Vol. 4, No. 6 June 2013 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.

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1 Sematic Method for Query Expasio i a Itelliget Search System 1 Ejiofor C. I, 2 Williams E. E, 3 Nwachukwu E.O, 4 Theo va der Weide 1,3 Uiversity of PortHarcourt, River State, Nigeria, 2 Uiversity of Calabar, Cross River State, Nigeria, 4 Radboud Uiversity, Nijmege, the Netherlads ABSTRACT Hudreds of millios of users each day use web search egies to meet their iformatio eeds. Advaces i web search effectiveess are therefore perhaps the most sigificat public outcomes of IR research. Query expasio methods have bee extesively studied i iformatio retrieval despite the recet advaces i search quality; the fast icrease i the size of the Web collectio has itroduced ew challeges for Web rakig algorithms. I fact, there are still may situatios i which the users are preseted with imprecise or very poor results.oe of the key difficulties is the fact that users usually submit very short ad ambiguous queries, ad they do ot fully specify their iformatio eeds. Query expasio is oe such method for ehacig user query to improve search egie performace ad satisfy the user eed.adaptive query expasio (QE) allows users to better defie their search domai by supplemetig the origial query with additioal terms related to their prefereces ad iformatio eeds. I this work wepropose a ovel sematic method based query expasio techique usig WordNet, which allows disambiguatig queries submitted to search egies. This techique ca be see to sigificatly improve search egie performace, particularly recall. Keywords: Query Expasio, WordNet, Iformatio Retrieval, syoyms, Otology, Sematic, ad Search Egie. 1. INTRODUCTION Iformatio retrieval aims to fid documets that are relevat to a user s iformatio eed. I web retrieval, the eed is typically expressed as a query cosistig of a small umber of words [22], ad aswer documets are chose based o the statistical similarity of the query to the idividual documets i thecollectio. The amout of iformatio published o the World Wide Web is growig at a astoishig rate, thus makig it ecessary to devise effective methods for helpig users fid what they are lookig for [16]. Query expasio (QE) allows users to expad their search domai by supplemetig their origial query with additioal terms ad phrases [2],[10]. Much research over several decades has led to developmet of statistical similarity measures that are reasoably effective at fidig aswers for eve the shortestqueries [24]. However, erichig a user's query with syoyms or additio of good additioal query terms ca improve search performace i a text retrieval system ad lead to sigificat improvemets i effectiveess. Search egies require a iput from the user i form of a query cosistig of keywords, which have a umber of disadvatages: researchers ormally are ot traied for really comprehesive searchig. They may ot kow all the tricks required to locate the right sources of iformatio to review their literature. They may ot kow how to formulate a query describig all that they wat, thus, researchers pay a high price due to ieffective iformatio discovery [25]. I tur, the idea of takig advatage of additioal kowledge, by expadig the origial query with other topicrelated terms, to retrieve relevat documets has bee largely discussed i the literature, where maual, iteractive ad automatic techiques have bee proposed [4]. The idea behid these techiques is that, i order to avoid ambiguity, it may be sufficiet to better specify: the meaig" of what the user has i mid whe performig a search, or i other words the mai cocept" (or a set of cocepts) of the preferred topic i which the user is iterested. A better specializatio of the query ca be obtaied with additioal kowledge, that ca be extracted from exogeous (e.g. otology, WordNet, data miig) or edogeous kowledge (i.e. extracted oly from the documets cotaied i the repository) [3],[4]. I this paper, we propose discoverig sematically similar terms usig WordNet ad we perform query expasio by geeratig lexical paraphrases of queries. These paraphrases replace cotet words i the queries with their syoyms. The followig iformatio sources are used i this process: Sematic iformatio obtaied from WordNet [17] ad statistical iformatio obtaied from our documet collectiowere used as the iformatio source. The statistical iformatio is used to moderate the alteratives obtaied from the sematic resources, by preferrig query paraphrases that cotai frequet word combiatios. A probabilistic formulatio of the query paraphrases is the icorporated ito the vector space documetretrieval model [23]. 2. RELATED WORKS Query expasio is a techique that has bee prove to be helpful ad is implemeted bymay search egies [11]. A good example is the Yahoo! Web search egie i The expaded query suggestio appears just below the search bar [26]. I the Yahoo! Web search egie the query expasio terms are suggestios that they ca use or igore. May search egies use query expasio automatically, ad the users are ever aware that query expasio terms are beig use to refie their query [9]. These search egie use query expasio 577

