Towards Adaptive Information Merging Using Selected XML Fragments

Transcription

1 Towads Adaptive Infomation Meging Using Selected XML Fagments Ho-Lam Lau and Wilfed Ng Depatment of Compute Science and Engineeing, The Hong Kong Univesity of Science and Technology, Hong Kong {lauhl, Abstact. As XML infomation polifeates on the Web, seaching XML infomation via a seach engine is cucial to the expeience of both casual and expeienced Web uses. The etuned XML fagments in the list is not diectly usable, if not confusing, to the uses, since in most cases the XML fagments extacted fom a lage XML epositoy ae incomplete, scatteed and edundant. Thus, it is necessay to e-iteate the seaching pocess based on use pefeences in ode to obtain moe complete, detailed and usable esults. In this pape, we popose a unifying famewok which takes seaching, meging and use pefeences into account. We view seach queies and fagment labeling as an input in an on-going seaching pocess, in which the elevant XML fagments ae meged into a concise fom and etuned to the use a anked esult list. 1 Intoduction As the amount and use of XML data continue to gow, seaching and anking XML data has been an impotant issue studied in both database and infomation etieval communities [1 4, 6, 7, 10]. Following the usual pactice of handling esults in Web seach engines, the seach esults of these poposals ae usually pesented as a anked list of small XML fagments to the uses [1,3, 11]. In pactice, uses do not have the schema knowledge of the undelying XML souces o have vey little infomation of the data souces, theefoe, highly stuctued XML queies such as XQuey FT expessions fo seaching ae not easy fo them to fomulate. In addition, we ecognize that the usual appoach adopted by web seach engines, which etun a once-off list of items as the answes fo a seach quey, is not adequate in XML setting. Thee ae thee easons fo the inadequacy. Fist, the taget infomation may be scatteed on the anked list and thus it is not diectly useful fo the uses. Second, the XML fagments can be duplicated in diffeent ways. Thid, a once-off quey may not contain all desied infomation. In this pape, we popose a unifying famewok which takes seaching, meging and use pefeence into account. Figue 1 shows the conceptual oveview of ou poposed famewok. Fist, the use submits a quey to the system and the system etuns a list of fagments to the use. Then, the use selects the pefeed fagments as feedback, the feedback will be meged and contibute as new seach quey which enlage the

2 2 Ho-Lam Lau, Wilfed Ng feedback Mege use seach XML souce Seach fagments with quality metics Quality Metics Fig.1. A conceptual oveview of the poposed famewok set of candidate fagments fo the next iteation. Finally, the meged fagment and the new seach esults ae etuned to the use with ou peviously poposed notions of Quality Metics (QMs) [9] which help uses to judge the quality of fagments. We do not epeat the details in [9] hee but mention that the QMs poposed ae simple but effective metics to assess the quality of individual data souce o a combination of data souces, and ae natual metics to measue diffeent dimension of the stuctue, data and subtees. We contibute two main ideas elated to seaching XML infomation. Unifying Famewok We popose a unifying famewok that seaches and meges XML fagments in a anked esult list. The seach is based on a fagment, which is viewed as a set of path-key pais. Adaptive Meging We popose an adaptive meging appoach and fou diectional seaching techniques, that ae able to suppot pogessive meging the seach esults accoding to the uses continuing feedback. With the combination of seaching and meging, we povide flexibility on meging that match diffeent uses pefeences. Pape Oganization. Section 2 pesents an oveview of the unified famewok fo seaching and meging techniques. Section 3 illustates the meging techniques and intoduces the meging appoach fo adopting the use feedback. We conclude the famewok and discuss futue wok in Section 4. 2 The Unifying Famewok fo Seaching and Meging In this section, we pesent an oveview of ou unifying famewok fo seaching and meging. A path-key pai is an odeed pai (p, k), whee p is a path fom the oot to the paent node of the keywod, k. Thus, a path-key pai can be viewed as a simplified fom of XPath [5]. An XML fagment is a sequence of non-epeated path-key pais. Figue 2 depicts the basic ideas of ou famewok which is able to incopoate the use pefeence and to suppot iteative seaching and meging. Initially, the use submits a quey to the seach engine. We view the souces as the undelying XML database which collects XML fagments in a epositoy. Due to the space limitation, we do not descibe the implementation details and the seaching and anking mechanism of the databases. Howeve, we emak that ou appoach of seaching and anking techniques of XML fagments ae simila to the ecent wok in [1, 3,4,7, 10]. The seach engine etuns the list of anked XML fagments as the aw list. The aw list is decomposed into candidate path-key pais soted by the fequency in the aw list. The top k path-key pais is then displayed to the use (By

