Personalization on E-Content Retrieval Based on Semantic Web Services

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1 Iteratioal Joural of Computer Iformatio Systems ad Idustrial Maagemet Applicatios. ISSN Volume 5 (2012) (2013) pp MIR Labs, Persoalizatio o E-Cotet Retrieval Based o Sematic Web Services A.B. Gil 1, S. Rodríguez 1, F. de la Prieta 1 ad De Paz J.F. 1 1 Departmet of Computer Sciece, Uiversity of Salamaca, Plaza de la Merced, Salamaca 37008, Spai {abg, Abstract: I the curret educatioal cotext there has bee a sigificat icrease i learig obect repositories (LOR), which are foud i large databases available o the hidde web. All these iformatio is described i ay metadata labelig stadard (LOM, Dubli Core, etc). It is ecessary to work ad develop solutios that provide efficiecy i searchig for heterogeeous cotet ad fidig distributed cotext. Distributed iformatio retrieval, or federated search, attempts to respod to the problem of iformatio retrieval i the hidde Web. Multi-aget systems are kow for their ability to adapt quickly ad effectively to chages i their eviromet. This study presets a model for the developmet of digital cotet retrieval based o the paradigm of virtual orgaizatios of agets usig a Service Orieted Architecture. The model allows the developmet of a ope ad flexible architecture that supports the services ecessary to dyamically search for distributed digital cotet. A maor challege i searchig ad retrievig digital cotet is also to efficietly fid the most suitable cotet for the users. This model proposes a ew approach to filterig the educatioal cotet retrieved based o Case-Based Reasoig (CBR). It is based o the model AIREH (Architecture for Itelliget Recovery of Educatioal cotet i Heterogeeous Eviromets), a multi-aget architecture that ca search ad itegrate heterogeeous educatioal cotet through a recovery model that uses a federated search. The model ad the techologies preseted i this research exemplify the potetial for developig persoalized recovery systems for digital cotet based o the paradigm of virtual orgaizatios of agets. The advatages of the proposed architecture, as outlied i this article, are its flexibility, customizatio, itegrative solutio ad efficiecy. Keywords: Learig Obect, Repositories, Federated Search, Web Services, CBR, Recommedatio, Multi-Aget System. I. Itroductio There is a large volume of educatioal cotet o the Web that is ot directly accessible through covetioal search egies. This iformatio is said to belog to the so-called hidde, deep, or ivisible Web, as opposed to the cotets foud i the more accessible surface web. The solutios developed by covetioal search egies are very efficiet for retrievig the visible Web cotets. The simplified method based o cetralized recovery model works well whe the iformatio sources have left their cotet exposed to web crawlers. However, this does ot apply i the deep Web, where iformatio ca oly be accessed via search mechaisms adapted to specific sources. This paper presets our research withi this cotext as related specifically to educatioal cotet repositories. The curret eviromet presets a icreasig variety of distributed repositories of educatioal cotet. Repositories are ofte highly heterogeeous, with differet storage systems, access to obects with their ow methods of cosultatio, etc. The problem of heterogeeity i database systems is a ope issue i educatioal repositories. A large umber of iitiatives have bee brought forth to stadardize the processes ad techologies that comprise the issue of heterogeeity, such as Cotet Obect Repository Discovery ad Registratio/Resolutio Architecture (CORDRA) [1], Digital Repositories Iteroperability (DRI) [2] or Learig Obect Discovery & Exchage (LODE) [3]. This paper is focused o Learig Obect Repositories (LOR). May of these repositories do ot have a system that allows a higher level of abstractio betwee the iteral ad ed users of the stored data. Others form etworks usig architectures to facilitate iteroperability. Most are based o various stadards that assig a abstractio layer that coects their iteral characteristics with the exterior characteristics, allowig for greater automatio ad computerizatio for cotaiig LO. Others, such as the MERLOT repository, implemet architectures that eable iteroperability of their cotets by providig offlie cosultatio mechaisms through federated searches usig a web customer service Simple Query Iterface (SQI) [4] (via WSDL specificatio [5]) or through Restful Web services [6,7]. Usig the applicatios that make use of these web services, it would be possible to access the tagged iformatio for learig obects. This iformatio could be displayed i ay of the metadata stadards that exist, maily Learig Obect Metadata (LOM) [8] or o Dubli Core [9]. Cosultig metadata repositories is the mai way to obtai the iformatio eeded to locate learig obects, evaluate their usefuless, ad retrieve them. The obective of this paper is to preset certai sigularities i the effort to adapt sematic web techologies while recoverig iformatio i the field of olie educatio. Maily to show the importace of cotet recommedatio systems based o available sematic iformatio i the search for a ordered maagemet of educatioal resources for MIR Labs, USA

