Improving Ranking of Search Engines Results Based on Power Links

Transcription

1 IPASJ Inernaional Journal of Informaion Technology (IIJIT) Web Sie: hp:// A Publisher for Research Moivaion... edioriiji@ipasj.org Volume 2, Issue 9, Sepember 2014 ISSN Improving Ranking of Search Engines Resuls Based on Power Links Mahmoud Rokaya College of Compuer and Informaion Technology, Taif Universiy, Taif, Saudi Arabia Permanen Address: Faculy of Science, Tana Universiy, Tana, Egyp ABSTRACT Informaion rerieval (IR) involves finding some desired informaion in a sore of informaion or a daabase. Despie here are as many as mehods of informaion rerieval bu here is few mehods of rerieval which considered a cusom rerieval ha depends on he relaionships beween insered words and oher words in he same domain of he insered words. Even he mehods ha ried o rely on he relaion beween keywords, hey followed hese key words blindly wihou any analysis o he acual relaions beween hese key words or oher relaed key words. Power Link aims a finding he relaion beween conceps, ideas and problems in a specific discipline. The power link algorihm was suggesed o measure how ow erms end o appear ogeher in a given corps. This work will presen a mehod o find relaions beween erms depending on Power Link. Power link is used o exrac se of linked erms for a given query based on he relaion beween he curren query and he previous one. A ranking scheme is used o filer he se of linked erms. The power links beween he documens ha appear in he search resuls and he filered se of linked erms combined wih erms in he query is used as a measure for ranking he search resuls. Terms in he filered se are given a differen weigh depending on is ype. The resuls of experimens reflec an improving in precision for he proposed mehod comparing o he ordinary approaches. Keywords:-Co-ciaion, Co-word Analysis; Informaion rerieval; Word co-occurrence; Power Link, Informaion Rerieval, Ranking, Learning o Rank. 1. INTRODUCTION The concep of he link relaed o co-word analysis sudies. Co-word analysis is a conen analysis echnique ha uses paerns of co-occurrence of pairs of iems (i.e., words or noun phrases). Indexes based on he co-occurrence frequency of iems, such as an inclusion index and a proximiy index, are used o measure he srengh of relaionships beween iems. Based on hese indexes, iems are clusered ino groups and displayed in nework maps. Co-word analysis considers he dynamics of science as a resul of acor sraegies. Changes in he conen of a subjec area are he combined effec of a large number of individual sraegies. This echnique should allow us in principle o ideniy he acors and explain he global dynamic [9; 10; 11 and 23]. Many sudies concenraed on he imporance of a single erm or compound erm. Some sudies deermined he opic of a documen or a par in a documen based on he populariy of single erms, hose erms are called Field Associaion erms. [7; 13; 20; 19; 21 and 22]. Field associaion erms (FA erms) are he erms ha indicae each subjec maer caegory in he classificaion scheme [12]. Field means a basic and common knowledge ha can be used in human communicaion [5; 6 and 22]. All hese previous mehods depend on he absolue frequency of he individual erms or he posiion of he erm inside he documens. Using he absolue frequency means ha he frequency of a erm will reflec he lengh of he documens raher han he weighs of erms. Also depending on he frequency only, as a ool o decide he fields of he erms, wases valuable informaion ha can be derived from he co-occurrence relaions beween erms. Rokaya e. al., 2008, presened a mehod o cluser and rank he erms in a given corps based on co-ciaion analysis [17 and 22]. Cociaion analysis depends on he co-frequency of wo erms regardless he posiions of insances of each erm in a given documen. Rokaya e., al., 2010, presened he power link algorihm which defined and measured he relaion beween wo erms based on he co-frequency and he relaive posiions of differen insances. The link power become bigger for hose erms ha have nearer relaive posiions [23; 25 and 26]. All hese previous mehods depend on he absolue frequency of he individual erms or he posiion of he erm inside he documens. Using he absolue frequency means ha he frequency of a erm will reflec he lengh of he documens raher han he weighs of erms. Also depending on he frequency only wases valuable informaion ha can be derived from he co-occurrence relaions beween erms. Bu he co-occurrence calculaions should no only depends on he co-frequency bu also depends on he relaive posiions of differen insances of he wo Volume 2, Issue 9, Sepember 2014 Page 1

