Web Object Indexing Using Domain Knowledge *

Transcription

1 Web Object Indexng Usng Doman Knowledge * Muyuan Wang Department of Automaton Tsnghua Unversty Bejng , Chna (86-10) Zhwe L, Le Lu, We-Yng Ma Mcrosoft Research Asa Sgma Center, Hadan Dstrct Bejng , Chna (86-10) [email protected] {t-zl, llu, wyma}@mcrosoft.com Nayao Zhang Department of Automaton Tsnghua Unversty Bejng , Chna (86-10) [email protected] ABSTRACT Web object s defned to represent any meanngful object embedded n web pages (e.g. mages, musc) or ponted to by hyperlnks (e.g. downloadable fles). Users usually search for nformaton of a certan object, rather than a web page contanng the query terms. To facltate web object searchng and organzng, n ths paper, we propose a novel approach to web object ndexng, by dscoverng ts nherent structure nformaton wth doman knowledge. In our approach, Layered LSI spaces are bult for the herarchcally structured doman knowledge, n order to emphasze the specfc semantcs and term space n each layer of the doman knowledge. Then, the web object representaton s constructed by hyperlnk analyss, and further pruned to remove the noses. Fnally, the structure attrbutes of the web object are extracted wth the knowledge document that best matches the web object. Our approach also ndcates a new way to use trust-worthy Deep Web knowledge to help organze dspersve nformaton of Surface Web. Categores and Subject Descrptors H.2.8 [Database Management]: Database Applcatons Data Mnng; I.7.m [Document and Text Processng]: Mscellaneous; H.3.3 [Informaton Storage and Retreval]: Informaton Search and Retreval Clusterng, Selecton Process. orented search engne s requred. Consequently, t s expected that some ntegral technque could be developed to ndex the web objects. In ths paper, web objects are defned to represent those meanngful objects embedded n web pages (e.g. mages), or ponted to by hyperlnks (e.g. song streamng, downloadable fles). Usually, the surroundng texts (ncludng anchor text) can prelmnarly descrbe a web objects. Complementary nformaton of the web object may be also possble presented n the neghborng pages that have hyperlnks among them. Fgure 1 llustrates two examples of web object, wth or wthout descrptons n the surroundng texts. Fgure 1(a) stands for a book object, wth a lttle valuable nformaton n the surroundng text. The nformaton about ts author, publsher, and the ntroducton of the author are found n the pages hyperlnked wth t. Fgure 1(b) llustrates a song object wth the descrptons of ts contanng album and performer n the neghborng pages. In a broad sense, almost everythng on the web can be regard as some knd of web object, ncludng those vrtual objects or concepts descrbed n web pages (e.g. a musc revew or book revew). General Terms Algorthms, Performance, Expermentaton. Keywords Web object, ndexng, doman knowledge, latent semantc ndexng, lnk analyss, confdence propagaton, nformaton retreval 1. INTRODUCTION Contemporary web search engnes are manly page orented, that s to say, ther ndexng granularty s web page. As a result, they are only able to provde search results n the form of ranked web pages wth respect to user's query. However, n many cases, the users want to search for nformaton of a certan object rather than the web page. For example, users may use query artst Beatles to search for the bography about Beatles band, ther albums and songs, nstead of the web pages that contan the query terms only. To meet such nformaton need, an object Copyrght s held by the author/owner(s). Conference 05, Month 1 2, 2005, Cty, State, Country. Copyrght 2005 ACM /00/0004 $5.00. Fgure 1. Some examples of web object 1

2 Web objects usually le n some mplct structure organzatons. For example, song object can consttute a layer n the herarchcal structure of artst-album-song, as Fgure 2 shows. Structured organzaton of web objects provdes a good drectory to facltate user to browse web resources dstrbuted dspersvely. Fgure 2 Herarchcal organzaton of a musc database, whch ncludes three layers: artst, album and song. In some Deep Web stes, each node s a web page; relatonshps between them are presented by hyperlnks. To facltate web object searchng and organzng, n ths paper, we propose a general framework to ndex web objects, and further, organze them based on ther mplct structure. To acheve such an objectve, we should solve two major dffcultes, 1. Lack of nformaton. Usually, there s lttle descrptve textual nformaton around web objects. The nformaton may be nosy and nsuffcent to represent web objects. It s crtcal to enrch the descrpton of the target web object and prune the nosy nformaton. 