2 automatically to reduce overhead time that is wasted whe users are tryig to decide which query expasio terms to use. Search egies that use query expasio automatically are very cofidet i the terms they choose ad usually have complicated algorithms i order to choose their terms wisely. Automatically query expasio teds to improve the average overall retrieval performace by improvig certai queries ad makig it worse o others. Quite ofte automatic query expasio eds up hurtig the overall retrieval performace because of sematic oise. Sematic oise that is added to results leads to query drift ad low precisio. To make sure the beefits outweigh the cost, search egies that use automatic query expasio do a lot of testig to make sure query drift is small eough so that query expasio ca still be helpful. A commo test doe to evaluate automatic query expasio is to look at web pages that are already classified ad ru searches twice. Oe test will be doe with query expasio ad the other test will be doe without. Because the web pages are already classified search egies ca easily calculate precisio ad recall to compare the two searches [7]. Whe query expasio is doe maually especially i explicit relevace feedback it cabe very time cosumig. There is a lot of overhead whe users have to sca through pages ad decide which pages are relevat ad irrelevat, they also eed to select terms they wat to use to expad their query. Users ca select multiple terms by lookig through the suggestio ad select the oes that they like ad dislike. This is doe at ruttime ad this iterative process requires clickig o web pages ad terms evaluatig their relevace. This overhead ad extra time discourages users from query expasio ad keeps them from usig it. Maual query expasio eeds to be doe efficietly ad ituitively i order for it to be utilized. 3. AUTOMATIC QUERY EXPANSION The automatic query expasio or modificatio based o term cooccurrece data has bee studied for early three decades. The various methods proposed i the literature ca be classified ito the followig four groups: i. Simple use of cooccurrece data: The similarities betwee terms are first calculated based o theassociatio hypothesis ad the used to classify terms by settig a similarity threshold value [20]. I this way, the set of idex terms is subdivided ito classes of similar terms. A query is the expaded by addig all the terms of the classes that cotai query terms. It turs out that the idea of classifyig terms ito classes ad treatig the members of the same class as equivalet is too aive a approach to be useful [14]. ii. Use of documet classificatio: Documets are first classified usig a documet classificatio algorithm.ifrequet terms foud i a documet class are cosidered similar ad clustered i the same term class (thesaurus class) [5]. The idexig of documets ad queries is ehaced either by replacig a term by a thesaurus class or by addig a thesaurus class to the idex data. However, the retrieval effectiveess depeds strogly o some parameters that are hard to determie [6]. Furthermore, commercial databases cotai millios of documets ad are highly dyamic. The umber of documets is much larger tha the umber of terms i the database. Cosequetly, documet classificatio is much more expesive ad has to be doe more ofte tha the simple term classificatio). iii. Use of sytactic cotext: The term relatios are geerated o the basis of liguistic kowledge ad Cooccurrece statistics [12],[15]. The method uses a grammar ad a dictioary to extract for each term t a list of terms. This list cosists of all the terms that modify it. The similarities betwee terms are the calculated by usig these modifiers from the list. Subsequetly, a query is expaded by addig those terms most similar to ay of the query terms. This produces oly slightly better results tha usig the origial queries [12]. iv. Use of relevace iformatio: Relevace iformatio is used to costruct a global iformatio structure, such as a pseudo thesaurus [18]. A query is expaded by meas of this global iformatio structure. The retrieval effectiveess of this method