2 IPASJ Inernaional Journal of Informaion Technology (IIJIT) Web Sie: hp:// A Publisher for Research Moivaion... edioriiji@ipasj.org Volume 2, Issue 9, Sepember 2014 ISSN erms. Power Link ha couns and analysis he co-occurrence of key erms in a corps based on co-occurrence and relaive disribuions of erms on a given subjec will be used o measure he relaions among a seleced sample of erms. Insead of depending on populariy of single erms or populariy of web pages o deermine he imporance of resuling documens wih respec o a given query, he new mehod ranks he documens depending on he populariy of he link beween erms in a query, erms in previous query and erms in he documens. The links map for a given corps is pre-calculaed. For a given query he proper erms are exraced and he linked lis of hese erms are filered and he ranking scheme is used o calculae he power link beween he resuling documens and he combinaion lis of query erms and he filered linked se of erms. The power link in he sum of each erm is give a double weigh for he new erms, negaive weigh for he removed erms and one for coninued erms. The resuled show ha he proposed mehod performing abou 25% in precision beer han he ordinary approach [27]. The remaining pars of his work are organized as follows: secion 1 discusses he idea of his paper and compares he expeced effec of using he resuls of his sudy in rerieval procedure. Secion 2 discusses he deails of he Power Link seps used in his sudy and is resuls. Secion 3 presens an arranging scheme for resuls of queries depending on resuls of secion 2. Secion 4 presens an evaluaion experimen for he approach presened in secion 3 finally secion 5 presens he conclusion and possible fuure work. 2. RETRIEVAL SYSTEM BASED POWER LINK Similar ex searching calculaes he degree of similariy beween he user's ex inpu and he daabase exs. A ex wih a high degree of similariy becomes oupu search resul Some mehods used in search engines show resuls in arranging ha reflec he degree of similariy beween query and documens. The documen ha has a highes similariy wih he query come firs hen he documen ha has smaller similariy ill reaching he one wih he smalles similariy wih he query o be shown a las. Oher common arranging mehod is erm frequency/inverse documen frequency. This mehod considers he disribuion of words and heir frequencies, hen generaes numerical weighs for words ha signify heir imporance in individual documens. Words ha are frequen (such as " or," " o" or " wih") or ha appear in many documens are given subsanially less weigh han words ha are more relevan semanically or appear in comparaively few documens. In addiion o erm weighing. Finally pages can be ranked using link analysis ha considers he naure of each page in erms of is associaion wih oher pages namely, by he number of oher pages ha poin o i or a hub by he number of pages i poins o [2; 3; 4; 8; 14; 15; 16 and 18]. Since all hese mehods depend on he absolue frequency of words in he query and documens or link analysis, hen arranging resuls according o hese mehods ignore compleely he relaions beween words in he query and he resuls ha may be irrelevan come in he firs. Also ordinary mehods in compuing similariy beween exs and user queries use he whole words available in he queries and exs, his rend is inefficien for processing large ex in heerogeneous subjec areas. Rokaya e., al., 2008, presened an arranging scheme of search resuls,ha considers relaions beween FA erms in he query,. The sysem picks he FA erms from he query hen make a ranking of hese FA erms, depending on his ranking he query resuls arranged [22]. Depending on cofrequency o deermine he relaion beween wo erms gives a wrong implicaion, especially, when he insances