Revealing Paths of Relevant Information in Web Graphs

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1 Revealng Paths of Relevant Informaton n Web Graphs Georgos Kouzas 1, Vassleos Kolas 2, Ioanns Anagnostopoulos 1 and Eleftheros Kayafas 2 1 Unversty of the Aegean Department of Fnancal and Management Engneerng Department of Informaton and Communcatons Systems Engneerng {gkouzas, janag}@aegean.gr 2 Natonal Techncal Unversty of Athens School of Electrcal and Computer Engneerng vkolas@medalab.ntua.gr, kayafas@cs.ntua. gr Abstract In ths paper we propose a web search methodology based on the Ant Colony Optmzaton (ACO) algorthm, whch ams to enhance the amount of the relevant nformaton n respect to a user s query. The algorthm ams to trace routes between hyperlnks, whch connect two or more relevant nformaton nodes of a web graph, wth the mnmum possble cost. The methodology uses the Ant- Seeker algorthm, where agents n the web paradgm are consdered as ants capable of generatng routng paths of relevant nformaton through a web graph. The paper provdes the mplementaton detals of the web search methodology proposed, along wth ts ntal assessment, whch presents wth qute promsng results. 1 Introducton In ths paper, a new web search methodology based on the ant colony algorthm, s proposed. In more detal we suggest an ant colony algorthm approach, whch s capable of tracng relevant nformaton n Internet. Based on [1][2], Ant Colony algorthm can be appled on a connected graph Gp = (P,L), where P are the nodes, and L the lnk between the nodes, whch represents a problem defnton. Every route n ths graph represents a soluton of the ntal problem. ACO converges n an optmal soluton tracng routes n the graph. In our approach, we consder the world-wde web as a graph G. Although our methodology mantans most of the Please use the followng format when ctng ths chapter: Kouzas, G., Kolas, V., Anagnostopoulos, I., and Kayafas, E., 2009, n IFIP Internatonal Federaton for Informaton Processng, Volume 296; Artfcal Intellgence Applcatons and Innovatons III; Eds. Ilads, L., Vlahavas, I., Bramer, M.; (Boston: Sprnger), pp

2 106 George Kouzas et al. ant colony algorthm characterstcs [3][4], ts unqueness les upon the fact that t apples n an envronment, the structure of whch s not pre-defned. In addton, some search technques for locatng and evaluatng the relevant nformaton are used based on web page smlarty [5], [6],[7] as well as web page clusterng [8]. 2 Basc Concepts Used The am of ths paper s to propose a methodology that s able to trace routes between hyperlnks, whch connect two or more relevant nformaton unts (web pages) wth the mnmum possble cost. Intally we consder a web nformaton unt (web page), whch s relevant to the user requests. Ths web page s consdered as the startng pont of the search. The search s based on the prncple that when some nformaton relevant to the user s request exsts on a pont-node of the world-wde web, then another pont-node n a close dstance s hghly possble to contan smlar (and thus relevant) nformaton [9]. We defne the hyperlnk as the basc dstance unt n the web unverse. The dstance between two nodes, s defned as the number of subsequent hops needed n order to be transferred from one node to another and vce versa. The methodology conssts of three phases. In the frst phase the start pont of the search are defned. These could be ether user defned web pages, ether result of