, calculat a trm wight for ach lmnt by using a trmwighting schm; nxt, comput a scor for ach lmnt using ths trm wights. Trm-wighting schms for XML lmnt rtrival ar oftn drivd from studis on documnt rtrival. Both of ths ar composd of thr typs of factors: local wights that ar statistics drivd from ach documnt (lmnt); global wights that ar statistics drivd from all documnt in a documnt st; and constant valus (cofficints and paramtrs). Local wights and constant valus ar asy to calculat and rfr to bcaus local wights ar computd for a nwly insrtd lmnt. Howvr, it is difficult to calculat global wights on dmand bcaus th ntir documnt st must b scannd to comput ths. Th most significant diffrnc btwn documnt rtrival and XML lmnt rtrival is th mthod for computing global wights. Trm-wighting schms in documnt rtrival assum that vry documnt has th sam attribut and blongs to th sam class. Thus, global wights ar calculatd using all documnts. Howvr, in XML lmnt rtrival, lmnts ar assignd to classs. Global wights ar calculatd for lmnts of th sam class. Thr ar diffrnt ways to classify lmnts. On approach is to classify lmnts by path xprssion. In Figur 3, sinc Elmnts 4, 5, and 6 of Documnt 1, and Elmnt 4 of Documnt 2 all hav th sam path xprssion /articl/body/sc, th global wights ar calculatd using ths lmnts. Altrnativly, lmnts with th sam tag can b placd in th sam class. Bcaus Elmnts 4, 5, and 6 of Documnt 1 and Elmnts 2 and 4 of Documnt 2 all hav th sc tag, th global wights ar calculatd using ths lmnts, as dpictd in Figur 3. W us classification basd on path xprssion in our systm, bcaus this is rportdly mor accurat [18]. Thr ar svral kinds of trm-wighting schms for XML lmnt rtrival;.g. TF-IPF [11], BM25E [12], and th qury liklihood modl for XML lmnt rtrival [9] (QLMER). BM25E is a probabilistic modl. In a trm calculation of th classic trm-wighting schm TF-IPF, statistics on th occurrnc frquncis of trms ar utilizd. Convrsly, BM25E utilizs not only th statistics but also lmnt lngth (th numbr of trms in an lmnt). Languag modl tchniqus hav bn dvlopd in th filds of spch rcognition and machin translation. Rcntly, ths tchniqus hav bn introducd into th fild of information rtrival. In particular, th qury liklihood modl [14] is wll studid and achivs significant rsults. This modl has bn adaptd to XML lmnt rtrival [9]. In th trmwighting schm, th scor of ach lmnt is th product of th occupancy probabilitis of th qury kywords as shown in Eq. (6). This mans that non-zro valus ar computd only for th lmnts containing all th qury kywords. To avoid this, smoothing tchniqus ar oftn usd. Smoothing valus ar computd not with a documnt (lmnt) modl but with a background languag modl, which is applid for an ntir documnt st. BM25E is rgardd as a mor ffctiv trm-wighting schm than TF-IPF. Actually, most of th top-rankd sarch systms at INEX us BM25E [2]. Howvr, no xhaustiv comparison btwn BM25E and QLMER has bn xplord. W thrfor xamin th potntials of ths trm-wighting schms in this articl. 2) Efficint XML Sarchs: Although th most important rquirmnt of XML lmnt rtrival is nabling ffctiv sarchs, fast qury procssing is also rquird by systm usrs. To attain fficint XML lmnt rtrival, various approachs hav bn takn, such as: applying top-k algorithms to rturn sarch rsults quickly, and comprssing and rducing data to supprss th indx siz to minimiz th amount of data scannd in qury procssing. Many top-k sarchs hav bn proposd [6]. Thr ar two conditions for fficint qury procssing: 1) trm wights ar calculatd bfor qury procssing bgins, and 2) trms ar sortd in dscnding ordr of wight. This mans that w only nd to scan highly rankd trms in qury procssing. Not that som qury procssing mthods also utiliz an indx for a random scan, which is usd to rfr to th wight of an arbitrary trm in any lmnt. Som studis [20], [22] hav usd trm-wighting schms [12] for ffctiv sarchs. Thobald t al. proposd two typs of indics and a top-k algorithm for fficint sarchs [20]. On typ is for scoring an lmnt in qury procssing, and th othr typ is for chcking a structural constraint on a qury. Thy also proposd cost-basd qury procssing, which idntifis an ffctiv momnt to chck th structural constraints and dtrmins which qury kyword is rasonabl to procss. Trotman t al. proposd a low-cost mthod of data comprssion and slction [22]. In ths studis, th aim was to rtriv lmnts satisfying th information nd of a usr by rtriving lmnts from a fixd documnt st; i.. documnt updats wr not considrd. B. Handling Documnt Updats on th Wb Th handling of documnt updats is spcially important in Wb sarch systms bcaus documnts ar constantly insrtd, dltd, and modifid. Whn documnts ar updatd, usful sarch systms should trat thm as sarch targts immdiatly. If systms prsnt sarch rsults basd on a