2. Dffcult to dentfy structure. Even f the descrpton of a web object s suffcent and extracted, t stll leaves a dffcult problem to extract the structure nformaton of the web object. Automatc dscovery of mplct structure s very challengng. Although some herarchcal clusterng methods are proposed to detect semantc structure, the obtaned herarchy s usually not meanngful to the target web object that s doman specfc [15]. t wll also meet some tradtonal obstacles lke polysemy and synonymy due to varous word usages n dfferent web pages. Moreover, the tradtonal wrapper-based nformaton extracton approaches are not sutable n these cases, snce a wrapper s usually sutable to only one knd of web pages but could not adapt to all knds of web pages, whch are usually dverse n ther desgn format. To deal wth these problems, we propose a novel non-clusterng and non-wrapper approach to ndex web objects n varous web pages, by ntegratng lnk analyss and doman knowledge. Hyperlnk analyss s used to enrch the textual descrpton of the web object; and doman knowledge s used to help nose removng and structure attrbutes constructon of the domanspecfc web objects. Currently, many doman knowledge databases are avalable and well organzed n Deep Web stes, such as All Musc Gude (for musc) [8] and Amazon (for books), where the doman knowledge s usually organzed herarchcally, and each node s descrbed by an ndvdual web page. These databases provde meanngful and doman-specfc structures for web objects, and thus can be greatly helpful n web object ndexng. Wth herarchcal doman knowledge, web objects can be ndexed usng the structure nformaton contaned n the knowledge document that best matches the web object. From ths pont of vew, our proposal provdes a promsng way to use trust-worthy Deep Web nformaton to help nformaton organzaton of Surface Web. The rest of ths paper s organzed as follows: Secton 2 presents the proposed framework. Secton 3 descrbes our layered ndexng scheme for herarchcal doman knowledge. Secton 4 proposes our approach to web object representaton through text and lnk analyss. Secton 5 presents the matchng process between web objects and the documents n knowledge database. Fnally, the proposed approach s evaluated n Secton 6, and an example applcaton n musc doman s presented n Secton 7. Related work and conclusons are gven n Secton 8 and FRAMEWORK Fgure 3 llustrates our proposed framework for web object ndexng. It s manly composed of three steps: knowledge space buldng, web object representaton, and web object ndexng. Step One: Knowledge Space Buldng. In the frst step, the doman knowledge s ndexed by Latent Semantc Indexng (LSI), for further use n web object representaton and dentfcaton. A knowledge database s usually herarchcal or tree-lke (as shown n Fgure 2), where dfferent layers have dfferent semantcs. Therefore, t s a better choce to represent knowledge layer by layer. Thus, n ths step, knowledge s ndexed n each layer respectvely, and Layered LSI spaces are bult for the doman knowledge. Step Two: Web Object Representaton. In ths step, textual nformaton of the web object s extracted and pruned. Frstly, a prelmnary neghborhood graph s constructed around target web object wth hyperlnk analyss, n order to enrch the descrpton of the web object. Then, the content of each web page s projected to the Layered LSI spaces of doman knowledge, to remove the rrelevant words to the web object. In order to obtan a better web object representaton, neghborhood graph s further pruned to remove nosy pages. After prunng, all web pages n the pruned neghborhood graph are combned together to get a new descrpton vector of the target web object. Step Three: Web Object Indexng. The major task n ths step s to dentfy the structure attrbutes of target web object based on ts descrpton vector and doman knowledge. To accomplsh ths task, the smlarty between the descrpton vector of web object and each knowledge document s frstly measured n the Layered LSI spaces; and then a process of structure-based confdence propagaton s performed to select the knowledge document whch best matches the web object. The correspondng structure attrbutes n the knowledge document are then used to ndex the web object. Although the proposed framework emphaszes on utlzng the doman knowledge and the correspondng herarchcal structure, our approach s stll feasble for non-herarchcal structure, whch can be consdered as a specal case of herarchcal structures, wth the layer number equal to one. In ths smplfed case, our approach becomes equvalent wth the method proposed n [4]. * Ths work was performed when the frst author was a vstng student at Mcrosoft Research Asa 2

3 matrx nstead of term frequency only. smplest TFIDF formula s employed, as: In our approach, the N TFIDF( w) = f ( w) log (1) D( w) where w denotes a term, f(w) represents the term frequency, N s the dmenson of term space, and D(w) stands for the set of documents that contan term w. Supposng there are c layers n knowledge database, and the weghted term-document matrx n each layer s denoted as A, ( =1,,c), each matrx can be decomposed by SVD as T ΣV A = U (2) Then, largest k sngular values are selected to construct the latent semantc structure of A, denoted as A : k k k k T Σ V k A = U (3) Thus, each document (or query) q can be represented as k T U 1 k q n the new semantc space. Fgure 3 The framework of two-step ndexng engne It s noted that, n ths paper, we manly focus on web objects ndexng, and assume web objects have been detected. In practce, web object detecton s usually doman specfc and thus can be solved wth some smple heurstc rules. 