search engnes [10][11][12]. In phase two, the suggested search algorthm takes place. The algorthm procedure runs teratvely, and each tme that converges to an nformaton unt, t specfes a new startng pont. The thrd and last phase s to group the results accordng to how relevant ther contents are. Durng the pre-processng of the hypertext documents the textual nformaton s extracted from HTML format. Dependng on the tags, we consder three levels of mportance (that s Hgh, Medum and Low). Then, the outgong lnks are extracted n order to construct the search graph. The fnal step s the web page smlartes calculaton accordng to [13][14], n whch the document hyperlnk structure s taken nto account, whle t conssts of the pre-process phase and the smlarty estmaton phase. As document smlarty, we consder that sentences and phrases carry also sgnfcant nformaton regardng the textual content. Accordng to ths, we used a comparson measure based not only on the smlarty of ndvdual terms but also on the smlarty of sentences and phrases [15]. The smlarty between two documents, d 1 and d 2 s computed accordng to Equaton 1. In Equaton 1 g(l ) s a functon that marks the length of the common phrase, whle s j1 and s k 2 represent the ntal length of the d 1 and d 2 document sentences respectvely. Functon g(l ) s proportonal to the rato of the common sentence porton length to the total sentence length as defned n Equaton 2, whle ms s the matchng phrase length and γ ndcates a sentence parttonng greater than or equal to 1. Parallel to ths procedure, the term-based smlarty of the tested web pages takes place usng the Vector Space Model (VSM) [16],[17] as defned n Equaton 3. However, the nverse document frequency between terms s not taken

3 Revealng Paths of Relevant Informaton n Web Graphs 107 under consderaton, snce the estmated smlarty value concerns two web pages and not a collecton of web pages as requred from the VSM. Therefore the fnal document smlarty SIM value s gven from Equaton 4. Result groupng s used for presentng the search results better. The acqured web pages are analyzed and presented n clusters. Each cluster contans a set of hgh mportance nformaton unts. The cluster creaton s based on the methodology proposed n [13][14] and the smlarty hstogram analyss. For specfyng smlarty, the classc vector space model s used [16],[17]. [g(l ) (f1w1 + f12 s j1 w j1 + sk2 2 w2 )] S p(d1,d 2 )= (1) w γ ms g ( l = ) s (2) d d j (wk, wk,j ) sm(d,d j )= = d d j 2 2 w w (3) SIM = S(d1,d 2 )= 0.5S p(d1,d 2 )+ 0.5St (d 1 2 k, k2 k,j,d ) (4) 4 Ant Seeker In ths secton we present the proposed search algorthm, whch s based on the theoretcal model of the ant colony algorthm [1]. The proposed algorthm deals wth the world-wde web nformaton search problem. The structure of the worldwde web conssts of a set of nformaton unts (web pages) and a set of lnks (hyperlnks) between them. Thus, assumng a graph Gp = (P,L), where P are the nodes, and L the lnk set between the nodes, we can consder the world-wde web as a graph G wth nfnte dmensons. 4.1 An Overvew of the Proposed Algorthm The proposed algorthm s a slght modfcaton of the ant colony algorthm [9], [18] and therefore t adopts most of the basc characterstcs of the colony algorthms famly [1]. However, several modfcatons were made n order to apply the proposed algorthm n the partcular World Wde Web paradgm. Thus: Each artfcal ant can vst a predefned maxmum set of nodes.

