Query Clustering Using a Hybrid Query Similarity Measure

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1 Query clusterng usng a hybrd query smlarty measure Fu. L., Goh, D.H., & Foo, S. (2004). WSEAS Transacton on Computers, 3(3), Query Clusterng Usng a Hybrd Query Smlarty Measure Ln Fu, Don Hoe-Lan Goh, Schubert Shou-Boon Foo Dvson of Informaton Studes School of Communcaton and Informaton Nanyang Technologcal Unversty SINGAPORE {p , ashlgoh, assfoo}@ntu.edu.sg Abstract: Query clusterng s a task that groups smlar queres automatcally wthout usng predetermned class descrptons. Such clusters can be used to dscover the common nterests of onlne nformaton seekers to explot ther collectve search experence for the beneft of others. Snce smlarty s fundamental to the defnton of a cluster, measures of smlarty between two queres s essental to the query clusterng procedure. Ths paper ntroduces a hybrd query smlarty measure that uses both query terms and the results returned to queres. Experments show that the hybrd approach can generate query clusters of better overall qualty than exstng smlarty measures. Key-Words: Informaton retreval, query mnng, query clusterng, smlarty measures. 1 Introducton Query mnng s a term appled to technques used to determne the underlyng structure and relatonshps n large amounts of queres. It can be used to dscover common nterests of onlne nformaton seekers and to explot ther collectve search experence for the beneft of others. Query clusterng s one of the technques used n query mnng, and t groups smlar queres automatcally wthout usng predetermned class descrptons. Query clusterng allows users to utlze other users search experences or doman knowledge by analyzng the nformaton stored n query logs, and then groupng and extractng useful related nformaton on a gven query. The extracted nformaton can then be used as recommendaton tems (used n query recommendng systems) [1] or sources for automatc query expanson [2, 3]. An example nvolvng query clusterng s gven below. Consder a scenaro where user A s nterested n automatc query expanson research. She wants to look for a partcular research paper related to ths feld but she cannot remember ts name. Hence she enters automatc query expanson n a search engne and obtans a lst of results. However nothng n the top 50 results contans the desred nformaton and she does not know how to modfy her query snce she s a novce n ths research area. At the same tme, another user B may remember the paper s name and knows that good search results can be obtaned by usng automatc generaton of extended queres as the query. In ths case, B s search hstory s usually stored as part of the search engne s query logs. Thus by clusterng smlar queres and then recommendng these clusters to users, there s an opportunty for user A to take advantage of prevous queres and use the approprate ones to meet her nformaton need. Snce smlarty s fundamental to the defnton of a cluster, measures of smlarty between two queres s essental to the query clusterng procedure. The classcal method n nformaton retreval suggests a smlarty measure based on query terms (content-based approach) [4]. Here, queres are grouped nto the same cluster f they contan one or more common terms. However the precson wll lkely be low due to the short length of queres and the lack of contextual nformaton n whch queres are used [2, 5]. For example the term lght can be used n four dfferent ways (noun, verb, adectve and adverb). An alternatve approach s to use the results (e.g. result URLs n Web search engnes) to queres as the crtera to dentfy smlar queres (results-based approach) [1, 1

2 2]. Here, query clusters are constructed by calculatng the overlap between the result URLs n response to dfferent queres. However one result URL mght cover dfferent topcs, and queres wth dfferent semantc meanngs mght lead to the same result URLs. In ths paper, we propose a hybrd query smlarty measure for query clusterng that combnes the content-based and results-based approaches. Two queres wll be clustered together f they share some number of dentcal query terms and/or dentcal query results. As dscussed, queres are often too short to convey enough nformaton to deduce ther semantc meanngs. Thus by usng the results to queres, our proposed hybrd approach mght overcome the lack of contextual nformaton n whch the queres are used. However the resultsbased approach s affected by the possblty that queres representng dfferent nformaton needs mght lead to the same results snce one result URL can contan nformaton n several topcs. Hence, n order to compensate for ths drawback, query terms are consdered as complementary nformaton for determnng smlarty. Consequently, we hypothesze that a combnaton of both methods wll help us detect smlar queres more effectvely and thus generate better query clusters. Our expermental results ndcate that ths hybrd approach generates better clusterng results than usng content-based or results-based approach separately. query terms are not able to convey much nformaton or help to detect the meanngs behnd them snce the same term mght represent dfferent semantc meanngs, whle on the other hand, dfferent terms mght refer to the same semantc meanng [2]. 