3. KNOWLEDGE SPACE BUILDING Both web object descrptons and knowledge database are textual, but usually are authored by dfferent authors. Ths fact leads to ther dfferent term spaces. Consequently, drectly usng doman knowledge to dentfy web object s not feasble. In order to solve ths problem, n our approach, Layered Latent Semantc Indexng s used to ndex documents n the knowledge database. LSI s capable to deal wth polysemy and synonymy problems n some degree, as ndcated n many textual retreval applcatons [6]. 3.1 Layered LSI of Knowledge Database Usually, each layer of the herarchcally structured knowledge represents dfferent concept, and has dfferent term space. For nstance, the three layers n musc doman knowledge represent the concepts of artst, album and song, respectvely; and the term Beatles s obvously more probable to appear at the artst layer, but not at the layer of album or song. Moreover, the scale of the term space of each layer s also dfferent. Thus, f all layers are ndexed together nto one LSI space, the layer wth smaller term space wll be overwhelmed by the larger one. That means the term mportance n the correspondng layer wll be unfarly reduced. Therefore, n our approach, we ndex each layer ndependently and thus compose Layered LSI spaces. In our approach, the textual content contaned n each record or page of the knowledge database s consdered as a knowledge document. These documents are tokenzed to construct a termdocument co-occurrence matrx. To mprove the effectveness of LSI, TFIDF s used as the weghtng functon n the co-occurrence 3.2 Dscusson Doman knowledge wll be further used n the web object representaton and web object ndexng. In these modules, all web pages (descrbng the target web object) are mapped to the Layered LSI spaces, n order to remove rrelevant words that are out of doman. As mentoned above, each layer of doman knowledge has ts unque semantc and term space. Thus, when we project each page nto the LSI space of one layer, n some degree, only the words that are accord wth the correspondng semantcs are amplfed; whle other rrelevant words are suppressed or removed. Therefore, n the further processng, the smlarty measure between two web pages or between web object and knowledge document, s computed n the Layered LSI spaces. It s more reasonable than drect comparson n the orgnal space. Meanwhle, such smlarty comparson actually s measured n the LSI space of each layer, respectvely. That s, the comparson s made from dfferent semantc aspects. The basc dea behnd ths process s that we can use the nformaton from all knds of aspects to cross-verfy the fnal decson. Our experments also ndcate the effectveness of ths method. 4. WEB OBJECT REPRESENTATION The prelmnary nformaton of the web object can be found n the web page contanng t or the correspondng web block [15]. However, the nformaton s usually nsuffcent to descrbe the web object and may also contan many nosy words. In order to mprove the web object representaton, a neghborhood graph around the web object s constructed wth text and hyperlnk analyss. Neghborhood pages are consdered n ths case snce they usually can help verfy or complement nformaton about web object. In ths secton, we frstly present the algorthm on neghborhood graph buldng and then address how to prune the nosy pages, whch are rrelevant to the target web object. 4.1 Neghborhood Graph Buldng To buld a neghborhood graph, the web page or the web block contanng the target web object s taken as the graph centre, and 3

4 all the web pages are connected accordng to ther hyperlnks. The utlzed approach to neghborhood graph buldng s based on the technque proposed by Harmandas [4]. That s, t constructs an undrected graph, where each web page s taken as a node and each hyperlnk as an edge, as llustrated n Fgure 4. In the followng sectons, the web page (or web block) contanng the target web object s referred as contaner for smplcty, whle the pages lnked wth the contaner are called neghborhood pages, Fgure 4 A 2-layer neghborhood graph wth 1- and 2-step pages In general, the less steps the page s from the contaner, the more closely the correspondng node s related to the target web object. Therefore, the number of the Graph Layer s an mportant parameter n web object representaton. Wth larger neghborhood layer number, more nformaton can be used to descrbe the web object, but more rrelevant (nosy) nformaton s also ntroduced. In the experments, we wll dscuss ts selecton. 