2 244 olie educatio systems. This study presets the AIREH tool (Architecture for Itelliget Recovery of Educatioal cotet i Heterogeeous Eviromets) [10], which makes it possible to search ad recover educatioal resources ecapsulated i the form of a LO. Similarly, a system ca use a CBR (Case-Based Reasoig) system to recommed which educatioal resources might be of particular iterest to the user, based o iformatio from previous uses ad searches. This system is based o Multi-Aget Systems (MAS) usig virtual orgaizatios (VO). II. Gaps i Curret Educatioal Repositories The emergece of what ca already be cosidered as the Learig Obect (LO) paradigm has brought with it a umber of advatages regardig the reuse of learig cotet. While LOs offer facilities related to cotet specificatio, ad the search ad recovery of educatioal resources, the process of iovatio has also produced differet challeges that have ot yet bee resolved. The problems impedig commitmet to the iteroperability of educatioal cotet ca be grouped ito two geeral areas. The first is related to the problems associated with the moolithic structure of learig obect repositories such as lack of reliability or availability, high access times i some cases, erroeous results, poor results, etc. I summary, LORs do ot allow comprehesive user maagemet to solve the LO recovery task with the flexibility ad power ecessary to esure easy iteroperability of dispersed ad heterogeeous sources. The secod importat problem is related to the absece of automatic mechaisms that cotrol the techical quality, sematics ad sytax of Learig Obects, esurig their correct specificatio i ay of the metadata schemas that describe them. For example, the IEEE LOM stadard (IEEE Draft , 2002), specifies the coceptual schema that defies the structures of the data for istaces of LO metadata. The basic schema of LOM [8] is composed of 9 categories (Geeral, Life Cycle, Meta-Metadata, Techical, Educatioal, Rights, Relatio, Aotatio ad Classificatio) ad 47 elemets. Although these 9 categories ca describe the resources very well, LOM is able to embed other metadata stadards usig XML amespaces, like Dubli Core, etc. But the sytactical defiitio aloe is isufficiet, sice there is o obligatio for the attributes to be specified to esure that ay LO has a miimum quality that ca be used withi a particular educatioal cotext, as show i Figure 1. A series of problems i the repositories requires solutios that are adapted to the heterogeeity of each of these repositories, that are isolated, ad that esure real ad effective iteroperability of educatioal cotet globally. A solutio will eable a cetralized global search ad the effective reuse of resources by the ed user i a persoalized way to access the cotets. This requires raisig the level of abstractio ad lookig at the classificatio of systems storig ad searchig for LOs. While i theory this ca be see as a advatage because it icreases the umber of results, i practice it has two drawbacks. The first relates to the respose time, which icreases cosiderably, ad the secod ivolves the repeated occurrece of LOs i the results. Furthermore, this ca be cosidered a additioal challege i the efficiet maagemet of services ad elemets ivolved i this type of platform. This is due to the iclusio of efficiet maagemet techiques usig labeled tags that facilitate the storage, search, retrieval, etc. withi the educatioal cotet. 8. Aotati o 9. Classificatio 1. Geeral 2. Life Cycle 3. Meta Metadata 4. Techical 5. Educatioal 6. Rights 7. Relatio ,08% 32,85% 37,08% 37,08% 37,08% 36,79% 1,31% 6,86% 7,30% 22,48% 21,02% 21,31% 21,31% 22,92% 22,77% 21,75% 27,74% 30,07% 7,01% 11,53% 7,59% 8,47% 6,86% 8,47% 24,82% 32,55% 11,39% 28,03% 16,93% 0,73% 2,04% 11,24% 16,20% 17,37% 17,37% 17,37% 44,09% 40,44% 40,73% 40,73% 40,73% 24,82% 41,31% 3,80% 16,93% 4,96% 20,73% 4,23% 36,93% 93,43% 94,31% 95,18% 47,15% 94,60% Figure 1. Percetage of LOM tag elemets i use A comprehesive solutio for the problem of educatioal cotet retrieval goes beyod ay simple recovery. What is eeded is a filterig mechaism that icludes sematic aspects of the obects retrieved ad that ca be evaluated by geeratig the most suitable results accordig to the user. III. Related Work Gil, Rodríguez, de la Prieta ad De Paz With so may LOR, a maor challege is to fid the most suitable LOs for the users as efficietly as possible. This obective has attracted much research i the field of the selectio ad recommedatio of LO. Researchers ad developers of e-learig have begu to apply iformatio retrieval techiques with techologies for recommedatio, especially collaborative filterig [11], or web miig [12], for recommedig educatioal cotet. A recet review of these applicatios ca be see i [13]. The features that hadle these iformatio filterig techiques i this cotext are the attribute iformatio of educatio items (cotet-based approach) ad the user cotext (collaborative

3 Persoalizatio o E-Cotet Retrieval Based o Sematic Web Services 245 approach). Oe of the first works i this cotext was developed by Altered Vista: a system i which istructioal techiques are evaluated based o collaborative filterig recommedatio with close eighbors [14, 15]. These works explore how to collect user reviews of learig resources ad propagate them through word-of-mouth recommedatios. RACOFI (Rule-Applyig Collaborative Filterig) proposes a collaborative filterig by rules, with a architecture for the custom selectio of educatioal cotet [15]. The author s recommedatio is to combie both approaches to reduce recommedatio by itegratig a collaborative filterig algorithm that works with user ratigs of a set of rules of iferece, which creates a associatio betwee the cotet ad rate of recommedatio. McCalla [16] has proposed a improvemet to collaborative filterig called the ecological approach to desigig e-learig systems. Key aspects of this proposal take ito accout the gradual accumulatio of iformatio, ad focus o ed users. Maouselis et al. [17] have coducted a case study with data collected from the CELEBRATE portal users to determie a appropriate collaborative filterig algorithm. Some solutios take a hybrid approach. [18-21] make use of algorithms based o reviews from other users accordig to iterests which are extracted through earest eighbor algorithms. These correlatio-based algorithms are used to calculate a idex score o the usefuless of learig obects through the aalysis of commets from studets with similar profiles. These algorithms improve preferece-based selectio algorithms by icorporatig aspects of studet prefereces. The preferece patter of each studet is recorded i a history of prefereces that is geerated ad updated accordig to commets from the studet's preferece. If a selected learig obect has bee give a positive score, its preferece score icreases for all the features of the learig obect. The combiatio of the scores for a learig obect is determied by the two algorithms that decide the positio of the learig obect accordig to the outcome of the recommedatio. However, all of the selected