depeds heavily o the user relevace iformatio. Moreover, the experimets i (Smeato, 1983) did ot yield a cosistet performace improvemet. O the other had, the direct use of relevace iformatio, by simply extractig terms from relevat documets, is proved to be effective i iteractive iformatio retrieval [19]. However, this approach does ot provide ay help for queries without relevace iformatio. I additio to automatic query expasio, semiautomatic query expasio has also bee studied [8]. I cotrast to the fully automated methods, the user is ivolved i the selectio of additioal search terms durig the semiautomatic expasio process. I other words, a list of cadidate terms is computed by meas of oe of the methods metioed above ad preseted to the user who makes the fial decisio. Experimets with semiautomatic query expasio, however, do ot result i sigificat improvemet of the retrieval effectiveess [8]. Amog the various approaches, automatic query expasio by usig plai cooccurrece data is the simplest method. I cotrast to the approaches preseted, we use a similarity thesaurus as the basis of our query expasio. First we show how such a similarity thesaurus is 578

3 costructed ad the we preset a query expasio model i order to overcome the drawbacks of usig plai cooccurrece data. 3.1 Query Expasio with Word Net WordNet otology is oe of the most importat resources available to researchers i the field of text aalysis, computatioal liguistics, ad may others related areas. WordNet [1] is otology of lexical refereces whose desig was ispired by the curret theories of huma liguistic memory. Nous, verbs, adjectives ad adverbs are grouped ito sets of syoyms (sysets), each represetig a distict cocept. Sysets are iterliked by meas of coceptualsematic ad lexical relatios such as hyperym/hypoym (is.a), ad meroym/holoym (part. whole). The WordNet purpose is to produce a combiatio of dictioary ad thesaurus that is more ituitively usable, ad to support automatic text aalysis ad artificial itelligece applicatios. WordNet is used i may text classificatio methods as well as i Iformatio Retrieval (IR) because of its broad scale ad free availability. 4. OUR APPROACH OF THE QUERY MODIFIER DESIGN FEATURE I this sectio, we described our approach to query expasio ad, i particular, focus o the ovel use of query associatios i the expasio process. Our geeralized method for query expasio proceeds as follows: first, a query is submitted to our search system ad the keywords as extracted from the query strig by the removal of stop words Let word (Qt ) be the total keywords which cosists of keyword1 (k 1 ), keyword2 (k 2 ), keyword3 (k 3 ) ad the rest keyword () (k ) i a user search query be represeted as show i equatio 1 Qt = k k = 1 The, every keyword (k ) has its ow sematic words (s ) as may as possible which are available i the sematic data or dictioary (d) as show i equatio (2). S represets the total sematic keywords derived from a sigle query keyword term which cosists of a few keywords (K) i the search fields. (1) ca yield few words which deped o the words provided i the sematic dictioary. The Figure 1 below shows the expaded query (Q) for a sigle query term. It starts with the user sigle query strig (Qt 1 ) to be traslated ito syoyms keyword term query (s 1, s 2, s s ) of words. Each keyword (k1, k2, k3 k) has a few sematic or syoym words (s1, s2, s3 s) accordig to the related keywords i the sematic data or dictioary. The collective words which cosist of the origial user query term ad the sematic words of each of the query term extracted from the user query are used to query search egie as show i equatio (3) Q = k 1i + s = 1 The sum of the etire query terms keywords ad there syoyms are used to extract relevat result from a search egie ad let D be the total retrieved documets that are related to each keywords ad there syoyms. A keyword or syoym is related to a documet if i each documet the first keyword (k 1 d 1, k 1 d 2, k 1 d 3.k 1 d ), secod keyword (k 2 d 1, k 2 d 2, k 2 d 3. k 2 d) ad the last keyword i the user query term should be (k d 1, k d 2, k d 3.k d ) with the additioal results of first syoym or sematic word (s 1 d 1, s 1 d 2, s 1 d 3.s 1 d ), secod syoym (s 2 d 1, s 2 d 2, s 2 d 3.s 2 d ) ad the last syoym word is i documet as i (s d 1, s d 2, s d 3.s d ). The relevat documets that ca satisfy the users query are couted as show i equatio 4 D = k d + d = 1 k 2i + s = 1 s d d = 1 + k mi (3) s = 1 (4) Where is the umber of documets retrieved for a particular search query. The Sematic (S) of the documets are the results to be derived from multiple D s for the retrieved results which depeds o each keyword (k) that has a related syoym words (S) which comes from sematic dictioary (d) like wordnet. K Qt = ( k ϵ k = 1 s) c d (2) = 1 wherek 1 is the first query term keyword ad k is the last umber of keywords that exit i the user query term. Therefore the query term keywords ca be as may as possible as log as the total of retrieval results from the iput words is ot iflueced. I additio, those words ca cotribute their ow sematic words where oe word 579