4.2 Neghborhood Graph Prunng The neghborng graph s assumed contanng more nformaton to descrbe the target web object, compared wth the contaner tself. However, n the graph there are possble many nosy pages, whch do not contrbute to our task and wll even lead to 'topc drft'[1], such as the 'contact us'page and 'how to buy'page. They are necessary to be removed for the further processng. To remove the nosy pages, the smlarty between each neghborhood page and the contaner should be calculated, assumng the pages smlar to the contaner are more relevant to the web object. In our approach, the cosne measure s used to measure smlarty between each neghborhood page and contaner n each layer s LSI space. The smlarty n the jth-layer actually represents the confdence that whether the correspondng page has mutual semantcs wth the contaner from the vew of the jth-layer. Tradtonally, the pages wth small smlarty can be consdered as nosy pages [1]. In our experments, a porton of pages wth the smallest smlarty n each layer s taken as nosy pages. The threshold s called cutoff percent n our paper and s determned expermentally. By ths way, we get a set of nosy pages from each layer of doman knowledge, respectvely. Snce dfferent pages have dfferent semantcs or functons, they may get dfferent confdence n dfferent layers. Therefore, only the ntersecton of these canddate sets s confrmed as nosy pages, and then pruned from neghborhood graph. For some specal cases where the knowledge database s not herarchcal, e.g., for that only conssts of some plan text documents, our prunng scheme s stll avalable wth the layer number equal to 1. Once removng nosy pages n neghborhood graph, we can ntegrate the textual nformaton of all pages together to better descrbe the web object. In our approach, the weghted centrod (of neghborhood graph) s smply used as a descrpton vector of web object, whch s defned as, C = w D (4) D G where G s the pruned neghborhood graph, D s a web page n G, and w s the weghtng of web page D. The weghtng s set accordng to the path steps from the correspondng web page to the contaner. A general rule s, the less steps the page s, the hgher weghtng t has. In our approach, the weghtng s expermentally defned as equaton (5) and then normalzed to one, 1 w (5) log( d + 2) where d s the correspondng path steps, and the coeffcent 2 s heurstcally added n order to avod zero n the denomnator. 5. WEB OBJECT INDEXING To ths end, we get a descrpton vector of the target web object n Layered LSI spaces. Then, the vector s used to extract more exact structure nformaton of the target web object, by dscoverng the most approprate knowledge document whch best matches the web object. Thus, the problem s converted to textual nformaton matchng, that s, matchng between the web object representaton and each document n doman knowledge. At last, the obtaned structure data n knowledge document wll be used to ndex web objects. 5.1 Object-knowledge Matchng To acheve object-knowledge matchng, the smlarty between the descrpton vector and each knowledge document n each layer s calculated usng cosne measure. Usually, a web object belongs to only one certan layer of knowledge database. Therefore, our goal can be smply acheved by fndng the canddate document that s most smlar to the target web object, from the correspondng target layer n the knowledge database. However, ths method mght be not accurate enough, snce the smlarty score only provdes some confdence, and some noses would harm the matchng accuracy. To deal wth ths ssue, the herarchcal structure of doman knowledge s utlzed, and each canddate document n the target layer s re-scored or re-ranked by confrmng wth ts relatves (ncludng ancestors and offsprng). For example, as Fgure 5 shows, when we re-rank the confdence of the node T (a canddate document), the confdence of ts ancestors (node 1-2) and offsprng (node 3-7) are all ncorporated. Ths process s called confdence propagaton. The confdences from the canddate document s relatves actually represent the smlartes between the canddate document and the target web object n the correspondng semantc attrbutes (layers). The fnal score s thus cross-verfed from varous semantc aspects. The confdence propagaton from the relatves of a canddate document can be dvded nto two drectons, propagatng from ts ancestors (root) nodes (top-down), and propagatng from ts offsprng nodes (bottom-up), as Fgure 5 llustrates. In top-down propagaton, we drectly sum the confdence score of up-layers n the path from root to canddate document. It s feasble snce the 4

5 path from root to the canddate document s unque. However, from ts offsprng, the bottom-up propagaton path s not unque. We could also sum scores of all paths from the canddate document to ts offsprng for confdence propagaton. However, t s usually unfar snce the canddate documents wth lots of offsprng would be over-weghted. To deal wth ths problem, the canddate document wth a bg sub-tree s punshed by dvdng the number of ts chldren, n our approach. Fgure 5 Web object matchng and confdence propagaton The detaled formula of confdence propagaton s shown n equaton (6), whch sequentally combnes the nformaton from upper and lower layers, as well as normalzes nformaton comng from lower layers. D( r ) = s( r ) + D( k), 1 < TL k par( r ) U ( k) U ( r ) = s( r ) +, NC( r ) k ch( r ) TL < < L U ( l) S( rtl ) = s( rtl ) + D( k) + NC( r ) k par( rtl ) l ch( rtl ) TL where s(.) represents the orgnal confdence of a document, S(.) means the updated confdence score of a document, U(.) and D(.) means the propagated confdence from bottom-up and top-down, respectvely. r s a knowledge document and ts subscrpt represents ts layer number. L s the layer number of doman knowledge and TL s the target layer. par(.) and ch(.) stand for the set of parents and chldren of a document, respectvely, and NC(.) s the number of ts chldren. After confdence propagaton, the canddate document wth the hghest smlarty score S(r TL ) s chosen as the optmal document, whch best descrbes the structure attrbutes of the target web object. The propertes of ths web object are further used n ndexng and organzaton. It s noted that, n the case that the knowledge database s not herarchcal, we need not do any confdence propagaton. 