4 108 George Kouzas et al. All artfcal ants start from the start-node. In addton, when the algorthm starts there s no further nformaton for the graph structure n whch search wll be appled. In ths way, the harvestng procedure of real ants s smulated. The process of nodes recognton, whch they have relevant content wth the ntal node, s based on the web page smlarty mechansm as descrbed n the prevous secton. The search begns from the ntal start node whch s gven by the user and n each step of the algorthm, each ant-agent moves from node to node j. Assumng that n node j the pheromone value at tme t s τj(t), then the ant vsts node j through node accordng to the pheromone functon. The process s repeated untl each ant vsts the predefned maxmum number of nodes. After the creaton of the canddate routes, the best route s extracted and the node pheromone values are updated. Ths process s then repeated, whle t ends when a convergence to a specfc route s found. The node, whch has the route wth the maxmum smlarty value n comparson wth the startng node, s assgned as the startng node for the next search. Durng the ntalzaton phase, three varables are defned: the total ant-agent number NoA, the ntal pheromone value IPV s defned n each new node and the number (Nmax) of the maxmum nodes an ant can vst. 4.2 Heurstc Functon-pheromone Model For every new node s added, the content smlarty to the ntal one s checked. Thus, each node s characterzed from a smlarty value, whch mples the qualty of a node (Equaton 4). In order to specfy the qualty of a node, apart from ts content smlarty to the ntal node, a second value that defnes ts ablty to leads to a node wth hgh qualty content s also specfed. Therefore, ponts (nodes) wth low smlarty values ncrease ther sgnfcance when they lead to ponts of hgh frequency. The calculaton of a qualty value s gven by the heurstc functon accordng to Equaton 5, where d s the path of an ant-agent where node s ncluded n ( 0 < d < NoA ), SIM s the smlarty functon of node j as defned n d Equaton 4, and SIM j stands for the smlarty functon of node j, whch belong to the route d rght after ts prevous vst n the node ( < j < Nmax ). As mentoned before, n the ntal phase, each node nserted n the graph, has a pheromone value IPV. Every tme, a complete teraton of algorthm occurs, the pheromone s updated. More specfcally, the nodes used as ntermedate or fnal ponts n the ant routes are updated. Ths procedure s gven from Equatons 6 and 7. In Equaton 6, h s the heurstc functon gven from Equaton 5, whle k s the number of ants that used node for the route creaton. Accordng to Equaton 7, the nodes that lead to hgh frequency routes ncrease ther pheromone values substantally over the algorthm teratons. To avod nfnte assgnment of pheromone values n certan nodes, the pheromone value s normalzed between zero and one as defned by Equaton 8, where τmax (t + 1), s the maxmum pheromone value n

5 Revealng Paths of Relevant Informaton n Web Graphs 109 the current teraton. Each tme an ant s n a node, t must choose the next node j. The nodes, whch are consderng to be vsted, are the drectly connected nodes. Ths defnes the accessblty value of each web page gven by Equaton 9. In order to avod endless loops, accessblty excludes the nodes, whch contrbuted n the past n the creaton of the route. The algorthm utlzes the classc probablty model of the ant colony algorthms gven from Equaton 10. Whenever an algorthmc teraton occurs, ants make a route, based on the pheromone value and the qualty of the nodes n the search graph. The nodes wth the hghest qualty values ncrease ther pheromone values, and thus they have hgher probabltes to be chosen. A soluton conssts of a chosen route (set of nodes) and not a sngle node. As soluton we defne the node of the fnal route, whch presents the larger smlarty value. Ths node s then added to the lst of solutons. d h (t + 1 )= max( SIM,SIM,h (t)) (5) < j<n j MAX τ = kh (6) ' τ(t + 1 ) = τ(t)+ τ (7) ' τ(t + 1) τ(t + 1)= τ (t + 1) max (8) η j = 1 f node js drectly lnked from node (9) 0 otherwse τ j ηj Ρ j = (10) τ η k k allowed k k 5 Results - Evaluaton Ths secton presents the results of the proposed methodology. The evaluaton took place n two experment phases. In the frst we evaluate the performance of the proposed Ant-Seeker algorthm [9], [18] by applyng the algorthm n three dfferent queres. In the second phase we evaluate the ntroducton of some clusterng technques to the methodology for groupng the results. The search procedure was examned by queryng dfferent parts of the World Wde Web three tmes. Durng ths procedure we used only web-pages wth Englsh content. In order to apply and evaluate the algorthm, we followed three steps. The frst step nvolves the preprocessng of the WebPages; the second nvolves the balancng of varables NoA, Nmax and IPV whle the thrd step nvolves the algorthm executon. For all experments the varable values where chosen to be NoA=10,

