Impact of Contextual Information for Hypertext Documents Retrieval

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Duane Oliver Townsend
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1 Impact of Contextual Informaton for Hypertext ocuments Retreval Idr Chbane and Bch-Lên oan SUPELEC Computer Scence dpt. Plateau de Moulon 3 rue Jolot Cure 9 92 Gf/Yvette France {Idr.Chbane Bch-Len.oan}@supelec.fr Abstract. Because the noton of context s mult-dscplnary [7] t encompasses lots of ssues n Informaton Retreval. In ths paper we defne the context as the nformaton surroundng one document that s conveyed va the hypertext lnks. We propose dfferent measures dependng on the nformaton chosen to enrch a current document n order to assess the mpact of the contextual nformaton on hypertext documents. Experments were made over the REC-9 collectons and sgnfcant mprovement of the precson shows the mportance of takng account of the contextual nformaton. Introducton Snce the begnnng of the Web nformaton has become wdely-accessed and wdely-publshed. he volume of heterogeneous and dstrbuted nformaton avalable on the Web has been exponentally and contnuously growng. hat s why the seekng and selecton of relevant nformaton s a very complex and dffcult task. Search engnes help the fnal-user n ths retreval task by ndexng a part of the Web but they have very few nformaton concernng the nformaton need of the user. Experments show that most of user s requests contan 2 or 3 terms. So few numbers of terms often leads to nose and slence n the responses gven by search tools. hs s a consequence of several reasons that nclude among others the mplct user s nformaton need for example her ntenton the context of the query and the non use of contextual nformaton of the documents n the ndexng phase. Several works on survey attempted to classfy dfferent contexts alongsde wth functonal or opposte crtera. For [4] [5] and [6] the context of a document s the nformaton related to the current document that s conveyed through hypertext lnks semantc network or surroundng text. he context s used to enrch the local ndex of a document wth nformaton extracted from ts neghbours. Experments showed that takng account ths context provde better precson for certan types of queres. In ths paper we are partcularly nterested n the local context of Web resources and we defne the context of Web pages as the neghbourhood nformaton of pages

2 whch s brought from the hypertext lnks to all resources drectly related to these current pages. In recent years several nformaton retreval methods usng the nformaton about the lnk structure have been developed and proved to provde sgnfcant enhancement to the performance of Web search n practce. Actually most of systems based on lnk structure nformaton combne the content wth the popularty measure of the page to rank a query result. Google s PageRank[] and Kenberg s HIS[2] are two fundamental algorthms employng the hyperlnk structure among the Web page. A number of extensons to these two algorthms are also proposed such as [3][7][8][9][][]. All these lnk analyss algorthms are based on two assumptons: the lnks convey human endorsement. If there s a lnk from page A to page B then we may assume that page A endorses and recommends the content of page B. hus the mportance of page A can n part spread to the pages besdes B t lnks to. 2 Pages that are co-cted by a certan page are lkely to share the same topc as well as to help retreval. he study of the exstng systems enabled us to conclude that all rankng functons based on lnk structure nformaton do not depend on query terms. It decreased sgnfcantly the found results precson. Indeed analyss of the user s behavours n ther research shows that they are not nterested n the popular pages f t does not contan the query terms. In ths paper we frst revew the related lterature on lnk analyss rankng algorthms. We also present some extenson of these algorthms by defnng the context of Web pages as enrched neghbourhood nformaton conveyed through hypertext lnks and whose mportance s computed accordng to the query terms. hen we ntroduce our new lnk analyss rankng algorthm wth the new rankng functon and we present experments on multple queres usng the proposed algorthm. We also present a comparatve of dfferent lnk analyss rankng algorthms. Last we dscuss results analyss. 2 Related Work Varous studes suggested that takng account of lnks between documents ncreases the qualty of nformaton retreval. PageRank[] of Google and the HIS[2] of Klenberg are the basc algorthms usng lnk structure nformaton. Generally these systems functon n two steps. In the frst stage a tradtonal search engne returns a lst of pages n response to user query. In the second stage these systems take account of the lnks to rank the documents results. In ths secton we descrbe some of prevous lnk analyss rankng algorthms. PageRank PR ntroduced by L. Page and S.Brn [] whch s part of the rankng algorthm used by google precomputes a rank vector that provdes a-pror mportance estmates for all the pages on the Web. hs vector s computed once offlne and s ndependent of the search query. At the query tme these mportance scores are used n conjuncton wth query-specfc IR scores to rank the query results. PageRank smulates a user navgatng randomly n the Web who jumps to a random page wth probablty -d or follows a random hyperlnk on the current page wth probablty d. hs process can be modelled wth a Markov chan from where the statonary probablty of beng n each page can be computed.