5.2 Indexng Scheme Once web object s matched wth a knowledge document, the trust-worthy doman knowledge can be used to further ndex and organze web objects. It can be dvded nto two cases: 1. If the doman knowledge s a fully-structured database, the record attrbutes of the knowledge document can be used to represent the structure nformaton of the web object and then (6) ndex them. For example, f a song object s matched wth a knowledge document, n whch the correspondng metadata, such as artst, album, genre and release year, are avalable, these structure data can be further used to ndex the song object. Furthermore, many applcatons can be developed based on these structure ndces. 2. If the doman knowledge s not a structure database, that s, the knowledge document s not a record but plan text, we can use the text to descrbe web object better. Then, tradtonal ndexng scheme n text search/retreval can be used to ndex the web objects. 6. EXPERIMENTS In ths secton, we evaluate the performance of the proposed approach n the musc doman, ncludng, 1. We evaluate the overall performance of our approach, respect to varous settngs of neghborhood graph, such as the graph layer and cutoff percent mentoned n secton 4. We also look nto the performance on dfferent web objects. 2. We compare our approach wth the algorthm proposed n [4] (note that the algorthm n [4] s just a specfc verson of our approach, wth the cutoff percent equals 0). 3. We evaluate the effectveness of each module of our proposed framework, ncludng doman knowledge ndexng, neghborhood graph n web object representaton, and confdence propagaton n web object matchng. In order to measure the precson of our approach, the process of web object ndexng s vewed as an IR problem. That s, the target web object s taken as a query, the knowledge documents are ranked accordng to ther confdences, and then the top N are returned. Thus, the precson at top N results s used to evaluate the performance, as P@ N = q Q N q R Q (7) where Q s the query set and q s a query, R N s the top N results correspondng to query q, and s to get the count. In our experments, P@1, P@5, and P@10 are used for performance evaluaton. All our algorthms are mplemented n C++, and experments are run on two workstatons wth two 3.0G Hz Intel CPUs each and 3.0G memory. For SVD, we use SVDPACK whch can deal wth large-scale sparse matrx [9][13]. 6.1 Data Preparaton Musc s one knd of most valuable objects on the web, and many mature musc databases exst as Deep Web stes, whch can be used n our experments convenently. Therefore, our approach s performed and evaluated n the musc doman. In ths secton, we present the data preparaton for doman knowledge and testng musc objects, whch are further dvded nto song objects, album objects and artst objects Doman Knowledge Preparaton In our approach, the doman knowledge about musc s collected from All Musc Gude [8], whch s a Deep Web ste. The artst pages, album pages and song pages are downloaded, and then organzed n a three-layered, tree-lke structure, n whch each layer represents artst, album, and song, respectvely, as 5

6 shown n Fgure 2. The detals of our collected doman knowledge are shown n Table 1. Overall, there are pages, ncludng song pages, 4635 album pages and 1599 artst pages. Type #Documents #Overall Artst 1599 Album 4635 Song Table 1 The detals of our doman knowledge tree A specfc HTML parser s developed to re-buld up the knowledge herarchy from the crawled pages. The parser s used frst to explore the mplct semantc relatonshps between pages; and second, to extract specfc keywords, such as artst name, album ttle, and genre, for further web objects ndexng and other applcatons. After the knowledge s ready, LSI s used to ndex them. In text retreval doman, many researchers reported that keepng dmensons s a good trade-off between performance and computaton complexty of LSI. In our experments, the dmenson s set as 200 accordngly. Based on our algorthms, three layers of the knowledge tree are ndexed respectvely. For comparson, we also used the non-layered LSI, by combnng all documents of three layers nto a whole corpus Musc Objects Collecton As mentoned above, we manly focus on web objects ndexng, and assume web objects have been detected. Snce there are few works address ths task, n our experments, web objects are detected wth some smple heurstc rules. For example, a song object usually assocates wth some fle-type ndcators, such as fle extenson 'rm'or 'wma', and keywords lsten. In order to collect the testng web objects, we frst collect the top queres from search