3 Towads an Adaptive Infomation Meging Using Selected XML Fagments 3 Use Initial Quey Fagment Q 1 Seach Engine Tags Statistic / Schema Infomation Use Feedbacks 3 Pefeence Analyst upwad downwad fowad backwad 5b base candidate... path-key pais 5a Metic Calculato 4 Additive Incement Mege Fagment Decompose 2 6 XML XML XML etuned XML Mege fagments Database A Database BDatabase C Fig.2. Oveview of seaching and meging XML infomation via key-tags default, k = 10). Initially, we categoize all the path-key pais as unclassified. The use feedback can be collected when he/she selects the pefeed path-key pais fom the candidate path-key pais, which is simila to collecting the clickthough data in the case of HTML data [8]. Howeve, the main diffeence between seaching HTML data in the mentioned wok and seaching XML data in ou appoach is that an XML fagment etuned can be futhe used as a sample fo e-queying. The use feedback is collected by the Pefeence Analyst and is e-classified into two categoies as follows: pefeed, and unclassified. Afte the (e-classification) pocess, the two categoies of path-key pais ae passed to the AIM. The pefeed path-key pais fom the use contibute the meging pocess in twofold. Fist, the AIM establishes the esult fagment by meging the pefeed path-key pais. The esult fagment is then etuned to the use. Second, they ae seved as new queies (i.e. e-queies) that ae sent to the fou seaches of Upwad, Downwad, Fowad and Backwad. The seach esults of the e-queies will be decomposed, added into the candidate pathkey pais and then displayed to the use in the next iteation. Moe details about AIM and Diectional Seaching will also be given in Section 3. 3 The Meging and Seaching Appoaches In this section, we explain ou appoach, the Adaptive Incement Meging (AIM) appoach, which suppots futhe decomposing the selected fagments fom uses into path-key pais. We also discuss the fou diectional seachings which ae able to enich the set of candidate path key pais in the e-queying pocess. 3.1 The Adaptive Incement Meging Appoach The inputs of AIM ae two lists: the pefeed and unclassified path-key pais and the outputs ae the esult fagment, R, and a list of eodeed candidate path-key pais, C. R is an XML fagment esulting fom meging the path-key pais in the pefeed list, which is possible to gow duing the meging pocess. C is a list of path-key pais displayed to the uses fo use feedback in each iteation. The path-key pais in C ae gouped accoding to thei data souces and ae soted accoding to the thei fequency among the list of fagments etuned by the seach engine. The AIM appoach is shown in Algoithm 1.