4 Query Term k 1 Qt1 k 1 s K 2 Qt2 WordNet Qt3 k 3 k 2 s k 3 s Qt K k s Fig 1 A: More detailed Desig of the Expadig Query Term 580

5 4.1 ReWritig Query by Augmetig query Term Syoym Recurrig terms ad phrases are extracted accordig to certai criteria, which iclude a term frequecy threshold to idicate the miimum umber of times a term should appear i the iput documets to be cosidered a frequet term ad the syoyms of the frequet terms ad phrases were extracted usig WordNet for query expasio. This is show i the Algorithm 1 below 1: //* start addig syoyms *// 2: SF list of syoym Features, empty 3: STD list of syoym TermDocumet Arrays, empty 4: for each documet d i D 5: for each term t i F 6: fid word for term i WordNet 7: if word is foud 8: for each sy set sy for word 9: for each syoym i sy 10: if syoym is the same as t // avoid addig the origial word from the syset 11: cotiue to ext syoym 12: ed if 13: if syoym is foud i F 14: td TermDocumet Array for syoym 15: icrease term frequecy i td for documet d by 1 16: f Feature that cotais term syoym 17: icrease total term frequecy for term syoym by 1 18: else //* syoym ot foud i F *// 19: if syoym is foud i SF // *check for same coditio above i previous syoyms *// 20:std TermDocumet Array for syoym 21: icrease term frequecy i std for documet d by 1 22: sf Feature that cotais term syoym 23: icrease total term frequecy for term syoym by 1 24: else // syoym ot foud i F or SF *// 25: td ew TermDocumet Array 26: icrease term frequecy i td for documet d by 1 27: add td to STD 28: f ew Feature 29: icrease total term frequecy for term syoym by 1 30: add syoym, td to f 31: add f to SF 32: ed if 33: ed if 34: ed for 35: ed for 36: ed if 37: ed for 38: ed for 39: apped SF to F 40: apped STD to TD Algorithm above shows the modified Extract Sigle Terms algorithm icludig the chages carried out for addig the syoyms usig WordNet 5. LEARNING TERMBASED CONCEPTS A problem of the stadard Vector space model used by the covetioal search egies is that a query is ofte too short to rak documets appropriately because covetioal search egies uses keyword based matchig techiques. To cope with this problem, our approach is to erich the origial query by expadig it with terms occurrig i the documets of the collectio ad there syoyms. But i cotrast to traditioal pseudo relevace feedback methods, where the top i raked documets are assumed to be relevat ad the all their terms are icorporated ito the expaded query, a differet techique is used to compute the relevat documets as follows: Let q = t 1 t be the user query cotaiig the terms t 1 t Ad q = (w 1,,w i,, w M ) T be the vector represetatio of this query. Let Q = {q 1,, q m } be the set of all syoyms of iitial query terms q 1,, q m Ad D k + be the set of relevat documets of the query q k. The goal is ow to lear for each term t i a cocept c i (1 i ) with the help of the syoyms i query terms ad their appropriate relevat documets. For this, the term t i is searched for its syoyms ad if it is foud, the relevat syoyms of the query terms are used to lear ad expad the cocept of a user query for term searchig usig search egie. Due to the VSM, a cocept is also a weighted vector of terms ad calculated with: c i = τ i (0,, wi,,0) T + δ i Σ D k + t i q k where 0 τ i,δ i 1 are weights for the origial term ad the additioal syoym terms, respectively. The expaded query vector is obtaied by the sum of all termbased cocepts: q = Σ C i i=1 Before applyig the expaded query, it is ormalized by q = q q For this approach, the cocepts are leared by addig terms from syoyms of the query terms. The complete documets (e.g. all term weights of the documet vector) are summed up ad added to the query to add more meaig to the query. 6. CONCLUSIONS & FUTURE WORK I coclusio, the techology of search egies is a very dyamic field, always lookig for improvemets ad ew ideas i order to satisfy user eeds. The ability of the system to fid relevat iformatio based o the user's search query to a successful system is based o how well a user query is formulated ad the terms that 581

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