log of All Musc Gude n one week, whch ncludes hundred of songs, albums and artsts, respectvely. The queres not contaned n our knowledge tree are removed n order that each potental musc object has correspondng structure nformaton n the doman knowledge. Then, we throw the vald queres nto Google to obtan some related web pages, and crawl pages by usng them as seeds. In the crawled pages, only those, whch contan hyperlnks pontng to musc fles (wth specal extensons, such as rm, mp3, wma), are labeled as musc object pages. Table 2 shows the detals of our testng musc objects. A 2-layer neghborhood graph s crawled for each musc object. We do not consder 3-step pages (n the thrd layer), snce the web pages ncrease explosvely but a majorty of the pages are rrelevant to the target musc object. We also lmt the total page number under 500 n neghborhood graph buldng, n order to lmt the computatons. Object Type #Object #Average pages Artst Album Song Table 2. The detals of musc objects n testng set Fnally, the ground truth s manually annotated for evaluatons. 6.2 Overall Performance In the frst experment, we evaluate the overall performance of the proposed approach on testng song objects. In summary, our approach s confgured as followng modules, doman knowledge representaton usng Layered LSI spaces, neghborhood graph buldng and prunng, and herarchcal web object matchng. However, the performance s also related to two parameters, graph layer and cutoff percent n the constructon of the neghborhood graph. Fgure 6 llustrates the system performance (P@5) on song objects ndexng, correspondng to varous graph layer and cutoff percents. In our experments, the graph layer s chosen as 1 or 2, due to the explosve number of rrelevant 3-step pages; and the cutoff percent ranges from 25% to 100%, wth an nterval of 5%. Fgure 6 Overall performance respect to varous graph layers and cutoff percents From Fgure 6, t can be seen that more than 80% web objects can be correctly found n the returned top 5 knowledge documents, wth 60% cutoff n 1-layer graph and 85% n 2-layer graph. It can be also seen that, wth the ncrease of cutoff percent, the performance s frstly ncreases and then decreases. It s reasonable, snce when the cutoff percent s small, nosy nformaton manly affects the performance; whle the cutoff percent s large, relevant nformaton may be also fltered so that the precson decreases. Moreover, the best performance usng 2-layer graph s 4% better than that of 1-layer graph. However, a majorty (85%) of pages n the 2-layer neghborhood graph are consdered as nose and removed. Although t means some waste n page crawlng and parsng, n the followng experments, we stll uses 2-layer graph (ncludng contaner, 1-step and 2-step pages) wth optmal cutoff 85% as our default settng, n order to get hgher performance. In our experments, we also fnd the performance of our approach s heavly depended on descrptve nformaton of the song object. In order to look nto the performance on dfferent testng song objects, we manually classfy them nto three sets, ncludng self-descrptve, mult-page descrptve and nondescrptve object. The self-descrptve object contans suffcent nformaton n ts contaner page for human to tell ts structure property; and the mult-page descrptve object have to be dentfed from the contaner combnng wth the neghborhood pages; whle the non-descrptve object does not contan suffcent dscrmnatve nformaton n ts neghborhood graph. 6

7 Fgure 7 Precson curve of dfferent set of song objects Fgure 7 llustrates the performances correspondng to dfferent sets of song objects. From the fgure, we fnd that the performance on self-descrptve set frst ncreases and then almost keep the same when cutoff percent grows, whle the performance on mult-page descrptve set has a ncreasng phrase and decreasng phrase, wth optmal cutoff equal to 85%. It actually reflects the tradeoff between the nosy nformaton and complementary nformaton contaned n the neghborhood pages. However, the performance of non-descrptve set s always poor. It s ntutve snce no suffcent nformaton s provded n the neghborng pages. 6.3 Look Into Each Module The above secton evaluates the overall performance of our proposed approach. In ths secton, we evaluate the effectveness of each module whch s correspondng to each step n secton 2, ncludng Layered LSI spaces for doman knowledge, neghborhood graph, and confdence propagaton Doman Knowledge wth Layered LSI Indexng In our approach, doman knowledge s used, and Layered LSI spaces are bult to represent doman knowledge, snce dfferent layer of doman knowledge usually has dfferent meanng and term space. In order to evaluate the effectveness of doman knowledge and Layered LSI, an experment s desgned to compare the methods wth dfferent usages of knowledge, n the module of neghborhood graph prunng (keep other modules the same). The compared methods nclude 1) Layered LSI on doman knowledge, 2) one-layer LSI on doman knowledge, and 3) wthout doman knowledge (that s, comparng pages n neghborhood graph drectly [1]). The performance comparson of these three approaches s shown n fgure 8. It s obvous