learig obects are treated equally without ay distictio betwee them, which would allow more precise assessmet criteria of the user, affectig the very patter of preferece of the user. The use of algorithms based o biological models, such as ACO (At Coloy Optimizatio) is the basis of [22], which proposes a set of attributes based o a coloy of ats (attributes- based at coloy system, AACS) to help studets fid their way through a adaptive model of learig obects more efficietly. This mechaism is based o the use of learig activities ad educatioal elemets to predict the optimal traectories associated with the ACO algorithm, ad recommed the sequece of learig obects. This work is iterestig, but bases its recommedatio o a path of learig through a differet set of learig obects. The ultimate goal is to attai certai kowledge. The recommedatio is the sequece produced by the optimal route betwee the differet LOs. The works by [23, 24, 25] suggest the eed for selectig learig obects by takig ito accout the educatioal cotet described by their metadata, which falls i lie with this thesis. They propose a mechaism called Cotextualized Attetio Metadata (CAM) to capture iformatio about the actios alog the life cycle of learig obects, icludig their creatio, labelig, supply, selectio, use ad maiteace. These studies proposed four metrics to LOM ad CAM for classifyig ad recommedig the learig obects retrieved: Lik Aalysis Rakig, Similarity Recommedatio, Persoalized Rakig ad Cotextual Recommedatio. These metrics classify learig obects accordig to criteria such as popularity rakig, the similarity of obects based o the umber of dowloads, ad more. How these rakigs cotribute to the selectio of learig obects ad how they combie with each other are still ope questios ad a highly iterestig field of study. Based o sematic aspects that cosider cotextual iformatio from the studet's cogitive activities ad the LO cotet structure, Qiya et al. [26] propose a framework for recommedig learig obects to suit the studet's cogitive activities through a approach based o otologies. The same approach follows the work of Ruiz-Iiesta [27] with a framework that simplifies the developmet of recommedatios for LO. There are other approaches, mechaisms or criteria for the categorizatio of educatioal cotet that require direct huma itervetio i their assessmet, but list some criteria for assessig the quality of the cotet. Amog these is the assessmet cotaied i the MERLOT repository or LORI tool. The MERLOT repository (Multimedia Educatioal Resource for Learig ad Olie Teachig) offers the best curret example of widespread applicatio i the evaluatio of educatioal cotet for Web-based educatio [28]. Cotet ratigs are obtaied through commets ad ratigs o a five poit scale by users ad reviewers appoited by MERLOT. This evaluatio is based o three geeral properties: quality of cotet, potetial effectiveess as a tool for teachig ad learig, ad ease of use. The classificatio is based o the quality of search results usig a weighted average of these three classificatios. The peer review process i MERLOT is carried out by two experts workig asychroously who retur the descriptios of the cotets recovered i a list sorted by the rakig, which has bee established i tur by evaluatig the quality of the cotet i descedig order of assessmet, where the cotets are ot evaluated at the ed. LORI (Learig Obect Review Istrumet) is a tool kow to assess the quality of educatio resources o-lie. It is simply a assessmet protocol for learig obects i ie areas o a bridges poit scale that ca be implemeted o-lie by usig rubrics, ratig scales ad commet fields. As a assessmet tool, it is available at its website, which ca be used to assess a idividual or a pael of experts from a rage of LOs, based o the advice of [28, 29] There are a growig umber of papers proposig systems to recommed learig resources, as evideced by the lack of operatioal solutios ad cofirmed by recet work [30]. The evaluated proposals all cocluded that the icorporatio of mechaisms to assess attributes related to the educatioal cotet, as well as aspects of user cotext ad their iteractio with the cotet, create effective recommedatio mechaisms. However, a closer look at the revised proposals uderscores

4 246 Gil, Rodríguez, de la Prieta ad De Paz the lack of applicatios o real systems ad educatioal cotet. Most of the obs listed i this sectio are based o simulatios or have bee applied to a local case study or a particular repository, with a priori cotrol, for small groups of parameters that are usually local. The solutio proposes that ot all display results are from real cotext. Some of the recommeded educatioal cotet obects are ot learig obects, as defied i the preset study, ad the great maority do ot therefore address aspects of sematic taggig of resources i their approach. The architecture proposed i this paper provides multiple perspectives to assess the recovery of educatioal cotet from a real, ope ad scalable eviromet, ad will also will be a support mechaism to implemet the recommedatio or rakig for the recovered LOs. IV. Architecture Overview Aspects The situatio i the preset cotext of educatio urgetly requires a ew type of applicatio that ca search for educatioal cotet i a distributed eviromet across differet formats, servers ad etworks. This paper proposes AIREH (Architecture for Itelliget Recovery of Educatioal cotet i Heterogeeous Eviromets), as a itermediary architecture that itroduces several eeded compoets desiged to simplify the problem: A traslatio feature which trasforms a particular query laguage ito oe that is valid i existig repositories. A federatio feature that seds queries to multiple repositories ad reflects their resposes. A aggregate feature that ca uify metadata from differet repositories, thus allowig the user the best possible choice. I this eviromet, the architecture provides the optimal use of itelliget agets, which ca ow apply their characteristics (autoomy, status, reactivity, ratioality, itelligece, coordiatio, mobility ad learig) to a stable system, ad ca also react itelligetly to the eeds of the eviromet alog several