2.2 Results-based approaches Raghavan and Sever [2] determne smlarty between queres by calculatng the overlap n documents returned by the queres. Ths s done by convertng the query result documents nto term frequency vectors. The smlarty between two queres s then decded by comparng these vectors. Ftzpatrck and Dent [7] further developed ths method by weghtng the query results accordng to ther poston n the result lst. They argue that the begnnng of a result lst s more lkely to nclude a relevant document to the orgnal query. Usng the correspondng query results s useful n boostng the performance of query clusterng n terms of precson and recall [2, 7]. However ths method s tme consumng to execute and s not sutable for onlne search systems [7]. Glance [1] thus uses the overlap of result URLs as the smlarty measure nstead of the document content. Queres are posted to a reference search engne and the smlarty between two queres s measured usng the number of common URLs n the top 50 results lst returned from the reference search engne. 2 Related Work In ths secton, we revew two common technques n the lterature used to cluster queres. 2.1 Content-based approaches Tradtonal nformaton retreval research suggests an approach to query clusterng by comparng query term vectors. Ths can be done by smply calculatng the overlap of dentcal terms between queres or usng varous smlarty measures ncorporatng term weghts such as cosne and Jaccard smlarty [4]. These measures have provded good results n document clusterng due to the relatvely large number of terms contaned n documents. Such methods are also straghtforward to mplement for query clusterng. However the content-based approach mght not be approprate for query clusterng snce most queres submtted to search engnes are qute short. A recent study on a bllonentry set of queres to AltaVsta revealed that more than 85% of queres contaned less than three terms and the average length of queres was 2.35 [6]. Thus 3 Query Smlarty Measures As dscussed, our approach to query clusterng uses a hybrd method based on the analyss of query terms and query results. Here, two queres are smlar when (1) they contan one or more terms n common; or (2) they have results that contan one or more tems n common. The remander of ths secton provdes defntons of dfferent query smlarty measures used n our experments. Our method of constructng query clusters based on dfferent query smlarty measures s also presented. 3.1 Content-based smlarty measure We borrow concepts from nformaton retreval [4] and defne a set of queres as Q={Q 1, Q 2 Q, Q Q n }. A query Q s converted to a term and weght vector shown n (1), where q s an ndex term of Q and w Q represents the weght of the th term n query Q. To compute the term weght, we defne the term frequency, tf Q, as the number of occurrences of term n query Q and the query frequency, qf, as the number of queres n a collecton of n queres 2

3 that contans the term. Next, the nverse query frequency, qf, s expressed as (2), n whch n represents the total number of queres n the query collecton. We then compute w Q based on (3): Q = { < q, w1 Q > ; < q, w2 Q > ;... < q, wq } (1) 1 2 > n qf = log( ) (2) qf w = tf qf (3) Q Q * Gven Q, we defne C as (4) whch represents the common term vector of two queres Q and Q. Here, q refers to terms that belong to both Q and Q. C = { q : q Q Q ) (4) Gven these concepts, we provde one defnton of query smlarty: Defnton I: A query Q s smlar to query Q f C >0, where the C s the number of common terms n both queres. Takng the term weghts nto consderaton, we can use any one of the standard smlarty measures [4]. Here, we only present the cosne smlarty measure snce t s most frequently used n nformaton retreval: Sm k k cw cw Q Q = 1 _ cosne( Q, Q ) = (5) k 2 2 cw = Q * cw 1 = 1 Q where cw Q refers to the weght of th common term of C n query Q. 3.2 Result URLs-based smlarty measure The results returned by search engnes usually contan a varety of nformaton such as the ttle, abstract, topc, etc. Ths nformaton can be used to compare the smlarty between queres. In our work, takng the cost of processng query results nto consderaton, we consder the query results unque dentfers (e.g. URLs) n determnng the smlarty between queres as n [1, 8]. Let U(Q ) represent a set of query result URLs to query Q : U ( Q ) = { u, u 2,.... u } (6) where u represents the th result URL for query Q. We then