that the performance of Layered LSI ndexng s best. It s about 5% better than that wthout LSI, and about 3% better than one-layer ndexng. It not only ndcates knowledge s helpful n web object representaton, but also ndcates that layered ndexng can obtan better results Neghborhood Graph for Object Descrpton In secton 4, the neghborhood graph s bult to enrch the descrpton of the target web object, snce the contaner page usually does not provde suffcent dscrmnatve nformaton. It s especally sutable for those mult-page descrptve objects. Fgure 9 llustrates the performance of mult-page descrptve object ndexng, comparng among 1) 1-layer neghborhood graph (wth optmal cutoff 60%), 2) 2-layer neghborhood graph (wth optmal cutoff 85%), and 3) wthout neghborhood graph (only the contaner page). Fgure 9 Performance comparsons wth/wthout neghborhood graph for mult-page descrptve song objects It can be seen that, wth consderng neghborhood pages, the performance s dramatcally mproved. For example, the P@1 and P@5 are almost doubled after usng 1-layer graph or 2-layer graph Confdence Propagaton for Object Matchng In Secton 5, confdence propagaton s performed to re-score the smlarty between a canddate document and the target web object. After propagaton, the confdence of canddate document s verfed by confrmng wth ts relatves, and consequently, better results are obtaned. Fgure 10 llustrates performance comparson between wth confdence propagaton and wthout confdence propagaton, n the module of web object matchng. Fgure 8 Precson comparson on dfferent ndexng approaches Fgure 10 Performance comparsons wth/wthout confdence propagaton 7

8 It can be seen that, wth confdence propagaton, the precson and s mproved about 5%-7%. It ndcates confdence propagaton s also very helpful n web object ndexng. 6.4 Performance on Dfferent Musc Object To explore the performance on musc objects n the same doman but located n dfferent layers of the knowledge database, we also evaluate the performance on album objects and artst objects. The detaled comparsons are llustrated n Fgure 11. Fgure 11 Performance on musc objects n dfferent layers It s noted that, the performance on artst and album object s much better than song object, especally on P@1. There are two reasons. Frstly, due to the dstnct page propertes n musc doman, the terms used to descrbe an artst or album s usually much more than a song. In other words, there s much more nformaton that can be used to dstngush artsts and albums. Secondly, nformaton n song page s usually not dscrmnatve. By combnng wth neghborhood pages, the descrpton of a song object has a large overlap wth those songs from the same album. As a result, t s usually dffcult to dscrmnate the songs from a same album, whle t s relatvely easy to dentfy an album or artst object. Therefore, the P@1 of song object s much lower than that of albums and artsts; however, for P@10, the values are very smlar snce an album usually has about 10 songs only. 7. APPLICATION: MUSIC SEARCH The proposed technology on web object ndexng can be used n varous domans. To llustrate further use of ndexed web objects, an example prototype on musc search s developed n ths secton. The prototype s desgned to organze numerous musc resources dspersed on the Surface Web. Wth the obtaned structured ndces usng our approach, the search results are organzed accordng to ther mplct structure. Moreover, all the related nformaton, whch s dscovered from the knowledge documents, could also be assocated wth the search results and presented to users. Fgure 12 llustrates a snapshot of the prototype system. Wth the query yesterday, the returned results are organzed accordng to ther artst, album and year. As shown n the left panel of Fgure 12, the dfferent versons of yesterday and smlar songs were clustered to dfferent artsts, such as the Beatles, Boys II man and Clla Black; and then they are further classfed nto dfferent albums, such as Sessons and Yesterday and Today. As our experment shows, although t s dffcult to dentfy a song, the accuracy of album and artst dentfcaton s relatvely hgh. Therefore, organze the searchng results nto artst and album s feasble based on our proposed approach. Moreover, the related nformaton of current category, whch are obtaned from the matched knowledge documents, s shown n the rght panel of Fgure 12, ncludng song detals, album nformaton and track lst. The ndexed web objects, whch are obtaned from the Surface Web and assocated wth the current category, are also lsted n the Web Objects block. The prototype provdes more related and organzed nformaton than contemporary web musc search engnes. The obtaned structure s more meanngful n the doman than those obtaned by tradtonal semantc clusterng approaches [14]. 