4 past snapshot of th Wb, th contnt of th Wb documnts may sinc hav changd. Sarch systms should rflct th currnt stat of th Wb and handl dynamically changing Wb documnts. Rcntly, som tchniqus for handling documnt updats hav bn proposd. Chn t al. [1] tackld this challng in th fild of information xtraction. Thy rportd that a long procssing tim is rquird to apply information xtraction tchniqus to documnt collctions whn documnt updats occur. As a rsult, a dlay occurs bfor information xtractd from th updatd documnts is availabl. To shortn th dlay, thy proposd a mthod for rcycling th intrmdiat rsults of past snapshots. Numann t al. [17] also ffctivly utilizd th information of past snapshots, but with Rsourc Dscription Framwork (RDF) data. Rn t al. [19] prsrvd not only th latst graph data but also past snapshots to trac th transition of th graph. Our study is diffrnt from thirs to th xtnt that w prsnt th information along with th latst stat of th Wb. Th aformntiond studis utilizd th intrmdiat rsults of past snapshots. Hnc, w also utiliz thos or xisting indics. W incrmntally updat xisting indics whn nw documnts ar insrtd. In addition, Wb sarch systms ar xpctd to maintain high prformanc with a low updat cost. In th cas of txt rtrival, high sarch accuracy should also b maintaind. Thr has bn no adquat study focusd on incrmntal updats in XML lmnt rtrival with ffctiv and fficint qury procssing. Thrfor, this is th first study to tackl th problm. Although som rsarchs hav focusd on incrmntal updats of an invrtd indx [21], [10], [15], thy proposd indx data structurs of indics and physical storag mthods. Our study diffr from thir studis bcaus w introduc a function for incrmntal updats of indics for svral purposs in XML lmnt rtrival by proposing an fficint mthod of data managmnt. IV. FAST INCREMENTAL UPDATES OF INDICES Gnral XML lmnt rtrival systms [20] [22] hav functions for indx construction and qury procssing. Thy rbuild an indx from scratch whn documt updats occur. This mans it tals long tim to build a nw indx whn th amount of accumulatd documnts is larg. In contrast, our proposd systm has capabilitis for documnt insrtion, dltion, and modification to rflct documnt updats immdiatly. Morovr, w propos two filtrs which rduc updat cost for attaining fast incrmntal updats. A. Expansion of Existing Functions 1) Trm Wighting Schm: Th proposd systm us an arbitrary trm wighting schm. Basd on th discussion in Sction III-A1, w us ithr BM25E [12] or qury liklihood modl for XML lmnt rtrival [9] (QLMER). In th BM25E, a trm wight w bm25 (p,, t) of trm t in lmnt with path xprssion p is calculatd as follows: w bm25 (p,, t) = (1) (k 1 + 1)tf,t k 1 ((1 b) + b l log N p pf p,t avl p ) + tf,t pf p,t Trm (DID, EID, trm, trm wight, Path ID, lmnt lngth) - Tag-trm (DID, EID, tag, trm, trm wight, Path ID, lmnt lngth) - RS (DID, EID, trm, trm wight) - Path (Path ID, path xprssion) - GW-Path-trm (Path ID, trm, frquncy [, valus of back-ground languag modl for QLMER]) - GW-Path (Path ID, frquncy, total lngth) - Trm-filtr (tag, trm, thrshold valu) Fig. 4. Structur of th indics whr tf,t is th trm frquncy of trm t in lmnt, pf p,t is th lmnt frquncy of trm t in th lmnts with p, N p is th numbr of lmnts with p, l is th lngth of lmnt, and avl p is th avrag lngth of th lmnts with p. Th paramtrs k 1 and b ar st as commonly usd valus 2.5 and 0.85, rspctivly, basd on our past xprimnts. Morovr, s bm25 () is th scor of and is calculatd as follows: s bm25 () = t i T w bm25 (p,, t i ) (2) whr T is a st of qury kywords. Likwis, w show th way of trm culculation with QLMER. Though thr ar som variations of QLMER, our prvious study rports that th path xprssion-basd Dirichlt smoothing approach is th most ffctiv [9]. Lt w qlm (p,, t) b a probability that trm t is gnratd in lmnt (i.. a trm wight), and lt s qlm (p,, t) b a scor of lmnt. Ths ar calculatd as follows: w(p,, t) = ˆP ml (t M ) + µ ˆP ml (t, p M b ) l + µ (3) ˆP ml (t M ) = tf,t (4) l p ˆP ml (t, p M b ) = tf,t p l (5) s qlm () = ˆP (T M ) = t T w qlm (p,, t) (6) whr M is an lmnt modl for, M b is a background languag modl for p, µ is a givn paramtr. 2) Structurs of Indics: W show th structurs of th proposd indics in Figur 4. In many xisting studis, th trm wights stord in th indics ar calculatd bforhand, and structural constraints can b chckd with ths. Th proposd indics inhrit ths capabilitis but also contain global wights to calculat a trm wight immdiatly, which is ssntial for fast incrmntal updats. As in th rlatd studis [20], [22], th structurs of th indics ar dfind in an RDB format. Primary kys ar undrlind. Th Trm, th Tag-trm, th RS, and th Path