defne R as (7), whch represents the common query results URL vector between Q and Q. Here u refers to the URLs that belong to both U(Q ) and U(Q ). R = { u : u U( Q ) U( Q )} (7) Next, the smlarty defnton based on query result URLs can be stated as: Defnton II: A query Q s smlar to query Q f R >0, where the R s the number of common result URLs n both queres. The smlarty measure can be expressed as (8): R Sm_ result( Q, Q ) = (8) Max( U( Q ), U ( Q ) ) where the U(Q ) s the number of result URLs n U(Q ). Note that ths s only one possble method for calculatng smlarty usng result URLs. Other measures such as usng the overlaps of document ttles or doman names n the result URLs may be used. 3.3 Hybrd smlarty measure The content-based query clusterng approach groups dfferent queres wth the same or smlar keywords. However, a sngle query term can represent dfferent nformaton needs. The results-based approach determnes the relatonshp between queres usng the results returned by a search engne. Ths method uses more contextual nformaton for a gven query. However, the same document n the search results lstngs mght contan several topcs, and thus queres wth dfferent semantc meanngs mght lead to the same search results. Whle the content-based approach may not be sutable for query clusterng by tself, query terms have been shown to have the ablty to provde useful nformaton for clusterng [5]. Therefore, we beleve that the content-based approach can augment the results-based approaches and compensate for the ambguty nherent n the latter. Hence, unlke [1], we assume that a combnaton of both methods wll provde more effectve clusterng results than usng each of them ndvdually. Based on ths hypothess, we defne a hybrd smlarty measure as (9): Sm _ hybrd ( Q, Q ) = (9) α * Sm _ result ( Q, Q ) + β * Sm _ cosne ( Q, Q ) where α and β are parameters assgned to each smlarty measure, wth α+β=1. Here, α and β represent varyng levels of contrbuton a partcular approach (results-based or content-based respectvely) has n determnng the smlarty between queres. 3.4 Determnng query clusters Gven a set of queres Q={Q 1, Q 2.. Qn} and a smlarty measure between queres, we next construct query clusters. Two queres are n one cluster whenever ther smlarty s above a certan threshold. We construct a query cluster G for each query n the query set 3

4 usng the defnton n (10). Here Sm(Q, Q ) refers to the smlarty between Q and Q whch can be computed by usng the varous smlarty measures dscussed prevously. G ( Q ) = { Q : Sm ( Q, Q ) threshold } (10) where 1 < < n; n s the total number of query. Note that there are alternatve clusterng algorthms besdes the one used n our experments [9]. Compared wth these approaches, our method s relatvely less tme consumng. The advantage s that t s sutable for use n systems that need to respond and recommend queres n real tme. 4 Query Clusterng Experments We collected sx-month query logs (around two mllons queres) from the Nanyang Technologcal Unversty dgtal lbrary. The query logs were n text format and were preprocessed to extract the query terms (see Table 1 for examples). For our experments, queres were used of whch 23% of the queres contaned one keyword, 36% contaned two keywords, and 18% contaned three keywords. Further, approxmately 77% of the queres contaned no more than three keywords. The average length of all the queres was Ths observaton s smlar to prevous studes [6]. The queres contaned ndvdual terms. It s nterestng to observe that there were 9503 dstnct terms wthn the sample of queres. Therefore, each dstnct term appeared 3.97 tmes on average showng that people tend to use smlar keywords to express ther nformaton needs. cards game communcaton between people fabrcaton of CMOS handbook data mnng moble phone works NT matrx compostes Table 1. Examples of queres We calculated the smlarty between queres usng the followng smlarty measures: Content-based smlarty (sm_cosne) Results-based smlarty (sm_result) Hybrd smlarty (sm_hybrd) Computaton for sm_cosne was straghtforward usng functon (5). For sm_result, we posted each query to a reference search engne (Google) and retreved the correspondng result URLs, smlar to [1]. By desgn, search engnes rank hghly relevant results hgher, and therefore, we only consdered the top 10 result URLs returned to each query. The result URLs were then be used to compute the smlarty between queres accordng to functon (8). For the hybrd approach, the ssue was to determne the values for the parameters α and β. We used pars of α and β wth the followng values respectvely: (0.25, 0.75), (0.5, 0.5) and (0.75, 0.25). Due to space constrants, we only report results for α=0.25 and β=0.75 snce ths par of values generates the best qualty query clusters. These values ndcate that sm_hybrd was a weghted combnaton of 25% for sm_result