of he erms are disribued over a separaed pars in he same documens. Rokaya & Alam, 2010, proposed a he power link algorihm o deermine he relaion beween wo erms depsending on he co-frequency normalized by he average disance beween differen insances of he wo erms [23]. In his paper he concep of power link will be used o ge he lis of op relaed erms o he curren query. A ranking scheme will use he query erms and he relaed erms o rank he search resuls. Fig. 1 shows a similar ex search of inpu ex file and daabase ex files and heir rerieval resuls for new mehod which considers index of words links based on Power Link 3. POWER LINKS The erm power link was proposed by Rokaya and Alam, 2010, as a mehod of building dynamic field associaion erms dicionary. Power link algorihm presened new rules o improve he qualiy of filed associaion erms (FATs) dicionary in English [24]. The origin of his concep comes from he co-word analysis researches. Co-word analysis considers he dynamics of science as a resul of acor sraegies changes in he conen of a subjec area are he combined effec of a large number of individual sraegies. This echnique should allow us in principle o ideniy he acors and explain he global dynamic [1]. Volume 2, Issue 9, Sepember 2014 Page 2

3 IPASJ Inernaional Journal of Informaion Technology (IIJIT) Web Sie: hp:// A Publisher for Research Moivaion... edioriiji@ipasj.org Volume 2, Issue 9, Sepember 2014 ISSN Figure 1: A similar ex search of inpu ex file and daabase ex files and heir rerieval resuls for ordinary ex dealing mehods and for new mehod. Fig.2 explains he phases of calculaing power links. The calculaions are a chain of subsequen calculaions which ends by calculaing he power link beween a erm and a field which based on calculaing he power link beween a erm and a documens conained in ha field. Power link beween a erm and a documen based on calculaing he power link beween his erm and oher erms conained in he documen. If any wo erms 1 and 2 belongs o a documen D i will be said ha here is a link beween 1 and 2. The power of his link is measured by he funcion LT ( 1, 2) where where D is he number of differen erms in he documen D, cr ( 1, 2 ) is he co-occurrence frequency of 1 and 2 in he documen D and average L( 1i, 2 j ) represens he average disance beween any insans 1 i and 2 j of he erms 1 i, j D cr( 1, 2 ) LT ( 1, 2 ) (1), average L( 1 j, 2 j ) i, j and 2 in he documen D. For more deails see Rokaya and Alam, To esimae he power like beween wo erms 1 and 2 over a given corps, he funcion LCORPS 1, ) is defined. where: LCORPS ( 2 ( 1, 2 ) average LT ( 1, 2 D corps ) (2) This funcion saes ha he erms 1 and 2 will end o appear nearer ogeher if he value of his funcion reasonably high. To give a hreshold many values were experimenally has been ried around he mean value for he power link. This means ha he hreshold is no unique and i is dynamic. In fac i is dependen on he given corps. For he experimens he mean value was 31.5 so he algorihm is acivaed wih values around his mean o cover he inerval (mean-std, mean+std), where STD is he sandard deviaion of he mean value. Volume 2, Issue 9, Sepember 2014 Page 3

4 IPASJ Inernaional Journal of Informaion Technology (IIJIT) Web Sie: hp:// A Publisher for Research Moivaion... edioriiji@ipasj.org Volume 2, Issue 9, Sepember 2014 ISSN Field Docum w, w, 1 2 Candidae erm W Power Link Power Link beween a Figure2. Power link overview Power Link beween a 4. POWER LINK ANALYSIS Figure 3 summarizes he seps of he power link analysis; he inpus of he sysem are documens and lis of erms, he oupus of he sysem are clusers of erms and he power links map beween erms. The analysis goes hrough seps, namely, power link marix, normalized marix and he linking marix. These ge heir names according o he oupu of he sep. The nex subsecions explains he meaning and deails of each sep. 4.1 Power Link Marix The corpus (7850 aricle, 36.7 MB) was go from WWW newspapers and Nuriion sies. This corpus was chosen o be disribued over 11 sub-fields of he field Nuriion. A folder for each sub-field was esablished and he documens for his sub-field are sored in ha folder. A random sample of hese documens (281) was chosen o represen he daabase ha will be used in he Power Link and he whole corpus will be used in he evaluaion experimens. The erms exraced from his corpus auomaically were 1572 erms. 4.2 Normalized Power Link Marix Figure3. Overview of power link analysis seps To generae he power links marix. a C code o calculae he weigh for each erm in he lis in every documen in he corpus. The comparison beween every erm and every oken was made according o sem of each. The following formula was used o calculae he weigh W for a erm x in a documen k is derived from he classic known xk formula TF*IDF of Salon. The power link beween wo erms is calculaed using formula (2). The second sep is o presen he resuls o anoher C code ha calculae he Normalized Power Links. The inpu of his program is he erms lis and heir Power Links in he corpus. The oupu of he program is A symmeric marix, he cell in row x and column y represens he correlaion facor r beween each wo erms in he lis. Since each erm is presened in he power link marix by a row hen o unify he effec of numbers in his marix, i is needed o normalize he number in he marix o be wihin a specific inerval. The bes inerval is [1, -1] and he bes known measure o be used is he Volume 2, Issue 9, Sepember 2014 Page 4

5 IPASJ Inernaional Journal of Informaion Technology (IIJIT) Web Sie: hp:// A Publisher for Research Moivaion... edioriiji@ipasj.org Volume 2, Issue 9, Sepember 2014 ISSN correlaion facor. Hence correlaion facor beween erms is used and replaced in he cell corresponding o he same erms. 4.3 Linking Marix The linking marix is a symmeric marix. The cell in row X and column Y represen he cosine beween vecors X and Y in he normalized power marix. If he angle beween correlaion vecors of keyword x and keyword y was less han a given hreshold hen here is a link beween x and y. A hreshold o deermine if here is a link is used. The value of his hreshold is dynamic. For a given corps many rials is done o deermine he bes hreshold value. Tile, Auhors, Body Paragraphs, Secions Headings and References 4.4 Clusers and Links Map The Normalized Power Link marix is presened o a saisical program and wards mehod is used o produce he clusers. The final sep is o arrange he erms according o number of links hen he sum of power links hen according o he oal frequency. Table 1 shows a sample of he arranged erms according o his scheme. In his able erms beauy nuriion and spors nuriion has he same number of links and belong o he same cluser bu he sum of power links of beauy nuriion is greaer han he sum of co-occurrence frequencies of spors nuriion so beauy nuriion ge a higher rank han spors nuriion. Comparing ranking of erms Table 1 in conras of ranking in Table 2 ha depend only on he oal frequency, we will find ha he ranking is differen since he oal frequency does no reflec he acual disribuion of he word among he whole documens, also he relaions beween erms canno be refleced using he oal frequency of he individual erms only. This shows ha he new mehod is more efficien o achieve he correc ranking o erms based on Power Link 5. RANKING SCHEME The deails of his scheme can be summarized as follows: When a user eners a new query, he erms in he curren query is compared o he erms in he previous query o deermine which erms are removed ( TR se), coninued ( TC se) and added ( TN se). These ses are saved and labeled for more processing laer. Le G be he union of erms ha have links wih erms from he ses TC, TR, and TN. Suppose, for simpliciy, ha G 1, 2,..., 10. Table1: Terms ranking using Power Link Table 2: Terms ranking using oal frequency Term No. links Of sum of power links Toal frequency Term Toal frequency nuriion viamins supplemens 4157 viamins nuriion food 2187 supplemens supplemens 1032 viamins supplemens nuriion healh 1024 nuriion facs nuriion facs 969 nuriion healh beauy nuriion 925 spors nuriion nuriion die 903 beauy nuriion spors nuriion 803 nuriion informaion nuriion sofware 787 Volume 2, Issue 9, Sepember 2014 Page 5