5 indics ar usd for fficint and ffctiv qury procssing as in othr studis. In th GW-Path-trm and th GW-Path indics, th global wights ar indxd for nabling fast trm calculation. In Eq. (1), (4), and (5), pf p,t, N p, avl p, and ˆP ml (t M b ) ar global wights. W discuss th Trm-filtr indx blow in Sction IV-C2. 3) Top-k Sarchs: Th proposd systm has a function for top-k sarchs [6] to nhanc its usability in fast qury procssing. To rturn sarch rsults, only th top k tupls ar rtrivd for ach trm (a pair of tag and trm for CAS quris). Th trm wights in th tupls ar summd for ach lmnt to calculat scors. Furthrmor, a wight in an arbitrary trm in any lmnt can b gaind with a random scan whn w nd to calculat xact scors for sarch rsults. W can attain not only fficint qury procssing but also ffctiv qury procssing with th random scan. A CO qury rtrivs th Trm indx whras a CAS qury rtrivs th Tag-trm squntially to xtract candidat sarch rsults in qury procssing. Accurat scors ar calculatd for lmnts by a random scan with th RS indx. Not that tupls in th Trm indx ar groupd by trm in dscnding ordr of trm wight, whras tupls in th Tag-trm indx ar groupd by pair of tag and trm. Whn a CAS qury contains two or mor structural constraints, th path xprssions of lmnts must b chckd to dtrmin whthr ths satisfy th qury constraints. B. Handling Documnt Updats 1) Documnt Insrtion: Whn a documnt is insrtd, th updating procss is conductd as follows: (1) xtracting lmnts from th insrtd documnt, (2) calculating trm wights for th lmnts, and (3) updating indics. First, th documnt is parsd and lmnts ar xtractd. As a rsult, lmnts 1 and 2 ar xtractd. Scond, th trm wights of t 1 and t 2 in 1, and t 1 in 2 nd to b calculatd. Trm wights ar calculatd immdiatly with th GW-Path-trm and th GW-Path indics. W only nd to stor all kinds of global wights in th indics whn using anothr trm-wighting schm rquiring othr statistics. Finally, th Tag-trm, th Trm, and th RS indics ar updatd incrmntally aftr th trm wights ar calculatd. Not that an ntir st of documnts can b updatd at onc to rduc th I/O cost. 2) Documnt Dltion: Whn a documnt is dltd, thr is a high cost to find and dlt all tupls rlatd to th documnt bcaus th tupls ar sprad across th indics. W thrfor tak anothr approach to rduc th cost of th dltion. W manag th DIDs of dltd documnts instad of dlting tupls in th indics. Thn, w simply ignor th tupls of th DIDs in qury procssing. With this approach, w can rflct th dltion immdiatly. Fig. 5. Vrsion list DID VID dltd indx DID EID VID modifid th vrsion list and qury procssing sarch rsults DID EID VID W prpar a vrsion list to manag th dltd documnts. Th list contains pairs. Each pair contains th DID of th dltd documnt and th vrsion idntifir (VID) with its valu markd as. W ovrwrit th VID as whn th DID of th dltd documnt is containd. Spcifically, Documnt 100 in Figur 5 has bn dltd bcaus th VID of Documnt 100 is. Th tupls of documnts dltd in th indics ar liminatd whn th load avrag is low. Aftr liminating th tupls, th DIDs of th documnts dltd in th vrsion list ar also liminatd. 3) Documnt Modification: Th modification procss is achivd through th dltion and insrtion procsss. In mor dtail, w dlt all tupls rlatd to th modifid documnt and insrt th latst vrsion of th documnt to handl th modification. W also utiliz th vrsion list to manag th vrsion of th documnt, bcaus thr is a high cost to dlt th tupls of a modifid documnt immdiatly. To nabl fast modification, w only targt th tupls of th latst vrsion in qury procssing. Not that th granularity of modification is documnt granularity, bcaus som problms aris with lmnt granularity. On of th problms is th siz of th vrsion list. Th ovrhad in qury procssing bcom gratr whn w manag not documnts but lmnts. Anothr problm is th difficulty in mapping old structur to nw structur whn th documnt structur changs. Ths ar th rasons that w adopt th documnt as th granul of modification. Th modification procss is conductd as follows: first, whn a modification occurs, th vrsion list is scannd to dtrmin whthr th DID of th modifid documnt is containd. If th DID xists in th vrsion list, 1 is addd to th VID; othrwis, th DID of th modifid documnt and its VID valu of 1 ar insrtd. For xampl, Documnt 101 in Figur 5 has gon through modification twic bcaus its VID is 2. Scond, th Trm, th Tag-trm, and th RS indics ar updatd in th sam mannr as for documnt insrtion. As shown in Figur 4, ach tupl contains a VID whos valu is th sam as that writtn in th vrsion list. Not that th VID of th first documnt insrtd is 0. Finally, ach tupl is chckd to dtrmin whthr th tupl is valid in qury procssing basd on th VID. Th tupl is th latst whn th VID of th tupl is th sam as that of th modifid documnt in th vrsion list. Morovr, th tupl is also th latst whn th DID of th modifid documnt is