3 Intutvely ths formula means that the PR of a page A depends at the same tme on the qualty and the number of pages whch ctes A. For example the pages ponted by the home page Yahoo! that have a hgher PR wll be judged of good qualty. he PR computatons are long and requre cleanng the entre Web. Moreover the results obtanng by Google shows that the algorthm wtch compute PageRank value of a page s not completely relevant. he query results do not have sometmes any relatonshp wth research carred out. Because search engnes does not take nto account semantcs context or user profle. From where the dea to compute personalzed PageRank. Last years research led to three radcally dfferent solutons [6] the modular Pagerank the BlockRank and the opc senstve Pagerank. he three approaches approxmate PR wth some approxmaton although they dffer substantally n ther computatonal requrements and n the granularty of personalzaton acheved. Consderng the Web s a nested structure the Web graph could be parttoned nto blocks accordng to the dfferent level of Web structure such as page level drectory level host level and doman level. We call such constructed Web graph as the blockbased Web graph whch s shown n Fg.2 left. Furthermore the hyperlnk at the block level could be dvded nto two types: Intra-hyperlnk and Inter-hyperlnk where nter-hyperlnk s the hyperlnk that lnks two Web pages over dfferent blocks whle ntra-hyperlnk s the hyperlnk that lnks two Web pages n the same block. As shown n Fg 2 the dash lne represents the ntra-hyperlnk whle the bold lne represents the nter-hyperlnk. here s several analyss on the block based Web graph. Kamvar et al. [8] propose to utlze the block structure to accelerate the computaton of PageRank. Further analyss on the Webste block could be seen n [3][5]. And the exsted methods about PageRank could be consdered as the lnk analyss based on page level n our approach. However the ntra-lnk and nter-lnk are not dscrmnated to be taken as the same weght although several approaches proposed that the ntra-hyperlnk n a host maybe less useful n computng the PageRank [7]. In [8] Klenberg ntroduced a procedure for dentfyng web pages that are good hubs or good authortes n response to a gven query. o dentfy good hubs and authortes Klenberg s procedure explots the graph structure of the web. Each web page s a node and a lnk from page A to page B s represented by a drected edge from node A to node B. When ntroducng a query the procedure frst constructs a focused subgraph G and then computes hubs and authortes scores for each node of G say N nodes n total. In order to quantfy the qualty of a page as a hub and an authorty Klenberg assocated every page wth a hub and an authorty weght. Followng the mutual renforcng relatonshp between hubs and authortes Klenberg defned the hub weght to be the sum of the authorty weghts of the nodes that are ponted to by the hub and the authorty weght to be the sum of the hub weghts that pont to ths authorty. 3 Modelng the context of documents Consderng a graph of HML pages where hypertext lnks relate source pages to destnaton pages and consderng the HML anchor text of a source page that pro-