6 IPASJ Inernaional Journal of Informaion Technology (IIJIT) Web Sie: hp:// A Publisher for Research Moivaion... edioriiji@ipasj.org Volume 2, Issue 9, Sepember 2014 ISSN nuriion food nuriion info 632 proein viamins 591 nuriion die nuriion whey 576 opimum nuriion proein 512 nuriion info nuriion informaion 405 nuriion whey opimum nuriion 395 nuriion sofware nuriion 338 By checking he disribuion of elemens of G over classes, one can rerieve o which class each erm in G belongs. Suppose, as appear in Fig. 4, he erms of G are disribued as follows: 1, 2, 3, 4 C1, 5, 6, 7, 8 C2 and 9, 10 C 3. The doed line beween wo erms, in Fig. 4, indicaes ha here is a link beween hese erms. If a erm has no links, his erm is called an isolaed erm. All isolaed erms are eliminaed from he resuling group FG. In Fig. 4, he erms 4 and 8 have no links wih oher erms, hence 4 and 8 are eliminaed from FG. Also, he erms 9 and 10 has a links wih erms ouside heir own class bu boh erms has no links wih oher elemens from G in heir own class. So, boh elemens are removed from he filered se FG. Now reurning o he original ses TR, TN and TC, erms in FG are classified ino hree ses. LTN is he se of elemens in FG and have a sronger link o TN, LTC is he se of elemens in FG and have a sronger link o TC and LTR is he se of elemens in FG and have a sronger link o TR. The erm "sronger" means ha he sum of power links beween a given erm and a se is he maximum among oher groups. Now, by knowing he ses ( TR, TC, TN ) and ( LTR, LTC, LTN ) one can wrie he equaion of calculaing he ranking facor ( RF ) for a given documens D ha appears in he search resuls for he curren query Q Prev ious Normalize d Linked C2 Que G={T TLN, TLC, C1 C1 G 9 C Figure.4 Overview of Ranking Scheme Volume 2, Issue 9, Sepember 2014 Page 6

7 IPASJ Inernaional Journal of Informaion Technology (IIJIT) Web Sie: hp:// A Publisher for Research Moivaion... edioriiji@ipasj.org Volume 2, Issue 9, Sepember 2014 ISSN RF Q, D * LT(, 2) * LT ( 1, 2) * (3) 1 LT ( 1, 2) TC LTC, D TN LTN, D TR LTR D , 2 Where, and belong o [-1,1]. In our experimens, he values assigned o, and were 0.5, 1 and -0.5 respecively. Example: Consider he query: "Viamins organic", assume ha he previous query of ha query was: "Organic minerals". Applying he conceps of ranking scheme we ge: TR ={ minerals }, TC ={ organic } and TN ={ Viamins }. Now we search he links map o rerieve he se of links G which has links wih he elemen in he se{ minerals, organic, organic}. G ={ subsances, plans, animals, inorganic, elemens }. By rerieving he class of each erm, one found ha {plans, animals} in one class and {subsances, inorganic, elemens} in anoher class and here are links beween all of hem. So he calculaions should consider all hese erms and FG = G ={ subsances, plans, animals, inorganic, elemens }. By calculaing he power link beween each elemen and he ses TR, TC and TN we found ha LTN = {plans, animals}, LTR ={subsances, elemens} and LTC ={inorganic}. Now he documens ha a big power link wih erms {Viamins, Plans, animals} will ge a high prioriy o come a he op of he resuls where documens ha have a big power link wih he erms {minerals, subsances, elemens} will come a he ail of he resuls. This shows ha he rank of a given documens will depend on he links map of erms ha his documen conains and also will depend on he user decision o refine he search by adding or removing erms. The removed and heir linked erms will give a negaive weigh for documens ha conain hese erms and will affec heir appearance order in he search resuls as expeced from such scheme. 6. EXPERIMENTS AND RESULTS. In his circumsance, one asks himself how o es his mehod, why i is expeced o give beer resuls han oher mehods. One of he keys of his issue is he relaion beween he se of erms in he query Q, erms in he se G and erms in he se FG. FG is a funcion is in Q and G, G iself is a funcion in Q and PQ FG F 1( G ) and FG F2 ( Q, PQ). The parameers of boh funcions F1 and F2 are he links map and he cluser. The F qualiy of he links map and he clusers leads direcly o he qualiy of he funcions 1 and F 2. So, i is needed o design experimens o check he qualiy of linking and clusering processes. From he oher hand i is imporan o check he validiy of he ranking process iself. This discussion leads us direcly o wo ses of experimens (a) Experimens o check he qualiy of links and clusers (b) Experimens o evaluae ranking process. 