4 4 Ho-Lam Lau, Wilfed Ng Algoithm 1: Adaptive Incement Meging Appoach Input: R the esult fagment; P a set of positive path-key pais; U a set of unclassified path-key pais; ϱ a quota vaiable; S[ ] an aay of sets which epesent the souces; ϖ[ ] an aay of weights fo the souces; // e.g. ϖ[j] is the weight of souce S[j] Output: R the esult fagment; C a list of candidate path-key pais display fo next iteation; fo each souce S[j] do S[j] = ; Mak S[j] as negative souce; C = ; fo each path-key pais p i P do R = R p i; if p i is oiginated fom souce S[j] then S[j] p i; Mak S[j] as positive souce; fo each negative souce S[j] do ϖ[j] = ϖ[j]/2; fo each positive souce S[j] do ϖ[j] = 1 weights of negative souces ; numbe of positive souces C = C top-(ϱ ϖ[j]) path-key pais in S[j]; Pefom Diectional Seaching using (P, U); Retun R and C; Conside the following example, given a quey, Q = (//autho : May, //title : XML) and the fagments etuned by the seach engine is shown as tees in Figue 3(a). The fist step of AIM is to decompose the fagments into candidates pathkey pais and allows use to select his/he desied path-key pais as shown in Figue 3(b). In this example, we have thee souces, F 1, F 2 and F 3 ae fom the souces, S A, S B and S C espectively. We can see that the path-key pais ae soted accoding to thei fequency (i.e. the numbe of thei appeaance in the aw list). Fo example, thee path-key pais whose path equal to //pub/autho ae at position 1 to 2 of the souce S A. At the fist iteation, the weights fo the souces ae {0.3333, , }, theefoe the top-3 pathkey pais fom each souce will be displayed to the use in next iteation. Since thee ae only nine path-key pais in this case, we need an additional path-key pai in ode to have ten path-key pais fo use to select, we may simply add the fouth path-key pai fom eithe S A, S B o S C, and in this example, we add the fouth path-key pais fom S A. The ten candidate path-key pais fo next iteation is shown in bold lettes in Figue 3(b). Now, assume the use selects all path-key pais fom S B and S C. The esult fagment is shown in Figue 3(c). We can see that the esult fagment is built as expected. With the use feedback, the weight of S A is halved and S B and S C shae the deceased weight of S A, the new weights ae {0.1667, , }. The candidate path-key pais ae shown in bold in Figue 3(d).

5 key RJ Sai Towads an Adaptive Infomation Meging Using Selected XML Fagments 5 pub autho autho Stong F 1 title XMLIndex Path... yea 1980 autho XML publication title XML 42 Seach aea title authos XML autho autho Joins May.F Pete. L F 3 initpage //pub/autho :Sai //pub/autho :Stong SigmodRecod S A //pub/key :RJ-2736 //pub/yea :1980 aticle //pub/title : XML Index Path... Page authos //SigmodRecod/aticle/authos/autho :May. C //SigmodRecod/aticle/authos/autho :Ken 65 autho S B //SigmodRecod/aticle/title: XML Seach //SigmodRecod/issue/aticles/aticle/initPage: 42 May.C Ken //SigmodRecod/issue/aticles/aticle/Page: 65 F 2 //publication/authos/autho :May. F //publication/authos/autho :Pete. L S C //publication/title: XML Joins //publication/aea: XML (a) (b) S A //pub/title : XML Index Path... title autho May. C Ken //SigmodRecod/aticle/initPage: 42 S B //SigmodRecod/aticle/Page: 65 XML Seach (m, 2) S C //publication/aea: XML XML Joins (m, 3) (c) May. F Pete. L (d) Fig. 3. (a)the etuned fagments by the quey Q, (b) the coesponding decomposed path-key pais (c) meged esult fagment and (d) candidate path-key pais afte the fist iteation 3.2 Fou Diectional Seaching Appoaches In this section we intoduce fou diectional seaching appoaches used in the e-queying pocess. They ae upwad, downwad, fowad and backwad seachings. Upwad Seaching. The objective of upwad seaching is to find a set of fagments with simila stuctue but diffeent data values. Given a quey, Q, and the list of pefeed path-key pais in pevious iteation, P. We fomulate a e-quey, q i, fo each path-key pai in P, f i = (p i, k i ) P, whee p i is the path fom the oot to the paent node of the keywod, k i. We check if p i is located at the oot of the document. If yes, we stop, since we cannot go up anymoe, othewise, the e-quey is given by ρ 0 //ρ n :, whee ρ 0 is the oot of p i and ρ n is the paent node of k i. Downwad Seaching. The objective of downwad seaching is to find a set of fagments which can povide futhe details accoding to use pefeence. We fomulate a e-quey which aims at the childen o siblings of the pefeed path-key pais. Given a quey, Q, and the list of pefeed path-key pais in pevious iteation, P. We fomulate a e-quey, q i, fo each path-key pai in P, f i = (p i, k i ) P, whee p i is the path fom the oot to the paent node of the keywod, k i. The e-quey, q i is given by ρ 0 //ρ n / : k i, whee ρ 0 is the oot of p i and ρ n is the paent node of k i. The Fowad Seaching. The coe idea of fowad seaching is to seach elevant fagments that ae ignoed in the initiate quey (i.e. the quey submitted by the use at the vey beginning) by poviding moe detailed quey fo moe accuate esults. Given the initiate quey, Q, and the pefeed path-key pais in pevious iteation, P. Fo each path-key pais f i = (p i, k i ) P, if f i does