6 not containd in th vrsion list. In contrast, th tupl is invalid whn th VID of th tupl is smallr than that of th modifid documnt in th vrsion list. W giv a spcific xampl of th validation chck in Figur 5. Th DID of th first tupl in th indx is 103, and th vrsion list dos not contain that DID. Thus, th first lin is valid. Th documnt of th scond tupl has bn dltd, bcaus th DID of this tupl is containd in th vrsion list and its VID is. Th third tupl is th latst, bcaus th VID of this tupl is th sam as th VID corrsponding in th vrsion list to th Documnt 101. Similarly, th fourth tupl is not th latst, bcaus th VID of this tupl is lss than that of th VID corrsponding to th Documnt 101. Old vrsions of tupls in th indics ar rmovd whn th load avrag is low. In this rgard, th VID of th latst vrsion of a tupl is rwrittn as 0, and th DID of th dltd documnt is rmovd from th vrsion list. C. Filtrs for Rducing Updat Cost W propos two kinds of filtrs for slcting important lmnts and trms to indx. It is obvious that w can rduc updat cost with ths filtrs. Howvr, sarch accuracy will b rducd if w rmov lmnts and trms rlvant to any qury. This would violat th first rquirmnt. To avoid a dcras in sarch accuracy, w should dcid carfully which lmnts and trms can b rmovd. 1) Elmnt Filtr: W propos an lmnt filtr to rmov unncssary lmnts. W prviously proposd a mthod to rmov lmnts that cannot b th sarch rsults of any qury [3] and a mthod to idntify th most appropriat granularity for sarch rsults [8]. Thos studis ld to th fact that modrat granuls ar th most appropriat as sarch rsults, bcaus xtrmly larg granuls (.g. whol documnts) tnd to contain irrlvant dscriptions and xtrmly small granuls cannot satisfy th information nd by thmslvs. Hrinaftr, w attmpt to rmov xtrmly small lmnts, sinc idntifying ths is asir. It is ssntial to dfin what xtrmly small lmnts ar. Many of th Wb documnts includ tabl-of-contnts or rfrnc information, which basically consists not of sntncs but of kywords. Ths dscriptions cannot satisfy an information nd dirctly, although thy can srv as navigational information. Sinc on rquirmnt for txt summarization is that information should b slf-containd [14], w rmov any lmnt that cannot b undrstood by itslf. Basd on th discussion abov, w dfin thr conditions of xtrmly small lmnts as follows: (1) lmnts containing fw trms (thrshold τ l ), (2) lmnts with dp path xprssions (thrshold τ dpth ), and (3) lmnts with rar path xprssions (thrshold τ Zipf ). Rgarding th first condition, th trms in th information othr than th body txt including tabl-of-contnts and rfrnc information, contain fw trms also in th lmnts. Actually, study [3] rports that sarch accuracy improvs whn short lmnts ar rmovd. Fig small trms 2 dp path 3 4 rarly apparing path l m n t filt r trm calculat ion - GW-Path-trm - GW-Path Th lmnt filtr and th trm filtr thrshold valu : 0.5 t 1 :1.0 t 2 : 0.3 t 3 : RS t r m filt r - Trm - Tag-trm In th scond condition, lmnts with dp path xprssions ar liminatd. Tabls or lists in HTML hav a tndncy to b nstd dply. Th valu of ach cll is nonsns without furthr information, which is th rason that w rgard ths lmnts as irrlvant. Rgarding th third condition, path xprssions that rarly appar in th documnt st cannot b calculatd accuratly, as discussd abov in Sction V-B. W thrfor us Zipf s law [16] to obtain th thrshold of mdian frquncy f, which is computd as follows: 8F f = (7) 2 whr F 1 is numbr of th path xprssions apparing only onc in th documnt st. To rtain sarch accuracy, w sk appropriat thrsholds to rmov only irrlvant lmnts. Prliminary xprimnts on th lmnt filtr ar dscribd in Sction VI-B. Figur 6 illustrats th bhavior of th lmnt filtr. Suppos that four lmnts, 1, 2, 3, and 4, ar xtractd from insrtd documnts. Elmnts 1, 2, and 4 ar liminatd by th lmnt filtr bcaus 1 is too short, th path xprssion of 2 is too dp, and th path xprssion of 4 rarly appars. As a rsult, only 3 is chosn as a targt. 2) Trm Filtr: Although thr ar many candidat sarch rsults, only a fw lmnts ar prsntd as sarch rsults. Thrfor, w suppos that sarch accuracy is not significantly affctd if indics do not contain trms with low wights. Basd on this ida, w rmov th unimportant trms with th trm filtr. Th thrsholds τ tw ar dfind as th trm wights of th nth largst trm for ach pair of tag and trm containd in th indics. Ths valus ar stord in th Trm-filtr indx so that thy can b lookd up quickly. Not that w apply th trm filtr only to th Trm and Tag-trm indics to nabl accurat calculation of th scor for lmnts with th RS indx. In addition, w do not apply th filtr whn th numbr of tupls of th pair of tag and trm is lss than n. Figur 6 shows an xampl of how th trm filtr works. Suppos that τ tw is 0.5 and thr ar thr trms to insrt into th Tag-trm, th Trm, and th RS indics. W us th singl valu of τ tw for simplicity although τ tw diffrs for ach