and 75% for sm_cosne. Recall that two queres are n a cluster whenever ther smlarty s above a certan threshold. Threshold s the mnmum value, obtaned from a gven smlarty measure, that determnes whether two queres should be clustered nto to the same group. Thresholds were set to 0.25, 0.5, 0.7 and 0.9. In our experments, the qualty of query clusters s measured by average cluster sze, coverage, precson and recall. Average cluster sze sheds lght on the ablty of the dfferent measures to provde recommended queres to a gven query. In other words, ths value reflects the varety of recommended queres to a user. Coverage s the ablty of the dfferent smlarty measures to fnd smlar queres for a gven query. It s the percentage of queres for whch the smlarty measure s able to provde a cluster. Ths value wll ndcate the probablty that the user can obtan recommended queres for hs/her ssued query. Precson refers to the rato of the number of smlar queres to the total number of queres n a cluster. For precson, we randomly selected 100 clusters and checked each query n the cluster manually as done n [5]. Snce the actual nformaton needs represented by the queres are not known, the smlarty between queres wthn a cluster was udged by a human evaluator who took nto account the query terms as well as the result URLs. The average precson was then computed for the 100 selected clusters. Recall refers to the rato of the number of smlar queres to the total number of all smlar queres across the query set (those n the current cluster and others). However ts calculaton posed a problem as t was dffcult to compute drectly because no standard clusters were avalable n the query set. Therefore, an alternatve measure of recall was used. Here, recall s defned as the rato of the number of correctly clustered queres wthn the 100 selected clusters to the maxmum number of the correctly clustered queres n the query set, as done n [5]. The number of correctly clustered queres wthn the 100 selected clusters equals to the total number of queres n the 100 selected query clusters multpled by the average precson. In our work, the 4

5 maxmum number of correctly clustered queres was 1549, whch was obtaned by sm_cosne wth the threshold of Results and Dscusson By varyng the smlarty thresholds, we obtaned dfferent average cluster szes as shown n Fgure 1. Along wth the change of threshold from 0.25 to 0.9, the average cluster sze of sm_hybrd decreases from to 12.24, sm_cosne decreases from to 8.06 and sm_result decreases from 2.63 to It can be seen from the fgure that when the threshold s less than 0.7, sm_hybrd ranks second whle t ranks frst when the threshold s more than 0.7. Further, sm_hybrd and sm_cosne have average cluster szes consstently larger than sm_result. Ths ndcates that for a query cluster, the hybrd and content-based approaches can fnd a larger number of queres for a gven query than the results-based approach. Stated dfferently, the hybrd and content-based approach can provde a greater varety of queres to a user gven a submtted query. average cluster sze threshold hybrd cosne result Fg.1 Average cluster szes For coverage, sm_hybrd decreases from 82.29% to 8.43%, sm_cosne decreases from 82.74% to 18.02% and sm_result decreases from 22.03% to 6.99%, wth the change of threshold from 0.25 to 0.9 respectvely. Fgure 2 shows that sm_cosne ranks hgher n coverage wth sm_hybrd consstently rankng second. Ths demonstrates that the contentbased approach has a better ablty to fnd smlar queres from a gven query than the other approaches. The fact, as dscussed prevously, that users tend to use smlar terms to express ther nformaton needs mght account for the better performance of the content-based approach n terms of coverage. On the other hand, the number of dstnct URLs s often large. Ths mght explan the poorer performance of sm_result n terms of coverage snce many smlar queres cannot be grouped together due to a lack of common result URLs [8]. coverage % 80.00% 60.00% 40.00% 20.00% 0.00% threshhold hybrd cosne result Fg.2 Coverage Fgure 3 ndcates that the results-based approach s better able to cluster smlar queres correctly than the other approaches. In terms of precson, sm_result ncreases from 93.33% to 100%, along wth the change of smlarty threshold from 0.25 to 0.9. Ths ndcates that almost all of the queres n the cluster were consdered smlar. When the threshold equals 0.9, the precson of sm_result reaches the peak, 100%, whch ndcates that there are no rrelevant queres n the clusters. Here, sm_hybrd ranks second across all thresholds. Ths tme, the content-based approach suffers from poorer performance. The precson of sm_cosne s consstently below that of sm_result and sm_hybrd. Ths could be due to the short length of queres and the lack of the contextual nformaton n whch queres are used. On other hand, for sm_result, the reference search engne tends to return the same URLs to semantcally related queres [1, 9] whch mght account for the good performance of the results-based approach n terms of precson. precson % 80.00% 60.00% 40.00% 20.00% 0.00% threshold hybrd cosne result Fg.3 Precson For recall, sm_cosne has the best performance at 100% when the threshold equals 0.25, ndcatng that all smlar queres were contaned n the query clusters (see Fgure 4). It s nterestng to observe that sm_cosne outperforms sm_hybrd when the threshold s less than 0.5 whle sm_hybrd performs better when the threshold s larger than 0.5. The 5