features. The idea of modelig the architecture as a virtual orgaizatio stems from the otio that a orgaizatio ca adapt its actios to ay chage i order to achieve its goals ad iteract with heterogeeous compoets. Give the heterogeeous ad chagig techological situatio that accompaies the proposed educatioal cotext, ad the eed to reuse the data i a real operatioal cotext, we were motivated to desig a model of a itegrated architecture i which a orgaizatio of agets ca execute search ad retrieval actios of educatioal cotet based o a federated search model. The iovatio of this architecture will be to provide a orgaizatio of agets with the self-adaptive capabilities eeded to address the curret problems, ad with the ability to adapt to future chages i highly dyamic eviromets such as those discussed. This model will solve the problems of the distributio ad itegratio of differet repositories, the abstractio of the iteral logic of each repository, ad the classificatio, storage ad retrieval of LOs. I additio it will add the capacity of simple scalability, possible situatios for use of ew protocols, iteral logical repositories, ad catalogig or heterogeeous applicatios desiged to cover service-related features. A. Federated Search The mai cotributio of a federated search is that the search process is doe through search mechaisms i idividual iformatio sources. I additio, the search refers to the locatio of each source ad provides a distributed cotrol of iformatio related to the differet sources of hidde iformatio. The federated search mechaism is thus a much more complex, rich ad comprehesive cetralized recovery model. A federated search used to recover cotet i distributed heterogeeous systems, such as LO repositories, ca be described as the sequece i the resolutio of the three followig subproblems: 1) Selectio of Repositories Durig this phase it aalyzes the descriptio of resources i the repositories ad studies how to represet iformatio that is distributed i them (respose times, efficiecy, etc.). 2) Selectio of Resources This phase determies the eed for iformatio ad provides a set of descriptors i order to recover the results ad decide which results are most likely to satisfy the query usig a recovery algorithm. 3) Merger of Results This phase builds the itegratio ad combiatio of results retured by queries o the -repositories, formig a sigle list that gives the user a raked list of results. The corerstoe of this architecture is the recovery of LO i a real eviromet usig federated searches i differet repositories. It is ecessary to provide the user with a framework that uifies the search ad retrieval of obects, thus facilitatig the learig process that filters ad properly classifies the learig obects retrieved accordig to a set of rules. The geeratio of the rules for the orgaizatio of the items recovered is based o educatioal metadata ad will provide useful cotet to the ed user. Mechaisms will provide documetatio of the recovered obects, which ca be evaluated, ad will geerate the most suitable positio accordig to the user. The architecture provides multiple perspectives to assess the recovery of educatioal cotet. Figure 2. Diagram of the orgaizatioal model (by fuctio)

5 Persoalizatio o E-Cotet Retrieval Based o Sematic Web Services 247 B. Model Overview The desig ad developmet of SMA methodologies eed to support desigers, ad be both robust ad reliable. May traditioal approaches detail the structure of the SMA i terms of a role model, which idetifies the roles that agets play i the system ad the iteractio protocols i which they participate. These methodologies ca be classified as aget-orieted sice they assume a idividualistic perspective by usig a aget with clearly defied tasks ad skills to help the other agets achieve their idividual goals. Closed systems, o the other had, do ot allow the participatio of agets with behavior that is selfish or uauthorized. The proposed AIREH architecture is see as a itermediary commuicatio poit betwee the Learig Obect Repositories (LOR), the LOs that they store, ad users who use them. The system provides a federated search system. I additio, oce the results from the differet repositories have bee received, a idetificatio phase ad filterig process adapt the results to the user prefereces. Figure 2 details the elemets of the orgaizatioal model (fuctioal view), showig the results (products ad services) offered by the system, the type of eviromet, ad iterest groups. To provide these services the platform requires providers, represeted by the LOR, to offer search services that eable iformatio to be harvested. Moreover, the product also offers statistical iformatio o the performace of the repository ad the use of LOs, idetifyig those that are used accordig to the search patters. Figure 3. Orgaizatioal architecture model diagram The missio of the orgaizatio is to maximize the system performace of queries by reducig time ad icreasig performace, ad to maximize the quality of results. Figure 3 shows the fuctioal view (exteral fuctio) model for AIREH associated to the orgaizatio, where services are coected to each other with associated roles ad relatioships (WFProvides/WFUses). C. Roles Acquisitio by Service Facilitator The dyamism of the system, which is desiged as a orgaizatio, ca be reflected by the registratio i differet stages: registratio of ew players, ew services, ew protocols, service requests, ad expulsios from the system. The roles of maagemet ad the services associated to the orgaizatioal uits of this particular orgaizatio will be available through the OVAMAH platform [33]. OMS (Orgaizatio Maagemet System) provides the ecessary services for the proper fuctioig of the aget orgaizatio. It also provides a rage of services to register or uregister structural compoets, i particular, the roles, orms ad existig uits i the system, ad offers facilities to report o those compoets. Figure 4. Example acquisitio role by a exteral aget Figure 4 provides a sceario i which a ew LOR is registered i the orgaizatio. The rakig value idicates the degree of aligmet betwee the service ad the specified service proposed. The uits cotai