6 110 George Kouzas et al. Nmax=3, NC=100 and IPV=0.4. For each search the set of returnng results s equal to the number of algorthm teratons. Ths reduces the result qualty, but ths s mportant especally n cases where the algorthm doesn t manage to converge n a relatve to the query soluton durng the ntal search stages. The results of clusterng to the three algorthm evaluaton sets appear n table 6. Applyng clusterng methods to the returnng results, the percentage of related document retreval s decreasng (between 2% to 5%) but at the same tme ther qualty ncreases (from 30-50% to 80-90%). Ths s an expected behavor, because, the nodes-pages used for the search contnuaton only, are cut off due to the low smlarty value wth the ntal document. However, a small part of the correct results, are not ranked correctly durng the clusterng. The explanaton s that the smlarty calculaton model durng algorthm executon s gven by Equaton 4 and on the other hand the smlarty calculaton model, durng clusterng s the vector space model [17]. We use a dfferent functon to calculate the smlarty because the fnal collecton of the web pages s unknown durng the search, so we use equaton 4 that defnes smlarty between a par of pages. On the contrary durng clusterng the total returnng results vrtually defnes the collecton. In table 2 the results of applyng the methodology for 6 random queres n the world-wde web appear. The proposed algorthm s ablty to seek and extract nformaton relatve to the query from the world-wde web s outlned n the expermental results. However durng the experments we created a set of constrants. The most mportant constrant s the scale of the world-wde web whch dd not allow applyng the algorthm n a wder scale search. The sample sze was of value The proper evaluaton of the algorthm requres full defnton of the samples as concernng ther nformatonal relatvty to the reference node. The classfcaton of all the samples based on smlarty gves an estmaton of the relaton of the documents and therefore a classfcaton measure but stll remans a mechancal classfcaton method whch cannot replace the human factor. For the evaluaton some machne learnng technques could be used as n neural networks [10], [11] but a set of already classfed documents s requred n order to extract the relatve documents. The second constrant that vrtually s a result of the prevous constrant s the overlappng between searches. The algorthm ncludes an overlappng search preventon mechansm n order to avod creatng cyclc search routes. However, n a lmted porton of the world-wde web forbddng backtrackng would result n a search termnaton n only a few steps (2 to 5 searches per sample). For ths reason, the only constrant assgned for route creaton was blockng addng a node, whch belongs to the current set of solutons of the algorthm. 6 Concluson By evaluatng the behavor of the algorthm we observe that t enables search n real tme. The ablty to choose the drecton of the search autonomously, allowng

7 Revealng Paths of Relevant Informaton n Web Graphs 111 the search of unknown terrtores at the same tme s noteworthy. An mportant advantage n contrast to other classc search and classfcaton methods s the fact that t does not requre coverng the whole search space. In the experments the coverage percentage was at 40%, takng all the constrants under consderaton. However the retreved document percentage s lmted to 80% as depcted n table 1. The document structure n whch the search takes place doesn t seem to affect the search. In addton as the structure tends to that of a fully lnked document the algorthm performance s ncreasng. In contrast to the most search technques the ablty to use a textual query allows qualty searches. Search wth queres