6 reason s that snce the recall calculaton ncludes average cluster sze (refer to the defnton of recall n Secton 4.3), the value changes n accordance wth average cluster sze (see Fgure 1). Further both sm_hybrd and sm_cosne outperform sm_result n terms of recall. The low average cluster sze of sm_result mght account for ths. Our experments show that takng all qualty metrcs nto consderaton, sm_hybrd provdes a balanced set of performance results when compared wth the other approaches as shown n Table 2. The average value across all thresholds n terms of the dfferent qualty crtera was used to generate ths table. The approach whose average value s larger s regarded as better. For example, users wll obtan larger average cluster szes usng the content-based approach whle the precson of the recommended queres wll be poorer. Compared wth sm_cosne, sm_hybrd mproves the precson of the query clusters wthout sacrfcng the other aspects of cluster qualty sgnfcantly. On the other hand, the results-based approach mproves average precson but suffers from poorer coverage and recall. In summary, Table 2 shows that sm_hybrd has a more consstent performance n terms of the varous cluster qualty crtera, rankng n ether the Good or Better category, whle the other approaches exhbt greater fluctuaton n terms of performance across all crtera. recall 100% 80% 60% 40% 20% 0% threshold hybrd cosne result Fg.4 Recall Average Coverage Precson Recall cluster sze Better cosne cosne result hybrd Good hybrd hybrd hybrd cosne Worse result result cosne result Table 2. Comparson of dfferent smlarty measures 6 Concluson Our experments lends support to our ntal hypothess that the hybrd approach wll generate better query clusters than usng the content-based or results-based approaches alone. Our work can contrbute to research n query mnng whch harnesses the doman knowledge and search experences of other nformaton seekers. The results reported here can be used to develop new systems or further refne exstng systems that determne and cluster smlar queres n query logs, and augment the nformaton seekng process by recommendng related queres to users. In addton to the ntal experments performed n ths research, alternatve defntons of smlarty between queres wll also be nvestgated. For example, the result URLs can be replaced by the doman names of the URLs to mprove the coverage of the results-based query clusterng approach. Experments usng other clusterng algorthms [9] mght also be conducted to assess cluster qualty. Fnally, word relatonshps such as hypernyms and synonyms can be used to replace query terms before computng the smlarty between queres to ncrease the coverage as well as average cluster sze. References: [1] N.S. Glance, Communty search assstant, Proceedngs of the 6 th ACM Internatonal Conference on Intellgent User Interfaces (Santa Fe, January 2001), [2] V.V. Raghavan & H. Sever, On the reuse of past optmal queres, Proceedngs of the 18 th Annual Internatonal ACM SIGIR Conference on Research and Development n Informaton Retreval (Seattle, July 1995), [3] C.J. Crouch, D.B. Crouch & K.R. Kareddy, The automatc generaton of extended queres, Proceedngs of the 13 th Annual Internatonal ACM SIGIR Conference (Brussels, September 1990), [4] G. Salton & M.J. McGll, Introducton to modern nformaton retreval, McGraw-Hll New York, [5] J.R. Wen, J.Y. Ne & H.J. Zhang, Query clusterng usng user logs, ACM Transactons on Informaton Systems, Vol.20, No.1, 2002, [6] C. Slversten, M. Henznger, H. Maras & M. Morcz, Analyss of a very large Altavsta query log, DEC SRC Techncal Note, Vol.1998, No.14, [7] L. Ftapatrck & M. Dent, Automatc feedback usng past queres: socal searchng? 6

7 Proceedngs of the 27 th Annual Internatonal ACM SIGIR Conference on Research and Development n Informaton Retreval (Phladelpha, July 1997), [8] R.Z. Osmar & S. Alexander, Fndng smlar queres to satsfy searches based on query traces, Workshops of the 8 th Internatonal Conference on Obect-Orented Informaton Systems (Montpeller, September 2002), [9]A.K. Jan, M.N. Murty & P.J. Flynn, Data clusterng: a revew, ACM Computng Surveys, Vol.31, No.3, 1999,

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