7 pair of tag and trm. Trms t 1 (1.0 > τ tw ) and t 3 (0.8 > τ tw ) ar succssfully indxd with th Trm, th Tag-trm, and th RS indics bcaus thy ar gratr than τ tw. In contrast, trm t 2 (0.3 < τ tw ) is only indxd with th RS indx bcaus it is lss than τ tw. V. ESTIMATING ACCURATE GLOBAL WEIGHTS A. Effcts of Incrmntal Updats W xamin th ffctivnss and fficincy of incrmntal updats of indics. 1) Tst Collction and Implmntation Sttings: In th xprimnts, w usd th INEX 2008 tst collction providd by th INEX projct 2. This tst collction consists of thr componnts: (1) th INEX documnt collction, (2) th INEX topics, and (3) th INEX rlvanc assssmnts. Th INEX documnt collction is an XML Wikipdia corpus basd on a snapshot of th English vrsion of Wikipdia. Approximatly 660,000 articls ar in this corpus. Th INEX topics includ 68 quris, of which 32 ar CO quris and 36 ar CAS quris. W usd all of ths in th xprimnts. Th INEX rlvanc assssmnts ar th valuations of th quris to masur th ffctivnss of XML lmnt rtrival systms. In this tst collction, at most 1,500 lmnts ar prsntd as sarch rsults for ach qury. In th INEX projct, th intrpolatd prcision at th rcall lvl of 1% (ip[.01]) is usd as a formal masur of accuracy. Th valuation tool also masurs th man avrag intrpolatd prcision (MAiP) as th avrag prcision at 101 rcall lvls. Th PC that w usd for th xprimnts runs Oracl Entrpris Linux 5.5. It has four Intl Xon X7560 CPUs (2.3GHz), 512GB of mmory, and a 4.5TB disk array. Th indics wr implmntd using BrklyDB in GNU C++. 2) Exprimntal Procdur: W dfin an indx bfor incrmntal updats tak plac as an initial indx. W distinguish btwn documnts usd to construct initial indics (initial documnts) and documnts usd to updat indics (updat documnts). Hr, w assum that th statistics of th documnts ar static, i.., th statistics of th initial documnts and th updat documnts ar th sam. For this purpos, w randomly sampld documnts in ordr to distinguish btwn thm. In Sction VI-D, w considr a mor complx cas in which th statistics of th documnts chang dynamically. All documnts ar procssd through th stop-word and stmming stps bfor th construction of th initial indics bgins. Th procdur is as follows: first, th initial documnts ar parsd to calculat trm wights and th initial indics ar constructd; thn, th updat documnts ar obtaind for updating indics incrmntally. All data in th GW-Path-trm and th GW-Path indics ar scannd in th main mmory during updats. Thn, th updat documnts ar parsd and th Trm, th Tag-trm, and th RS indics ar updatd incrmntally. 2 TABLE I. ACCURACIES OF TERM WEIGHTING SCHEMES BM25E QLMER (µ) ip[.01] ) Choosing a Trm-wighting Schm: W conductd a prliminary xprimnt to choos a trm-wighting schm usd in latr xprimnts. W xamind th ffctivnss of BM25E and QLMER to ascrtain which trm-wighting schm is mor accurat on. Tabl I indicats that BM25E is mor ffctiv trmwighting schm. Hnc, w usd this in th latr xprimnts. 4) Evaluation Rsults: W invstigatd sarch accuracis, updat fficincy pr documnt, and total tim of indx construction by changing th prcntag of initial documnts within th documnt st, as indicatd in Tabl II. For xampl, whn th ratio is 30%, th initial indics ar constructd using 30% of th documnts in th st, and th indics ar updatd using th rmaining 70% of th documnts. Whn th ratio of initial documnts is 100%, updats of th indics do not tak plac (no-updat). Tabl II shows that incrmntal updats rduc sarch accuracy, which dmonstrats that global wights cannot b computd accuratly using only a subst of th documnts. To mak th incrmntal updat practical, w nd to solv th problm of inaccurat global wights. Th avrag tim for incrmntal updats is 53.4 ms pr documnt whn th ratio of initial documnts is 50%, whras th tim rquird to construct indics from scratch (no-updat) is 42.1 ms pr documnt. This suggsts that th updat fficincy dcrass as th ratio of initial documnts incras. As a rsult, indxing may tak a long tim whn w updat a numbr of documnts. B. Intgrating Path Exprssion for Accurat Global Wights W attmpt to calculat global wights accuratly using a limitd numbr of documnts. Sinc ths ar calculatd within lmnts having th sam path xprssion, w cannot obtain appropriat statistics for a path xprssion apparing rarly in th documnt st. W thrfor considr a mor ffctiv approach. Spcifically, w intgrat path xprssions having a similar proprty to xpand th lmnts in th sam class. To accomplish this, w utiliz th mthod proposd in our prvious study [7] for intgrating path xprssions. This intgration mthod calculats an accurat global wight for a path xprssion of fw frquncis. Th currnt cas is similar to that in th prvious study. In both cass, th global wights of lmnts with rar path xprssions ar not calculatd accuratly. Thrfor, th intgration mthod should improv th rsults. To intgrat path xprssions, w rgard a path xprssion as an array of tags and idntify th path xprssions that ar similar to ach othr in trms of th apparanc ordr and apparanc frquncis of tags. As a rsult of th intgration, w liminat classs that do not contain nough lmnts to calculat accurat global wights.