6 6 Ho-Lam Lau, Wilfed Ng not exactly match with any path-key pais in Q, we submit the e-quey, i, as //ρ n : k i, whee ρ n is the paent node of k i. The Backwad Seaching. The backwad seaching is simila to fowad seaching but in opposite diection. Backwad seaching aims to find infomation that match the initiate quey, Q, but ae diffeent fom the path-key pais in P. Given a quey, Q, and the list of pefeed path-key pais in pevious iteation, P. Fo each path-key pais f i = (p i, k i ) P, if f i does not exactly match with any path-key pais in Q, we submit the e-quey, i, as Q //ρ n : (NOT k i ), whee ρ n is the paent node of k i. 4 Conclusions An inteesting contibution in ou poposed famewok is to unify the pocesses of seaching XML fagments and meging the uses pefeed XML fagments etuned fom the anked esult list. We suggest ewiting the queies using path-keys of the set of coe paths in ode to incease the seaching coveage. We poposed the appoaches of Additive Incement Meging and Diectional Seaching in ode to geneate moe usable esults in a pogessive manne. The ideas pesented in this shot pape pave the way to pomote a wide use of XML data, since fagment seach is simple enough fo existing uses to seach the XML infomation systems. In addition, the mege povides moe usable and quality infomation accoding to the uses pefeences. This pape is a gound wok fo many inteesting issues fo futhe study. Fo example, we can futhe examine seveal schemes in ode to estimate path-key similaity in the meging pocess. This also allows us to ext ou famewok fo seaching and meging XML and HTML data, which seves as a moe useful tool fo seaching heteogenous Web data. Refeences 1. S. Ame-Yahia, N. Koudas, A. Maian, D. Sivastava, and D. Toman. Stuctue and content scoing fo xml. In Poc. of VLDB, J. Beme and M. Getz. XQuey/IR: Integating XML document and data etieval. In WebDB, D. Camel, Y. S. Maaek, M. Mandelbod, Y. Mass, and A. Soffe. Seaching XML documents via XML fagments. In SIGIR, pages , T. T. Chinenyanga. Expessive and efficient anked queying of XML data, Wold Wide Web Consotium. Xquey 1.0 and xpath 2.0 full-text. 6. N. Fuh and K. Goßjohann. XIRQL: An extension of XQL fo infomation etieval, L. Guo, F. Shao, C. Botev, and J. Shanmugasundaam. XRANK: Ranked keywod seach ove XML documents, T. Joachims. Optimizing seach engines using clickthough data. In SIGKDD Ho-Lam Lau and Wilfed Ng. A unifying famewok fo meging and evaluating XML infomation. In DASFAA, pages 81 94, A. Maian, S. Ame-Yahia, N. Koudas, and D. Sivastava. Adaptive pocessing of top-k queies in XML. In ICDE, pages , Matin Theobald, Ralf Schenkel, and Gehad Weikum. An efficient and vesatile quey engine fo topx seach.