8. RELATED WORK Prevous works on object-orented ndexng can be traced back to the framework of mage retreval proposed by Harmandas [4], n whch a model for Web mages retreval s presented. The model s based on combnng the text content and hypertext n neghborhood graph. However, ther defnton on objects s lmted to web mages, and they dd not consder fndng the underlyng structures of web objects, ether. There are also some efforts to obtan musc structure nformaton from Deep Web stes. For example, Wndows Meda Player 10 has the functon to extract album nformaton of a song from MSN Musc, based on other metadata embedded n the ID3 tag. Usng a connectve cluster of web pages to mprove web search s also addressed n some work. Klenberg's HITS [2] s an early mportant effort usng lnk analyss to rank documents, whle t only depends on n/out-degrees of lnks. Bharat et al. [1] dentfed a problem of HITS: topc drft, and proposed to resolve t by content analyss n local graph. Ths modfcaton acheved a remarkable better precson n topc dstllaton applcaton. By manpulatng the weghts of pages, Chang et al. [3] created customzed authorty by addng more weghts to the documents that users are nterested n. Recently, researchers proposed to use connectve analyss to mprove term weghtng scheme n hyperlnk envronment [5]. However, none of them consdered usng doman knowledge to mprove the combnaton of lnk analyss and content analyss. Smlar to our work (matchng web object representaton n knowledge database), "DB+IR" [10][11][12] was proposed n the database doman to use unstructured query to search structured database. Dfferent from tradtonal SQL (Structure Query Language) technologes, some works n "DB+IR" regard database records as plan text, and use IR technologes to ndex and search database. Some researchers also proposed to mprove search precson by usng the structure of database, such as XSearch[12], whch utlzed the structure of XML DOM tree, and proposed to use ILF(Inverse Leaf Frequency) to punsh common terms n leaf nodes, whch was replacement of tradtonal IDF. 9. CONCLUSION In ths paper, we propose a novel approach to ndexng web objects by usng correspondng doman knowledge. Although n ths paper our method s utlzed and evaluated n the musc doman only, we beleve that our approach s feasble for general web objects ndexng. Here we conclude our approach: 8

9 1. Doman knowledge from tradtonal databases or Deep Web stes could be used to ndex web objects to help buld a domanspecfc structure. Ths method overcomes the dsadvantages of tradtonal approaches on herarchcal clusterng and automatc ontology buldng. 2. Indexng herarchcal knowledge database layer by layer s a good strategy, whch emphaszes each layer's concepts and suppresses noses effectvely. Moreover, ths method enables us to compare objects from varous aspects (varous attrbutes of object). It s an mprovement of the tradtonal approaches usng knowledge. 3. Confdence propagaton can effectvely utlze the relatonshps among nodes of knowledge tree. Each node's confdence can be amplfed or suppressed wth the confdences of ts relatves. It actually also provdes an enhancement for tradtonal IR methods. In the future works, we wll apply our approach nto more domans to buld effcent systems. We wll also further develop a better approach to web object detecton, and use technologes on web nformaton extracton to obtan better descrpton of the web objects. 10. REFERENCES [1] K. Bharat, and M. R. Henznger. Improved Algorthms for Topc Dstllaton n a Hyperlnked Envronment. In Proceedngs of the SIGIR conference on Informaton Retreval, 1998, pages [2] J. Klenberg. Authortatve sources n a hyperlnked envronment. Journal of the ACM, Volume 46, Issue 5, pages: , 1999 [3] H. Chang, D. Cohn, and A. McCallum. Creatng customzed authorty lsts. In Proceedngs of the Seventeenth Internatonal Conference on Machne Learnng, [4] V. Harmandas, M. Sanderson, and M. D. Dunlop. Image retreval by hypertext lnks. In Proceedngs of the SIGIR conference on Informaton Retreval (SIGIR 97), 1997 [5] K. Sugyama, K. Hatano, M. Yoshkawa, and S. Uemura. Refnement of TF-IDF Schemes for Web Pages usng ther Hyperlnked Neghborng Pages. In Proceedngs of the fourteenth conference on Hypertext and Hypermeda, 2003 [6] S. Deerwester, S. Dumas, et. al. Indexng by Latent Semantc Analyss. Journal of the Amercan Socety for Informaton Scence, 41-6, 1990, pages [7] Wordnet 2.0: A lexcal database for the Englsh language [8] All Musc Gude. [9] B. Mchael, D. Theresa, et. al. SVDPACKC (Verson 1.0) User's Gude. Techncal Report: UT-CS , Unversty of Tennessee [10] B. Y. Rcardo, and M. P. Consens. The contnued saga of DB-IR ntegraton. The 30th Internatonal Conference on Very Large Databases (VLDB) Tutoral, 2004 [11] V. Hrstds, and Y. Papakonstantnou. DISCOVER: Keyword Search n Relatonal Databases, In Proceedngs of the 28th Internatonal Conference on Very Large Databases (VLDB), 2002 [12] S. Cohen, J. Mamou, Y. Kanza, and Y. Sagv. XSEarch: A Semantc Search Engne for XML. In Proceedngs of the 29th Internatonal Conference on Very Large Databases (VLDB), 2003 [13] SVDPACK. [14] B. Lu, C. W. Chn, and H. T. Ng. Mnng Topc-Specfc Concepts and Defntons on the Web. In Proceedngs of the twelfth nternatonal World Wde Web conference (WWW2003), May 2003 [15] D. Ca, S. Yu, J. Wen, and W.-Y. Ma. VIPS: a Vson-based Page Segmentaton Algorthm, MSR-TR ,

10 Fgure 12 A snapshot of a prototype musc search system, utlzng the ndexng created by our proposed approach 10