8 TABLE II. THE RESULTS OF THE SIMPLE APPROACH ratio of th initial ip[.01] MAiP updat tim total tim of documnts (%) (ms/doc) indx construction (h) from-scratch : /articl/sc 2: /articl/sc/sc 3: /articl/sc/mp/sc 4: /articl/mp/sc 5: /articl/mp/sc/sc articl: 1, sc: 1 articl: 1, sc: 2 articl: 1, sc: 2, mp: 1 1: /articl/sc 2: /articl/sc/sc 3: /articl/sc/mp/sc 5: /articl/mp/sc/sc Fig. 7. Exampls of path xprssions articl: 1, sc: 1, mp: 1 4: /articl/mp/sc Fig. 8. articl sc articl sc mp 1: /articl/sc 2: /articl/sc/sc 3: /articl/sc/mp/sc 4: /articl/mp/sc 5: /articl/mp/sc/sc An xampl of classification in ST Fig. 9. An xampl of classification in BT /articl+/sc+ /articl+/sc+/mp+/sc+ /articl+/mp+/sc+ 1: /articl/sc 2: /articl/sc/sc 3: /articl/sc/mp/sc 4: /articl/mp/sc 5: /articl/mp/sc/sc In addition, th cost to adopt ths mthods is small, bcaus ths approachs simply calculat a frquncis and chck th ordr of tags in a path xprssion. W can ignor th harmful ffcts on updat fficincy. W now xplain thr intgration mthods: 1) st-of-tags mthod (ST), 2) bag-of-tags mthod (BT), and 3) ordr-of-tags mthod (OT). 1) St-of-Tags Mthod (ST): Tags in structurd documnts ar sparatd into two groups. On rprsnts structural classifications such as articl and sc tags. Th othr indicats smantics, idas, attributs, and spcific contnts such as prson, mp, and tabl tags. Ths two groups of tags ar supposdly indpndnt in thir apparanc. This suggsts that a combination of tags can gnrat two or mor path xprssions. It is not always appropriat that ths path xprssions ar placd into diffrnt classs. This is why w focus on rlaxing th apparanc ordr and frquncis of tags in path xprssions to intgrat similar path xprssions. Th st-of-tags (ST) mthod rlaxs both th apparanc ordr and frquncis of tags in path xprssions. Accordingly, w considr only th nams of th tags. W classify path xprssions composd of th sam tag nams as mmbrs of th sam class. Classification of th path xprssions in Figur 7 is shown in Figur 8. Th first two path xprssions ar in th sam class bcaus thy ar both composd of articl and sc tags, whil th othr thr path xprssions ar in th sam Fig. 10. An xampl of classification in OT class bcaus thy ar all composd of articl, sc, and mp tags. Th global wights of th lmnts with th first two path xprssions ar calculatd togthr, and th global wights of th lmnts with th othr path xprssions ar calculatd togthr. 2) Bag-of-Tags Mthod (BT): Th bag-of-tags (BT) mthod rlaxs only th apparanc ordr of tags in path xprssions. W do not considr th ordr of tags from th prspctiv of th bag-of-words concpt. Classification of th path xprssions in Figur 7 is shown in Figur 9. W first numrat th nams and frquncis of tags in ach path xprssion to intgrat th path xprssions classifid as mmbrs of th sam class. As a rsult, w intgrat th third and fifth path xprssions bcaus both hav on articl, two sc, and on mp tags. 3) Ordr-of-Tags Mthod (OT): Th ordr-of-tags (OT) mthod rlaxs only th apparanc frquncis of squntial tags in a path xprssion. In som path xprssions, a tag appars conscutivly two or mor tims; for xampl, col tags in tabl of HTML. In this cas, vn if th frquncis of a tag apparing conscutivly ar diffrnt, w suppos that th faturs of a path xprssion ar not much diffrnt bcaus th smantics of ach tag ar fixd. Thrfor, if conscutiv tags ar th sam, such tags can b aggrgatd. Classification of th path xprssions in Figur 7 is shown in Figur 10. Not that sc tags appar conscutivly in th scond and fifth path xprssions. Th first and scond path xprssions ar intgratd, bcaus ths hav hav on or

9 TABLE III. ACCURACIES WITH CHANGING τ l τ l ip[.01] TABLE V. EFFECTS OF THE TERM FILTER WITH CHANGING n n no filtr ip[.01] TABLE IV. DEPTH OF PES AND THE RATIO OF ELEMENTS τ dpth All Top mor articl tags followd by on or mor sc tags. Th fourth and fifth path xprssions ar also intgratd, ths hav on or mor articl tags followd by on or mor mp tags, and on or mor sc tags. VI. A. Exprimntal Dsign EXPERIMENTAL EVALUATIONS With th simpl incrmntal updat systm as th baslin, w invstigat whthr th applying two filtrs and intgrating path xprssions ar ffctiv for sarching accuratly and fficint for updating th indics or not. Th xprimntal nvironmnt is th sam as that usd in Sction V. Th proposd mthods ar valuatd using two documnt sts; on