of type of a set of terms has the advantage of beng short as long as the query s accurate. But when the query s naccurate then the results are confusng. The use of a reference document for the search mproves the qualty of the returnng results. Of course, the documents relatve to the nformatonal need, but wth low smlarty value wth the reference document cannot be retreved. Accordng to the experment results the coverng percentage s about 40% wth an average near 90%. Although the download percentage for the specfc coverng percentage s satsfyng, applyng the algorthm to large scale searches s prohbtve. Applyng the algorthm to the world-wde web for the second set of experments s qute tmeconsumng. Addtonally, addng solutons unrelated to the query produces low qualty results. Addng clusterng technques to the retreved documents mproves the qualty of the results, but for each rrelevant to the query document return, an analogcal search cost s added. As an alternatve method for cuttng off bad solutons could be an addtonal varable to the algorthm solvng rule. Ths value would defne a mnmum lmt value to the smlarty of the canddate soluton to the reference document. Table 1 System performance usng clusterng technques Table 2 System Performance for random nternet queres Experment Relatve Algorthm Clusterng Download Accuracy Pages Total Found Relatve Correct (%) (%) ,40 83, ,47 81, ,28 92,86 Experment Pages Relatve (%) (%) Relatve System Download Accuracy Correct ,50 75, ,00 57, ,48 76, ,00 100, ,24 88, ,67 78,57

8 112 References George Kouzas et al. 1 M. Dorgo and T. St utzle. Ant Colony Optmzaton. The MIT Press, Dorgo M., and Caro G.D., 1999, Ant Algorthms Optmzaton. Artfcal Lfe, 5(3): Dorgo M., and Manezzo V., 1996, The ant system: optmzaton by a colony of cooperatng agents. IEEE Transactons on Systems, Man and Cybernetcs, 26(1): Dorgo M. and Caro G.D., 1999, The Ant Colony Optmzaton Meta-heurstc n New Ideas n Optmzaton, D. Corne, M. Dorgo, and F. Glover (Eds.), London: McGraw-Hll, pp Pokorny J (2004) Web searchng and nformaton retreval. Computng n Scence & Engneerng. 6(4): Oyama S, Kokubo T, Ishda T (2004) Doman-specfc Web search wth keyword spces. IEEE Transactons on Knowledge and Data Engneerng. 16(1): Pokorny J (2004) Web searchng and nformaton retreval. Computng n Scence & Engneerng. 6(4): Broder A, Glassman S, Manasse M, Zweg G. Syntactc clusterng of the Web. Proceedngs 6th Internatonal World Wde Web Conference, Aprl 1997; G. Kouzas, E. Kayafas, V. Loumos: Ant Seeker: An algorthm for enhanced web search, Proceedngs 3rd IFIP Conference on Artfcal Intellgence Applcatons and Innovatons (AIAI) 2006, June 2006, Athens, Greece. IFIP 204 Sprnger 2006, pp I. Anagnostopoulos, C. Anagnostopoulos, G. Kouzas and D. Vergados, A Generalsed Regresson algorthm for web page categorsaton, Neural Computng & Applcatons journal, Sprnger-Verlag, 13(3): , I. Anagnostopoulos, C. Anagnostopoulos, Vassl Loumos, Eleftheros Kayafas, Classfyng Web Pages employng a Probablstc Neural Network Classfer, IEE Proceedngs Software, 151(03): , March Anagnostopoulos I., Psoroulas I., Loumos V. and Kayafas E., Implementng a customzed meta-search nterface for user query personalzaton, Proceedngs 24th Internatonal Conference on Informaton Technology Interfaces (ITI 2002), pp , June 2002, Cavtat/Dubrovnk, Croata. 13 K.M. Hammouda, M. S. Kamel, Phrase-based Document Smlarty Based on an Index Graph Model, Proceedngs IEEE Internatonal Conference on Data Mnng (ICDM 2002), December 2002, Maebash Cty, Japan. IEEE Computer Socety 2002, pp K.M. Hammouda, M. S. Kamel, Incremental Document Clusterng Usng Cluster Smlarty Hstograms, Proceedngs WIC Internatonal Conference on Web Intellgence (WI 2003), October 2003, Halfax, Canada. IEEE Computer Socety 2003, pp J. D. Isaacs and J. A. Aslam. Investgatng measures for parwse document smlarty. Techncal Report PCS-TR99-357, Dartmouth College, Computer Scence, Hanover, NH, June G. Salton, M. E. Lesk. Computer evaluaton of ndexng and text processng, Journal of the ACM, 15(1):8-36, G. Salton. The SMART Retreval System Experments n Automatc Document Processng. Prentce Hall Inc., Kouzas G., E. Kayafas, V. Loumos Web Smlarty Measurements usng Ant Based Search Algorthm, Proceedngs XVIII IMEKO WORLD CONGRESS Metrology for a Sustanable Development September 2006, Ro de Janero, Brazl.