6.1 Experimens o check he qualiy of links and clusers Assuming ha a human would be able o idenify he clusers and links map for a given erm. If he se of linked erms for each erm derived by he auomaic sysem has a high overlap wih he human derived ses and if he classes derived by he sysem have a high overlap wih he classes derived by he human, he auomaic mehod should regarded as effecive. In all experimens, wo manually exraced ses are generaed. In boh cases he user is asked o classify he given erms in disjoin classes and deermine which erms should be linked or appear in he linked lis of a given erm. The evaluaion akes he following form: A user walks o he sysem and picks a se of erms. The erms are disribued randomly in ses and he user is asked o pick one of hem. The user is asked o classify hese erms in a specific number of disjoin classes. Also he user is asked o lis he linked erms o each erm in his se. Anoher manual classificaion is prepared by anoher user. The curren user is asked o compare his own resuls o he oher user resuls and refine his resuls if he wans or o approve his resuls. In he second case, he sysem generaes he clusers and he linked lis for each erm using our proposed mehod o ge he auomaic one. In boh cases, he user saisfacion is measured by he number of common clusers and commen linked liss for each erm beween his own resuls and he arge se (manually or auomaically derived). A random classificaion and derivaion of linked liss is done by randomly disribuing he erms in a disjoin classes and he lis of linked ses are derived for each ermed randomly based on he number of common characers Volume 2, Issue 9, Sepember 2014 Page 7

8 IPASJ Inernaional Journal of Informaion Technology (IIJIT) Web Sie: hp:// A Publisher for Research Moivaion... edioriiji@ipasj.org Volume 2, Issue 9, Sepember 2014 ISSN in each erm. If wo erms have a common hree characers hey should be added o he lis of each oher regardless he order of he characers in each erm. This mehod is called he random mehod Iniial: Anoher mehod depends on arranging he erms in each group in ascending manner. To ge he clusers of he erms, he oal number of erms is couned; he number of erms in each class is he oal number of erms divided by he number of required clusers. Le he number of erms in each class o be r, he firs r in he ordered se represens he firs class, he second r in he ordered se represen he second class and so on. To ge he linked lis for each group for a given erm. In he ordered group consider ha he nex erm of he las erm is he firs erm. The linked lis for a given erm is he r / 2 previous erms and he r / 2 nex erms in he ordered group. This mehod is called iniial mehod. Fify groups were chosen for he evaluaion process. Table 3. Evaluaion of power link clusering and linking Evaluaion for Deriving Clusers Evaluaion for Deriving Linked Liss Overlap beween manual clusers: 85.7 % Overlap beween manual Linking: 83.6 % Algorihm Cluser1 Cluser2 Average Linked Lis1 Linked Lis2 Average Power link 83.92% 72.26% 78.09% 73.92% 79.26% 76.59% Random 53.74% 48.36% 51.05% 51.74% 42.36% 47.05% Iniial 60.42% 54.67% 57.55% 58.34% 51.61% 54.98% The overlapping beween he wo manual clusers and he linked liss were 85.7 % and 83.6 % respecively which were expeced. Table 3 summarizes he resuls of he experimens which represens he overlapping for clusers and linked liss beween he auomaic and he manual mehods. From his able, he auomaic mehod does beer han he random and he iniial mehods. The resuls for he power link mehod were 83.92% and 72.26%, he average is 78.09% for clusers. The resuls for he power link mehod were 73.92% and 79.26%, he average is 76.59% for clusers. These resuls proof ha he proposed mehod is fine and gave a considered performance han he iniial and random mehods. 