the Acquire Role, Report Uit ad Stop Role services, i additio to the depedet domai services, which have already bee idetified above. For example, if a exteral LOR wats to cotact them, it is first ecessary to go through the process of acquirig the correspodig role. D. AIREH Recovery Performace Evaluatio The search process is itegrated ito the agets of the orgaizatio ad coected by services i a way that is totally trasparet to the user. This itegratio addresses problems regardig the distributio ad itegratio of differet repositories, the abstractio of the iteral logic of each, ad the classificatio, storage ad search for LOs. Moreover, the system adds capabilities such as easy scalability scearios, use of ew protocols, logical iteral repositories, catalogig, ad heterogeeous applicatios desiged to cover services with related features. The algorithm used to select the effective cotet for the user takes ito accout the sematics of LOs ad the techical aspects for the search i the LOR. This iflueces the cataloged results, which are retrieved automatically through several mechaisms ivolvig user assets. I this paper, the processig of the retrieved LO metadata addresses three aspects: completeess, reliability ad rakig accuracy. Give a set of metadata for a sigle LO recovered for a give repository J, the set of these metadata files is determied by O J ={ O 1 O i } with i=1,. The relevace is related to profit or the potetial use of recovered materials i relatio to achievig the goals,

6 248 Gil, Rodríguez, de la Prieta ad De Paz iterests or problems itrisic to the user. Based o this approach ad i the cotext of this work, the metadata of the LO recovered were categorized by the criterio of relevace based o the same biary operatio: R = {0,1}. For example if the LO caot be recovered because it lacks the tagged iformatio idicatig the source of the resource (the category attribute <techical> <locatio> LOM), it is described as irrelevat ad is credited with ull value (0). Otherwise, it qualifies as a relevat value (1). This approach allows the calculatio of the accuracy of the search egies for each query i LOR J, by usig Equatio 1. P E G R O i 1 (1) R O i 1 m R O i i 1i 1 umberlo time t) 1 (2) ( (3) Give a query Q i a series of Repositories, the full set of metadata recovered will be the uio of all the metadata repositories recovered i m. To calculate the Relative Recall, take the deomiator of the equatio; the sum of the LOs udged relevat to each search for the overall system is determied by the Equatio 2. It is particularly relevat to meet the demads for the LOs that meet the requiremets of a user request i real time. The dyamics of the eviromet i the recovery of resources allow for the user to be provided with a large umber of LOs very quickly, so it is ecessary to have some measure that allows us to evaluate this feature. At preset there is o published system which allows this type of cotrol over the cotet of what is proposed as a ew measure. Equatio 3 represets the temporary Gai, G J (t), for repository J which cotais the measure that relates the umber of LO retrieved for queries over time. Figure 5. Relevat LO recovered The architecture was evaluated by performig a battery of tests to validate its efficiecy i real eviromets. The system is robust agaist failure because it icorporates several methods i differet agets i the orgaizatio throughout the query time by plaig the maagemet of repositories based o the performace of the agets. Oce each istatiated LOR aget performs the query i each repository, each LOR is i charge of cacelig the query ad reportig ay problem affectig the established QoS levels, such as query time, performace of the repository, ad so o. This data reveals the sigificat icrease i the umber of relevat LOs recovered (Figure 5) while the umber of LOs to recover i time decreases (Figure 6). The proposed architecture icreases the temporary gai i the system by 15% o average over isolated repositories. Figure 6. Comparative average temporary gais V. Recommedatio Strategy A recommedatio system is a tool that predicts user likes accordig to their characteristics, iterests or abilities, based o previously obtaied iformatio. There are various techiques based o Artificial Itelligece (AI) which are orieted to carryig out these tasks. Oe of them is Case Base Reasoig (CBR). Recovery techiques ad their adaptatio to CBR techiques have become effective for the developmet of recommeder systems [31, 32]. The purpose of CBR is to solve ew problems by adaptig solutios that have bee used to solve similar problems i the past [33]. A CBR maages cases (past experieces) to solve ew problems. The way cases are maaged is kow as the CBR cycle, ad cosists of four sequetial steps which are recalled every time a problem eeds to be solved: retrieve, reuse, revise ad retai. A CBR depeds largely o the structure ad cotet represetatio ad its collectio of cases. The developed system is characterized by workig with cases defied by the characteristics of the educatioal cotext. Each case is divided ito the followig mai compoets: A set of attributes referred to as target, which cotais the defiitio of the problem, that is to say, the query. A set of attributes associated to the previous user iteractios. Oce the defiitio of the problem is formed i terms of attributes, the obective of CBR is to geerate the rakig of these learig obects i respose to user characteristics that are reflected i the characteristics of learig obects available, such as educatioal level LO, the format or the laguage of the resource. The CBR system is iitiated by a ew request made by the user who is searchig for LOs. At that momet, the CBR system is executed. The iformatio cotaied i the ew case at the begiig of the executio cycle of the CBR system is defied by the followig tuple: c T, u i, x } (4) { i Where T refers to the set of attributes defied i the target extracted maily from the iformatio i the markup laguage i accordace with stadard tagged used (LOM, DC, etc.) i.e. T = {title, laguage, keywords, format...}. The user idetifier is u i ad x i is the value associated with the fial solutio.