with static statistics, which mans that its topics rarly chang; and th othr with dynamic statistics, which mans that nw topics ar rgularly addd. Our proposd approachs admit som variations. Thr ar four ways of calculating global wights: th dfault mthod, which is classification basd on path xprssion; th stof-tags mthod (ST); th bag-of-tags mthod (BT), and th ordr-of-tags mthod (OT). Thr ar thr paramtrs in th lmnt filtr: th lmnt lngth thrshold τ l, th path dpth thrshold τ dpth, and Zipf s thrshold τ Zipf. By xamining th ffctivnss of ach approach, w can choos th bst stting. In our xprimntal procdur, w first ran som prliminary xprimnts to tun th paramtrs of th lmnt filtr and trm filtr. Nxt, with ths tund paramtrs, w masurd th avrag updat tim pr documnt, th indx siz, and th sarch accuracy for ach variation of th proposd mthods. W usd th documnt st with static statistics and chos 50% as th ratio of initial documnts. Finally, w confirmd th ffctivnss of th proposd mthods by using th documnt st with dynamic statistics. B. Prliminary Exprimnts for th Elmnt Filtr and Trm Filtr Th lmnt filtr liminats th lmnts that hav xtrmly short lmnts, xtrmly dp path xprssions, and rarly apparing path xprssions. In this sction, w dscrib som xprimnts that w conductd to dcid th thrsholds. According to th rsults listd in Tabl III, w st τ l to 35; namly, in trms of sarch accuracy, th bst valu for th lmnt-lngth thrshold is 35. Tabl IV shows th proportion of lmnts whos dpth of path xprssions is lss than or qual to τ dpth. W masurd th proportion for all lmnts in th tst collction and for only thos highly rankd lmnts obtaind in our prvious TABLE VI. EFFECTS OF THE PROPOSED APPROACHES updat tim disk run ID (ms/doc) siz(gb) ip[.01] MAiP no-updat (42.1) baslin ST BT OT τ l τ dpth τ Zipf lm filtr trm filtr two filtrs study [8]. Thr is a diffrnc btwn th rsult for all lmnts and that for highly rankd lmnts. This indicats that w can xtract purly usful lmnts if th dpth thrshold is st to xtract as many highly rankd lmnts as possibl and to discard uslss lmnts. W st τ dpth to 6, which ignors any lmnt whos dpth is six or mor. W also invstigatd th thrshold of Zipf s law. W computd mdian frquncy of th path xprssions by using Eq. 7. W st τ Zipf to 166, which ignors any lmnt whos path xprssion appars 166 or fwr tims in th initial indx. In analogy with th lmnt filtr, th trm filtr liminats trms whos wights ar blow th thrshold. W conductd an xprimnt to dcid th thrshold for th trm filtr, as shown in Tabl V. W st n to or τ tw, which ignors th trms whos wights ar lss than th 10,000th largst wight of ach pair of tag and trm. C. Evaluations of th Documnt St with Static Statistics W masurd th avrag updat tim pr documnt, th siz of indics, and th sarch accuracy with ach variation of th proposd mthods, as indicatd in Tabl VI. Not that in th cas of no-updat, or constructing a nw indx from scratch, th avrag updat tim rplacs th construction tim of th initial indics. Not that lmnts whos lngth is lss than τ l ar rmovd from all rsults, vn thos of no-updat. Compard with th ip[.01] of th baslin systm, thos of ST, BT, and OT ar improvd. In particular, ST is th most ffctiv mthod for calculating accurat global wights and is 2.57% mor accurat than th baslin. In addition, th updat fficincis of ths mttjhods ar almost qual. All componnts of th lmnt filtr (i.. τ l, τ dpth, and τ Zipf ) sav updat cost without rducing sarch accuracy. Th combination of τ l, τ dpth, and τ Zipf is th most ffctiv of all possibl combinations and yilds 23.6% fastr updats than th baslin approach. W usd this stting for th lmnt filtr in th subsqunt xprimnts. Th trm filtr also rducs th updat cost by 6.70% without sacrificing sarch accuracy compard with th baslin approach. Nxt, w valuatd th combination of th two filtrs. This approach prforms bttr than ithr of singl filtrs in trms