4 vdes nformaton to the destnaton page. HML anchors are often surrounded by addtonally text that seems to descrbe the destnaton page approprately. he anchor text and the text surroundng an anchor text of a lnk lnk-context s used for a varety of tasks assocated wth Web nformaton retreval. For example t may be used by a search engne to rank a page. hese tasks can beneft by dentfyng structural regulartes that appear around lnks and that would consttute a lnk-context. We descrbe a framework for conductng such a study. he framework serves as an evaluaton platform for comparng varous lnk-context dervaton methods. Our focus s on understandng the potental merts of usng the zone around the anchor text lnk-context for mprovng nformaton retreval. For that we propose a hyperlnkbased term propagaton model H. he H model propagates the frequency of query terms n a web page usng the context-lnk nformaton before assgnng the relevance weghtng algorthms to rank the documents. We consder three types of lnks: n-lnk out-lnk and n-out-lnk b-drectonal table. he H model can be appled to each type of lnk by recursvely propagatng the weght of lnk-context terms. able. Applcatons of the H model H propagaton functon n n-lnk F = F β ' In A n ' ' F Wegh t of lnkcontext outlnk Out A n F = F β n ' ' ' F n-outlnk n F = F β n ' ' ' ' F In Out A A In the Fgure we represent an example of a graph of pages where each node represents a page and each orented arc from node A to node B represents the lnkcontext to B. Each page contans a set of terms whose weght s calculated by combnng the Okap BM25 score and a term weght propagaton usng the lnk-context. It s necessary that these terms appear around the anchor text of lnks between documents. For example the weght of the term n the page P4 s calculated from all the weghts of the terms of the pages P P P2 and P3. he strength of each weght depends on the dstance between two documents n terms of lnks. For example there are three paths between the page and page 5: P-P-P4-P5 and P-P2-P4-P5 of length 3 and P-P-P2-P3-P4-P5 of length 4. Fgure. Example of lnk-context

5 We can easly calculate the weght of the term n the document as follow = In k In In n k F F F F F β β β In k represents a set of documents that are at dstance K from document. Fgure 2. Example of contrbuton of weght term propagaton from P to P5

6 In table 2 we provde an example of successve teratons correspondng to the fgure that llustrates our H algorthm of weght term propagaton. We notce that the propagaton weght of terms converge towards the red values. he number of teratons s fxed n order to elmnate the problem of cycles n the graph. able 2. Iteratons for the H model Iteraton Iteraton 2 F P=W F P=W F P2= F P3=W 3 F P4=W 4 F P5=W 5 F P=W F P= W β* W F P2= β* W F P3=W 3 F P4=W 4 β*w W 3 F P5=W 5 β*w 4 Iteraton 3 Iteraton 4 F 2 P=W F 2 P=W β*w F 2 P2=β*W F 2 P3=W 3 β 2 *W F 2 P4=W 4 β*w W 3 F 3 P=W F 3 P=W β*w F 3 P2=β*W F 3 P3=W 3 β 2 *W F 3 P4=W 4 β*w W 3 2*β 2 *W β 3 2*β 2 *W

7 F 2 P5=W 5 β*w 4 β 2 *W W 3 F 3 P5=W 5 β*w 4 β 2 *W W 3 2*β 3 *W F 4 P=W F 4 P=W β*w F 4 P2=β*W F 4 P3=W 3 β 2 *W F 4 P4=W 4 β*w W 3 β 3 2*β 2 *W F 4 P5=W 5 β*w 4 β 2 *W W 3 β 4 2*β 3 *W 4 Experments over REC-9 In ths secton we present an expermental evaluaton of our proposed algorthm that we compare to a content based model. We chose the Wg collecton. In our experments the precson over the standard recall levels whch are % % % s the man evaluaton metrc and we also evaluate the man average precson MAP and the precson at 5 and documents retreval P@5 & P@. Fgure 3 shows the expermental results on nformaton retreval usng dfferent context-lnk methods. he frst one whch s based on the content-only of the page and s presented wth the blue lne s the baselne algorthm. he others show results by usng our H model of term propagaton accordng to the types of lnks. he H model outperforms the content-only baselne and specfcally the H model of nlnk term propagaton s better than the others H models. hese results show that the nformaton conveyed by the n-lnk s the most mportant to descrbe a target page. Fgure 3. Results over REC erm Frequency erm Frequency Propagaton nlnk erm Frequency Propagaton outlnk erm Frequency Propagaton nlnk and outlnk Précson % % 2% 3% 4% 5% 6% 7% 8% 9% % standards recall levels able 2. Comparsons at MAP P5 and P