7 Persoalizatio o E-Cotet Retrieval Based o Sematic Web Services 249 Figure 7. CBR system implemeted i AIREH Usig the iformatio defied by equatio 4, the reasoig cycle for the CBR system is iitiated. Figure 7 illustrates the reasoig cycle, the CBR system is. Durig the retrieve phase the metadata for the learig obects are dowloaded from differet repositories usig simultaeous searches through a federated search procedure based o a orgaizatio of agets, as explaied i previous sectios. The Slope Oe method is applied durig the reuse phase i order to predict the degree of relevace of the recovered LO. Fially, durig the revise ad learig phase, iformatio related to the user s fial assessmet is stored. The differet steps for the reasoig cycle will ow be explaied i greater detail. Oce the iformatio has bee recovered from the repositories ad the retaied eviromet, differet cases are obtaied accordig to the structure idicated i Equatio 4. The iformatio listed i table 1 is obtaied from the data foud durig the retrieve phase. Each cell cotais a value v i that represets the user s evaluatio of the learig obect. User LO 1 LO 2 LO m u 1 v 11 v 12 v 1m u 2 v v 2m u v v m Table 1. Iformatio retrieved from the cases. The average is calculated for each pair of idividuals, as see i equatio 5. The fial averaged values could be combied accordig to equatio 6, with a weighted average relative to the umber of predictios that exist for each article. d i m 1 ( k 1 v ik v m 1 k ) (5) 1 m d i x (6) 1 ik 1 Where v ik represets idividual i for which the ukow value is beig calculated, m is the umber of values that exist for both articles i ad (if v ik is ukow, v k will ot be cosidered i the calculatio), ad v k is idividual. Where v ik represets idividual i for whom the ukow variable for k is calculated, m is the umber of values that exist for category, v k is idividual. Durig the revise ad retai phase, the user rates the obects retrieved durig the reuse phase. The values are the stored i the cloud for future retrievals. A. Evaluatio ad Results The recommedatio is made by implemetig the CBR proposed mechaism ad accordig to the group of recovered cases. A series of queries were made based o a selectio of 1 m 60 differet keywords from the computer sciece groud extracted from UNESCO codes. To validate the recommedatio proposal, we evaluated the results obtaied by the AIREH assessmet with Merlot 1 ad Loret 2 repositories over a period of 6 moths with 40 users. Each user iput a key word ad the aalyzed the predictios made for the previous 15 predictios. The values were assiged to each item o a scale of 1 to 5. The implemetatio of the algorithms was based o the Apache Mahout library, which provides techiques such as Map Reduce, allowig a high level of efficiecy i multiprocessig systems. Elemets KNN Slope Oe SVD s 39s 38s :37s 5:36s 5:52s Table 2. Compariso of results of the calculatio times. The first step was to compare the executio times for differet alteratives to collaborative filterig i order to determie the viability of the differet solutios. The executio times were based o simulated data, startig with the first test of 500,000 pieces of data ad a secod of 5,000,000. Table 2 lists the calculatio times to obtai the recommedatios. I order to aalyze the efficiecy of the CBR system, the predictios were compared with other methods of collaborative filterig. The techiques selected were KNN (K-Nearest Neighbor) ad SVD (Sigle Value Decompositio). KNN Slope Oe SVD Table 3. Compariso of efficiecy results. While the times for costructig the recommedatios are very similar, the differece is due to the fact that the KNN algorithm eeds the same executio time for ay predictio made for a differet user, while the Slope Oe ad SVD have a predictio time for executio of less tha oe secod, regardless of the user. The results show i Table 3 idicate the average error values obtaied by the methods idicated i each colum. The weighted values are based o a scale of 1 to 5. The results i Table 2 idicate that Slope Oe provided the best results, although very similar to those obtaied by SVD. The reaso for ot usig SVD is that it is ecessary to statistically determie the umber of elemets that reduce the dimesioality, which would ivolve the aalysis of the value with subsequet executios. We also evaluated user perceptio regardig the quality of the recommedatios made by the proposed mechaism throughout the evolutio of the CBR. The evolutio of the umber of cases i the case base allows for greater kowledge ad appreciatio of potetial LO as shows Figure 8. This improvemet is due to the system's ability to lear ad adapt to lessos leared. Likewise, the experieces allow a better adaptatio to the user profile. System success is evaluated through user iteractio with the recommeded LO, as well as the assessmet it makes of each. The user perceives a improvemet i the time of the 1 MERLOT, Multimedia Educatioal Resource for Learig ad Olie Teachig ( 2 LORNET, Learig Obect Repository Networks (