10 TABLE VII. CATEGORY AND QUERY Catgory nam CQ CW Tchnology and applid scincs Cultur and th arts Natural and physical scincs 9 24 Socity and social scincs 4 13 History and vnts 4 11 Philosophy and thinking 3 8 Gnral rfrnc 3 7 Halth and fitnss 2 7 Popl and slf 3 6 Gography and placs 2 5 Mathmatics and logic 0 0 Rligion and blif systms 0 0 mchan, mtadata, min, motor, musum, ntwork, nikola, opn, opr, park, patnt, program, raid, rcord, rtriv, rotari, scur, social, sourc, storag, systm, tata, tsla, virtual, wirlss CW of cultur : acquisit, africa, al, basktbal, brbr, bilingu, childbirth, childrn, classic, countri, cultur, danc, dish, urop, uropan, fiction, film, food, franc, gam, guitar, hors, instrumnt, japans, kyboard, languag, mahlr, musum, nba, north, prson, picasso, playr, portugus, produc, rgion, rul, scinc, scrabbl, song, spanish, styl, symphoni, tap, tast, trracotta, tradit, typic, vgtarian, vodka, win of both updat fficincy and sarch accuracy. Th updat fficincy is improvd by 26.3%. Th formr xprimnts showd that th sarch accuracy improvd with th path xprssion intgrating mthod and th updat fficincy improvd with two filtrs. Thn, w combind ST and th two filtrs as ST filtrs. Th sarch accuracy improvd by 3.73% compard with th baslin, whil th updat fficincy improvd by 24.9%. In trms of qury fficincy, ach mthod taks 1.5 s to 2.0 s pr qury. This should b accptabl for usrs. Finally, w can attain fast incrmntal updats of indics with an ffctiv and fficint sarch. D. Evaluations of th Documnt St with Dynamic Statistics In th prvious valuations, w assumd that th trm distribution and trm statistics ar static. Howvr, nw topics can mrg suddnly on th Wb and may chang th trm distribution drastically. Hr w artificially assmbl a documnt st with dynamic statistics to invstigat th ffctivnss of th proposd mthods. In this st, th initial documnts do not includ a crtain topic but th updatd documnts do includ th topic. W outlin th stps to valuat as follows: (1) idntify documnts on a crtain topic, (2) construct th initial indx using th othr documnts, and (3) updat th indics incrmntally using th documnts rlatd to th topic. W utilizd th catgoris in Wikipdia to judg whthr a documnt blongs to a crtain topic. Wikipdia has many catgoris of various sizs: twlv major catgoris ar listd in Tabl VII. W sparatd 68 quris into th twlv catgoris. Each qury contains from on to fiv qury kywords, and w obtaind a kyword st for ach catgory. Sinc th catgoris Tchnology and applid scincs (tchnology for short) and Cultur and th arts (cultur for short) includ rlativly larg numbrs of quris (catgory quris, or CQs) and qury kywords (catgory kywords, or CW), w usd ths catgoris in th valuation. W assignd a documnt to a crtain catgory if th documnt contains th catgory kywords. Not that ths catgory kywords ar stmmd. CW of tchnology : aircraft, applid, automobil, aviat, bay, bltchli, brak, car, cod, colossu, compani, comput, databas, dtct, ngin, xprt, fil, filtr, format, graphic, imag, inform, instal, intrus, invnt, java, languag, linux, manag, W usd th catgory quris only to xamin th ffctivnss of th proposd mthods, bcaus w focus on th ffcts of trm distributions with dynamically changing statistics. In this situation, w assumd that usrs xpct an ffctiv sarch to b availabl as soon as nw topics ar addd to th collction. Th numbrs of documnts in th initial indics of tchnology and cultur ar 280,000 and 200,000, rspctivly. W valuatd th ffcts of th changing statistics at four points during th updats. Aftr th updats, th numbr of indxd documnts rachd 660,000 for both catgoris. Tabl VIII lists th ip[.01] of ach catgory for th baslin and ST filtrs. For both catgoris, th proposd mthods attaind bttr sarch accuracis than th baslin. In particular, ST filtrs incrasd th sarch accuracis rapidly vn whn th numbr of updat documnts was small. VII. CONCLUSION In this papr, w proposd mthods for fast incrmntal updats of indics for XML lmnt rtrival to attain both ffctivnss and fficincy in th qury procssing. Th simpl solution for incrmntal updats has two problms: (1) dcrasd sarch accuracy, and (2) incrasd updat tim. W solvd ths problms by intgrating path xprssions and utilizing two filtrs for xcluding unncssary data. Th xprimntal valuations showd that our proposd approachs ar ffctiv and fficint for both static statistics and dynamic statistics. In particular, a variation of th proposd approachs can rduc updat tim by 25% whil th sarch accuracy improvd by 4% compard with th simpl incrmntal updat systm for static statistics. REFERENCES [1] Fi Chn, Xixuan Fng, Christophr Ré, and Min Wang. Optimizing Statistical Information Extraction Programs Ovr Evolving Txt. 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