8 FP_OU F FP_IN FP_IN_OU map P P F : contents only FP_IN : propagaton of terms frequency through n-lnks FP_OU : propagaton of terms frequency through. FP_IN_OU : propagaton of terms frequency through n-lnks and out-lnks. able 2 shows that the n-lnk H model propagaton of terms performs the best result for MAP P@5 and P@. For example the results of n-lnk H model propagaton acheve 27% for MAP and 22% for P@5. 5 Concluson Several algorthms based on lnk structure to take account of the context of a Web page as an atomc unt of nformaton were developed. But untl now many experments showed that there s no sgnfcant proft compared to the methods based only on content of page. In ths paper we proposed a new method based on lnk-context usng nformaton around the anchor text and the propagaton of term weghts through the lnks. We performed expermental evaluatons of our system usng IR test collecton of REC 9. We conclude that the context of Web pages has a postve mpact n the ncrease of the precson n the top of rankng and n MAP. We are currently testng our model for expandng queres relevance feedback by selectng terms from the surroundng of the anchor text ssued from the cooccurrence matrx between terms of the most relevant documents we select the top ten relevant documents. Our future work s to test ths framework at the semantc blocks level to see the structural effects of blocks on rankng query results. Fnally new measure representng addtonal semantc nformaton may be explored. 6 References [] Brn S. et Page L. 998 he anatomy of a large-scale hypertextual Web search engne In Proceedng of WWW [2] Klenberg L. 998 Authortatve sources n a hyperlnked envronment In Proceedng of 9 th ACM-SIAM Symposum on screte Algorthms 998. [3] Lempel R. et Moran S. 2 he stochastc approach for lnk-structure analyss SALSA and the KC effect In Proceedng of 9 th Internatonal World Wde Web Conference 2.

9 [4] Savoy J. et Rasolof Y. 2 Lnk-Based Retreval and strbuted Collectons Report of the REC-9 experment: Proceedngs REC-9 2. [5] Salton G. Yang C.S. et Yu C A theory of term mportance n automatc text analyss Journal of the Amercan Socety for Informaton Scence and echnology 975. [6] Havelwala aher; Kamvar Sepandar Jeh Glen 23 An Analytcal Comparson of Approaches to Personalzng PageRank rapport technque unversté de Stanford 23. [7] Havelwala aher H. 23 opc-senstve PageRank : A Context-Senstve Rankng Algorthm for Web Search Knowledge and ata Engneerng IEEE ransactons on 23. [8] Sepandar. Kamvar aher H. Havelwala Chrstopher. Mannng Gene H. et Golub 23 Explotng the Block Structure of the Web for Computng PageRank 23. [9] eng Ca; Shpeng Yu; J-Rong Wen; We-Yng Ma 24 Block-based Web Search Mcrosoft research ASIA 24. []Xue-Me Jang Gu-Rong Xue Wen Guan Song Hua-Jun Zeng Zheng Chen We-Yng Ma 24 Explotng PageRank at fferent Block Level - Internatonal Conference on Web Informaton Systems Engneerng 24. [].Jeh G et Wdom. J. Scalng personalzed web search. In Proceedngs of the welfth Internatonal World Wde Web Conference 23. [2] Porter M.F. 98 An algorthm for suffx strppng 98. [3] J-Rong Wen Ruhua Song eng Ca Kalhua Zhu Shpeng Yu Shaozh Ye and We-Yng Ma 24 At the web track of REC 23 Mcrosoft research ASIA 24. [4] oan B.L. and Brézllon P. 24 How the noton of context can be useful to search tools. Proceedngs of the World Conference "E-learn 24" Washngton C USA Nov [5] Aguar F. Improvement of Web ocument Retreval by the Use of Ste's Context Herarchy. In Intellgent Exploraton of the Web". Sprnger-Verlag Heldberg Germany 23. [6] Mark A. Starmand. extual Context Analyss for Informaton Retreval. Proceedngs of the 2th annual nternatonal ACM SIGIR conference on Research and development n nformaton retreval SIGIR '97 Volume 3 Issue SI. ACM Press. July 997 [7] M. Bazre et P. Brézllon. "Understandng context before to use t". In 5th Internatonal and Interdscplnary Conference on Modelng and Usng Context Lectures Notes n Artfcal Intellgence Vol 3554 pp Sprnger-Verlag 25.

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