6.2 Experimens o evaluae ranking process For comparison beween ordinary approach and he new one. The precision was calculaed according o he following equaions. No. of relevan documens among he firs op resuls Precision (4) Number of firs op resuls Ten differen users are asked o use he new mehod and oher mehods in he search process. Each user eners 10 subsequen queries in a specific subjec. Each ime he user adds and removes some erms o refine he resuls. The search resuls for every query are saved in a separaed folder. For every query he firs 100 resuls are considered o be he se D ha he arranging scheme will be applied on i. Afer geing he arranged se he number of relevan documens among he firs 50 resuls of he se D was couned before and afer he arranging process. In his experimen, number of firs op resuls is equal o 50. For his experimen, 10 queries are designed and presened o each user. Each query was designed o reflec one and only one subjec in he field Nuriion. Since of he users is an exper in Nuriion field, each user was asked o refine his queries gradually by adding new erms and removing some erms. In each sep, user saisfacion is measured using he precision measure in formula (4). The experimens were done over a corps consiss of 9740 aricles exraced from newspaper, Nuriion secion. (56.7 MB). Figure 5 shows he comparison beween he precision of he ordinary and he new approach. In his figure each poin represens he average of each user resuls. The number of relevan documens increased for all users wih differen raio. Also he average of relevan number of documens afer applying he arranging scheme was documens and he average of relevan number of documens before applying he arranging scheme was documens. These resuls show ha based on he proposed arranging scheme and ranking process for he erms a beer arranging of search resuls be achieved. Volume 2, Issue 9, Sepember 2014 Page 8

9 IPASJ Inernaional Journal of Informaion Technology (IIJIT) Web Sie: hp:// A Publisher for Research Moivaion... edioriiji@ipasj.org Volume 2, Issue 9, Sepember 2014 ISSN Figure 5: The resul of comparison beween he precision for he firs fify resuls before and afer applying he arranging scheme using he absolue frequency and TF*IDF frequency The average precision for he new approach was 78.37%whereas he precision for he ordinary approach was 53.32%. This means ha he new approach achieved a beer precision han he ordinary approach by 25.05% The comparison beween he precision of he ordinary and he new approach. show ha he average of relevan number of documens afer applying he arranging scheme was documens and he average of relevan number of documens before applying he arranging scheme was documens. These resuls show ha based on he proposed arranging scheme and ranking process for he erms a beer arranging of search resuls can be achieved. The average precision for he new approach was 78.37%whereas he precision for he ordinary approach was 53.32%. This means ha he new approach achieved a beer precision han he ordinary approach by 25.05% 7. CONCLUSION AND RECOMMENDATIONS In his work Power Link was used o develop a mehod o rank erms in Nuriion field. From he experimenal resuls i is clear ha he ranks of erms using frequency is oally differen from using ranking using Power Link, since he frequency does no reflec he acual disribuion of he word among he whole daa. Also ranking was inroduced o he search engine o achieve a beer rerieval ha considers he relaions beween erms. The new approach using Power Link performing abou 25% in precision beer han he ordinary approach. Also he ranking algorihm refines he ranking of subsequen query resuls depending of he feedback of he user which can be followed hrough adding and removing or oally changing he erms included in he query. REFERENCES [1] Bursein, J., Kukich, K., Wolff, S., Lu, C., Chodorow, M., Harder, L., Harris. M. D., (1998). Auomaed scoring using a hybrid feaure idenificaion echnique. In ACL/COLING 98, Proceedings of he 36h Annual Meeing of he Associaion for Compuaional Linguisics [2] Hofmann, T. (2000). Learning probabilisic models of he Web. In Proceedings of he 23rd ACM SIGIR, pp [3] Lempel, R., & Moran, S. (2000). The sochasic approach for link-srucure analysis (SALSA) and he TKC effec. In Proceedings of he 9h Inernaional WWW Conference, Amserdam, The Neherlands, pp [4] Hofmann, T. (2000). Learning probabilisic models of he Web. In Proceedings of he 23rd ACM SIGIR, pp [5] Lempel, R., & Moran, S. (2000). The