8 250 Gil, Rodríguez, de la Prieta ad De Paz recommeded LO. future studies could iclude the itegratio of richer sematic aspects for the recovery ad catalogig of educatioal cotet. Ackowledgmet This work has bee partially supported by the proect MICINN TIN C We would like also to thak the participats of our user study. Figure 8. Evaluatio of the recommedatios of the CBR Figure 8 also shows that the umber of updated cases decreases as the system acquires experieces (the X axis represets the evaluatios durig the time period ad the y axis quatifies the umber of cases cocerig the aspects evaluated (umber of cases, access to the base ad updates of cases). By icreasig the umber ad variability of the cases captured, the ability to fid cases similar to the query that the user requires icreases ad may validate the recovered LO criteria that defie user tastes ad/or eeds. VI. Coclusios ad Prelimiary Results A importat obective i the developmet of recovery techology i the deep Web cotet is to improve the quality, scope ad accuracy of existig visible Web egies through the use of structured descriptios of resources, i.e., through sematic rich metadata. This is possible if the metadata of these resources are accessible. The proposed architecture ca search multiple repositories simultaeously, a complex problem that is further exacerbated by the heterogeeity of digital repositories. The AIREH architecture provides multiple perspectives to assess the recovery of educatioal cotet from a real, ope ad scalable eviromet, ad also supports mechaisms that will implemet the recommedatio or rakig for recovered LOs. The developmet of a sigle ordered list of Learig Obects that icorporates a user's relevace criteria i this work is oe of the tasks that the AIREH aget model implemets with a CBR reasoig model. This paper has preseted a recovery architecture based o educatioal cotet parter orgaizatios. The mai ovelty i the proposed architecture is its dyamic capability. This ability cofers adaptive plaig to carry out a optimal distributio of the tasks of the orgaizatio's member agets, eablig the retrieval of itelliget cotet ad flexibility i highly dyamic eviromets for which it was created. I summary, the architecture preseted i this study ca defie the actios that a orgaizatio of agets must carry out, aticipate the chages that may occur durig the executio of a give query, ad use adaptive plaig withi a orgaizatio of agets accordig to cotext characteristics (users, profiles, features, cotet, variability of learig obect repositories, etc.). The system is still i a process of developmet ad udergoig more detailed testig, which will allow for more extesive results i the future. With AIREH it is possible for the user to retrieve LO efficietly ad simply, sice it allows the retrieved elemets to be filtered accordig to each user ad their previous actios. Some ew aspects to cosider i Refereces [1] ADL CORDRA (Cotet Obect Repository Discovery ad Registratio/Resolutio Architecture ) from: mets/adl%20registry%20documetatio/adl%20 Registry%20Documetatio.aspx [2] IMS Digital Repositories Specificatio. V [3] David Massart, Nick Nicholas, Nigel Ward. IMS GLC Learig Obect Discovery & Exchage (LODE). Versio 1.0, (Base Documet) Draft 14, March 2, [4] B. Simo, D. Massart, F. Va Assche, S. Terier, & E. Duval. A simple query iterface specificatio for learig repositories, CEN Workshop Agreemet (CWA 15454). Available o Web ftp://ftp.ceorm.be/public/cwas/e-europe/ws-lt/ CWA Nov.pdf, November [5] Web Services Descriptio Laguage (WSDL) 1.1. W3C Note 15 March Available o Web [6] Roy T. Fieldig ad Richard N. Taylor. Pricipled desig of the moder Web architecture. I Proceedigs of the 22d iteratioal coferece o Software egieerig (ICSE '00). ACM, New York, NY, USA, 2000, pp [7] Roy T. Fieldig. Architectural styles ad the desig of etwork-based software architectures. PhD Thesis, Uiversity of Califoria, Irvie, [8] IEEE , Draft Stadard for Learig Obect Metadata. The Istitute of Electrical ad Electroics Egieers, Ic. Retrieved March 23, 2010, [9] DCMI Specificatios [10] Gil, A.-B.; Rodriguez, S.; de la Prieta, F.; Marti, B. Educatioal cotet retrieval based o sematic Web services. Next Geeratio Web Services Practices (NWeSP), th Iteratioal Coferece o, pp , Oct [11] Bobadilla, J., Serradilla, F., Herado, A., & MovieLes. (2009). Collaborative filterig adapted to recommeder systems of e-learig. Kowledge-Based Systems. [12] Khribi, M. K., Jemi, M., & Nasraoui, O. (2009). Automatic recommedatios for e-learig persoalizatio based o web usage miig techiques ad iformatio retrieval. Educatioal Techology & Society, 12(4), [13] Maouselis, N., Vuorikari, R. ad Va Assche, F. (2010), Collaborative recommedatio of e-learig resources: a experimetal ivestigatio. Joural of Computer Assisted Learig, 26:

9 Persoalizatio o E-Cotet Retrieval Based o Sematic Web Services 251 [14] Recker, M., Walker, A., ad Lawless, K. What do you recommed? Implemetatio ad aalyses of collaborative iformatio filterig of web resources for educatio. Istructioal Sciece, 31(4-5), 2003, pp [15] Lemire, D., Boley, H., McGrath, S., ad Ball, M. Collaborative Filterig ad Iferece Rules for Cotext-Aware Learig Obect Recommedatio. Techolodgy ad Smart Educatio, 2(3) pp [16] McCalla, G. The Ecological Approach to the Desig of E-Learig Eviromets: Purpose-based Capture ad Use of Iformatio about Learers. Joural of Iteractive Media i Educatio, 2004 (7) Special Issue o the Educatioal Sematic Web. Volume 1, p. 18, [17] Maouselis, N., Vuorikari, R., ad Va Assche, F. Simulated Aalysis of Collaborative Filterig for Learig Obect Recommedatio. SIRTEL Workshop, EC-TEL [18] Aiua, D., & Baoyig, W. (2008). Domai-based recommedatio ad retrieval of relevat materials i e-learig. I IEEE iteratioal workshop o sematic computig ad applicatios 2008 (IWSCA 08) (pp ) [19] Ghauth, Khairil, Abdullah, Nor. Learig materials recommedatio usig good learers ratigs ad cotet-based filterig. Educatioal Techology Research ad Developmet Spriger Bosto. SN [20] Tsai, K. H., Chiu, T. K., Lee, M. C., ad Wag, T. I. A learig Obect Recommedatio Model based o the Preferece ad Otological Approaches. Proceedig of the Sixth Iteratioal Coferece o Advaced Learig Techologies (ICALT 06) [21] Wag, T. I., Tsai, K. H., Lee, M. C., ad Chiu, T. K. Persoalized Learig Obects Recommedatio based o the Sematic Aware Discovery ad the Learer Preferece Patter. Educatioal Techology ad Society, 10 (3), pp [22] Yag Y. A evaluatio of statistical approaches to text categorizatio. Joural of Iformatio Retrieval 1 (1999) [23] Kerkiri, T., Maitsaris, A., & Mavridou, A. (2007). Reputatio metadata for recommedig persoalized e-learig resources. I Proceedigs of the secod iteratioal workshop o sematic media adaptatio ad persoalizatio (pp ). Uxbridge. [24] Ochoa, X. ad Duval, E. Use of Cotextualized Attetio Metadata for Rakig ad Recommedig Learig Obects. Proceedigs of 1st Iteratioal Workshop o Cotextualized Attetio Metadata: Collectig, Maagig ad Exploitig of Rich Usage Iformatio pp [25] Wolpers, M., Naar, J., ad Duval, E. (2007). Trackig Actual Usage: the Attetio Metadata Approach. Educatioal Techology & Society, 10(3), pp [26] Qiya Ha; Feg Gao; Hu Wag, "Otology-based learig obect recommedatio for cogitive cosideratios," 8th World Cogress o Itelliget Cotrol ad Automatio (WCICA), 2010, vol., o., pp , 7-9 July 2010 [27] A. Ruiz-Iiesta, G. Jiméez-Díaz, M. Gómez-Albarrá. Persoalizació e Recomedadores Basados e Coteido y su Aplicació a Repositorios de Obetos de Apredizae. IEEE-RITA Vol. 5, pp , Núm 1, Feb [28] Vargo, J., Nesbit, J. C., Belfer, K., & Archambault, A. (2003). Learig obect evaluatio: Computer mediated collaboratio ad iter-rater reliability. Iteratioal Joural of Computers ad Applicatios, 25 (3), [29] Nesbit, J. C., Belfer, K., & Vargo, J. (2002). A coverget participatio model for evaluatio of learig obects. Caadia Joural of Learig ad Techology, 28 (3), [30] Maouselis, N., Drachsler, H., Vuorikari, R., Hummel H. ad Koper R. Recommeder Systems i Techology Ehaced Learig. Recommeder Systems Hadbook, pages Spriger, 2011 [31] Miquel Motaer, Beatriz López, ad Josep Lluís de la Rosa Opiio-Based Filterig through Trust. I Proceedigs of the 6th Iteratioal Workshop o Cooperative Iformatio Agets VI (CIA '02), Matthias Klusch, Sascha Ossowski, ad O Shehory (Eds.). Spriger-Verlag, Lodo, UK, [32] Corchado J. M. ad Laza R. (2003). Costructig Deliberative Agets with Case-based Reasoig Techology, Iteratioal Joural of Itelliget Systems. Vol 18, No. 12, December. pp.: [33] J. Koloder, Case-Based Reasoig, Morga Kaufma Author Biographies Aa B. Gil Doctorate i Computer Sciece from the Uiversity of Salamaca. Udergraduate studies i Physics ad MSC from the Uiversity of Salamaca. She is curretly a Assistat Professor i the Departmet of Computer Sciece at Uiversity of Salamaca. Her curret research iterests iclude applied sematic web, software agets, huma-computer iteractio ad commuity-eablig techology. Sara Rodríguez Doctorate i Computer Sciece ad Automatio from the Uiversity of Salamaca. Obtaied degree i Techical Egieerig ad Computer Sciece Systems i 2004 ad Computer Sciece Egieerig i May 2007 also from the Uiversity of Salamaca. Assistat Lecturer i the Departmet of Computer Scieces ad Automatio. Her research experiece focuses o the applicatio of multi-aget systems i differet cotexts, ad o the use of Digital Aimatio techiques. Ferado de la Prieta holds a MCS from the Uiversity of Salamaca (Spai) where he is curretly pursuig her PhD i Computer Sciece. Udergraduate degree i Computer Sciece from the Uiversity of Salamaca. Research focuses o multi-aget systems, virtual orgaizatios, Cloud Computig ad educatioal techology. He has published articles i prestigious atioal ad iteratioal cofereces, ad has participated i differet research proects both at a atioal ad Europea level. Jua F. de Paz Doctorate i Computer Sciece ad Automatio from the Uiversity of Salamaca (2010). Curretly Assistat Lecturer i the Departmet of Computer Sciece ad Automatio at the Uiversity of Salamaca. Udergraduate degree i Egieerig ad Computer Sciece (2005) ad Statistics (2007) from the Uiversity of Salamaca. His research work focuses o the coordiatio ad developmet of proects i the field of Bioiformatics ad Biomedicie, applyig reasoig techiques ad Artificial Itelligece algorithms.