sochasic approach for link-srucure analysis (SALSA) and he TKC effec. In Proceedings of he 9h Inernaional WWW Conference, Amserdam, The Neherlands, pp [6] Charles T. M., Ber R. B., (2000), Donald H. Kraf. Tex informaion rerieval sysems. Academic Press. London. [7] Fukea, M., Lee, S., Tsuji, T., Okada, M., Aoe, J. I., (2000). A documen classificaion mehod by using field associaion words. Informaion Science, 126, [8] Ng, Y., Zheng, X., & Jordan, I. (2001). Sable algorihms for link analysis. In Proceedings 24h Annual Inl. ACM SIGIR Conference, New Orleans, Louisiana, U.S, pp [9] Ding, Y., Gobinda G., Foo S., (2001). Bibliomeric carography of informaion rerieval research by using Power Link. Informaion Processing and Managemen, 37, Volume 2, Issue 9, Sepember 2014 Page 9

10 IPASJ Inernaional Journal of Informaion Technology (IIJIT) Web Sie: hp:// A Publisher for Research Moivaion... edioriiji@ipasj.org Volume 2, Issue 9, Sepember 2014 ISSN [10] Panel, P., Lin, D. (2002). Discovering word senses from ex. In ACM SIGKDD Conference on Knowledge Discovery and Daa Mining, Edmonon, Canada. [11] Ruhven, I. and Lalmas, M., van Rijsbergen, C. J., (2002). Combining and selecing characerisics of informaion use. Journal of he American Sociey for Informaion Science and Technology, 53, 5, CI24 [12] Lee, S., Shishibori, M., Sumiomo, S., Aoe, J. I., (2002). Exracion of field-coheren passages. Informaion Processing and Managemen, 38, [13] Alam El-S., M. Fukea, K. Moria, Aoe, J. I., (2003). Documens similariy measuremen using field associaion erms. Informaion Processing and Managemen, 39, [14] Chakrabari, S. (2003). Mining he Web: Analysis of Hyperex and Semi Srucured Daa Mining he Web: Analysis of Hyperex and Semi Srucured Daa. Morgan Kaufmann. [15] Tomlin, A. (2003). A new paradigm for ranking pages on he WWW. In Proceedings of he 12h Inernaional World Wide Wed Conference, Budapes, Hungary, pp [16] C. Tomlin, I. Michell, A. Bayen, and M. Oishi, Compuaional Techniques for he Verificaion and Conrol of Hybrid Sysems, Proceedings of he IEEE, Volume 91, Number 7, pp , July [17] Chakrabari, S. (2003). Mining he Web: Analysis of Hyperex and Semi Srucured Daa Mining he Web: Analysis of Hyperex and Semi Srucured Daa. Morgan Kaufmann. [18] Gull A., (2006). On Two Web Informaion rerieval Boosing Tools: Clusering and Ranking, PhD hesis, Degli Sudi di Pisa. Universiy. [19] Alam, El-S. Elmarhomy G., Moria K., Fukea, M., Aoe J. I., (2006). Auomaic Building of New Field Associaion Word Candidaes Using Search Engine. Informaion Processing & Managemen Journal, 42, 4, [20] Elmarhomy G., Alam, El-S. Fukea, M., Moria K., Sumiomo, T., Aoe J. I., (2006). Auomaic Deleion of Unnecessary Field Associaion Word Using Morphological Analysis. Journal of Compuer and Mahemaics, 83, 3, [21] Mahmoud Rokaya, Elsayed Alam, Masao Fukea, Tshering C. Dorji and Jun-ichi Aoe, Ranking of field associaion erms using Co-word analysis, Third Inernaional Compuer Engineering conference, Cairo Universiy, December 26-27, 2007 CI19- [22] Mahmoud Rokaya, Elsayed Alam, Masao Fukea, Tshering C. Dorji and Jun-ichi Aoe, Ranking of field associaion erms using Co-word analysis, Informaion Processing & Managemen, Volume 44, Issue 2, March 2008, Pages [23] Mahmoud Rokaya and Alam, E-S. (2010) Building of field associaion erms based on links, In. J. Compuer Applicaions in Technology, Vol. 38, No. 4, pp [24] Mahmoud Rokaya and Abdallah Nahla, (2011), Building a Muli-lingual Field Associaion Terms Dicionary, Inernaional Journal of Compuer Science and Nework Securiy, Vol. 11 No. 3 pp , [25] Mahmoud Rokaya, AbdAllah Nahla, Sulan Aljahdali, (2012), Conex-Sensiive Spell Checking Based on Field Associaion Terms. IJCSNS Inernaional Journal Of Compuer Science And Nework Securiy. Vol. 12 No. 3 pp , [26] Mahmoud Rokaya, Auomaic Summarizaion based on Field Coheren Passages. Inernaional Journal of Compuer Applicaions 79(9):38-44, Ocober Published by Foundaion of Compuer Science, New York, USA [27] Mahmoud Rokaya, (2013), Auomaic Tex Exracion Based on Field Associaion Terms and Power Links, Inernaional Journal of Compuer and Informaion Technology (IJCIT). Volume 2, Issue 9, Sepember 2014 Page 10