Using a Semantic Multidimensional Approach to Create a ContextualRecommender System

Abdulbaki UzunService-centric Networking

Deutsche Telekom Laboratories, TU Berlinabdulbaki.uzun@telekom.de

Christian RackCompetence Center FAME

Fraunhofer Institute FOKUSchristian.raeck@fokus.fraunhofer.de

AbstractItem recommendations calculated by recom-mender systems mostly in use today, only relyon item content description, user feedback andprofile information. In modern mobile services,however, contextual information and semanticknowledge can play a significant role concern-ing the quality of these recommendations. There-fore, the SMART Recommendations Engine ofFraunhofer FOKUS is extended by the SMARTMultidimensionality Extension and the SMARTOntology Extension that enable the recommenderto incorporate contextual and semantic data intothe recommendation process. The demonstra-tion of the SMART Ontology Extension visual-izes that the preciseness of recommendations canbe increased by exploiting implicit and indirectknowledge, classification and location informa-tion gained from ontologies when generating rec-ommendations in the scope of an exemplary foodpurchase scenario.

1 IntroductionToday, people are confronted with a large amount of infor-mation in the World Wide Web [Shenk, 1998]. Receivinga small subset of desired and filtered content through stan-dard search engines turns out to be very difficult. And itbecomes quite impossible, if user specific needs and inter-ests should be taken into consideration.

Recommender systems handle this issue by filtering rel-evant information and providing personalized content rec-ommendations to users based on their profile and feedback.Numerous recommendation methods were designed overthe years to improve the accuracy of recommendations.The most popular ones are content-based and collaborativefiltering algorithms or hybrid approaches comprising boththem [Adomavicius and Tuzhilin, 2005].

The results delivered by hybrid methods are often ac-ceptable. However, they only depend on content descrip-tions and ratings given to items, and user profile informa-tion. In a time when mobile and location-based servicesbecome very popular, context information and semanticknowledge can play a decisive role in order to significantlyimprove the preciseness of personalized recommendations.If John, for example, is vegetarian, eats only organic food,tries to live economical and goes shopping nearby, it doesnot make sense to recommend him groceries in stores faraway or only in discounters without taking his preferencefor vegetarian and organic food into consideration. This

example shows that context as well as semantic informa-tion (e.g. implicit knowledge about which food products fitto certain eating preferences) are important to satisfyinglyanswer a user’s grocery recommendation request.

In order to harness the potential of contextual and se-mantic information, the generic recommender system ofFraunhofer FOKUS, the SMART Recommendations En-gine [Raeck and Steinert, 2010], has been extended bytwo new recommender extensions. Inspired by the workof [Adomavicius et al., 2005], the SMART Multidimen-sionality Extension enhances the two-dimensional matrixrepresentation of recommender data by a multidimensionalrecommendation model allowing the integration of addi-tional contextual information when generating recommen-dations. The SMART Ontology Extension, on the otherhand, enables the recommender to incorporate contextualand semantic information gained from ontologies (e.g. im-plicit and semantic knowledge about a user and his pref-erences, location, time or ontological classification infor-mation). For this purpose, the extension provides a tool toexploit semantic data stored in ontologies and perform con-text and semantic filtering on the recommender databaseusing several filters. Both extensions can be used indepen-dently from each other or together depending on the givenscenario and application.

This paper is organized as follows: First, an overviewabout related work in the field of context-aware andsemantic recommender systems is presented. Afterwards,a background about the SMART Recommendations En-gine is given. Following that, concepts for the SMARTMultidimensionality Extension are described. Section 5explains the SMART Ontology Extension including theautomated mapping of ontological information into therecommender’s data model. In section 6, the functionalityof the SMART Ontology Extension is demonstrated withinthe scope of a food purchase scenario.

2 Related WorkContextual and semantic information is incorporated in thefield of recommendations in various ways. A context-aware collaborative filtering system is presented by [Chen,2005], which generates item recommendations for a userbased on different context situations. In order to achievethat, the traditional collaborative filtering is extended, sothat feedback of like-minded users in a similar context canbe used to recommend items to the active user in his currentcontext.

[Adomavicius et al., 2005] propose a multidimensionalrecommendation model, in which additional contextual di-

mensions can be added to the traditional User x Item matrixrepresentation. Recommendations are calculated by usingthe reduction-based approach, which reduces the problemof multidimensional recommendations to a traditional two-dimensional matrix in the required context, so that widelyknown traditional recommendation techniques can be ap-plied after the reduction is done.

Another methodology is suggested by [Farsani and Ne-matbakhsh, 2006], which recommends semantic productsto customers in the context of eCommerce based on prod-uct and customer classification via OWL.

[Kim and Kwon, 2007], on the other hand, developed anontology model with a multiple-level concept hierarchy fora grocery store scenario with four different ontologies: aproduct, location, consumer and record ontology. From theproduct ontology, most relevant products are taken usinguser information modeled in the consumer and record on-tology. Recommended products are presented in a concepthierarchy ranging from most specific to most broad. Whenusers select some of these concepts, the context of the re-quest is subsequently refined enhancing the specificity ofthe provided results.

Another interesting approach is demonstrated by [Yu etal., 2006]. They present a context-aware media recommen-dation platform called CoMeR, which uses an OWL ontol-ogy context description, a N x M-dimensional model anda hybrid processing approach to support media recommen-dations for smart phones.

In another paper by [Setten et al., 2004], the integrationof a context-aware recommender system into the mobiletourist application COMPASS is described, where users gettouristic information and services recommended based ontheir interests and contexts.

COReS, which stands for context-aware, ontology-basedrecommender system for service recommendation, is an-other example. It was developed by [Costa et al., 2007] andthis recommender system extends the capabilities of the IN-FRAWARE [Pereira Filho et al., 2006] service platform bysupporting service selection and user needs satisfaction fora certain context.

Previous research activities are either focused on theintegration of context or semantic information. However,incorporating both – context information and semanticdomain knowledge – would increase the preciseness ofrecommendations decisively. The food scenario shows thatthe integration of both types of data is necessary to satisfy-ingly answer a recommendation request. For example, inorder to generate accurate grocery recommendations, thesystem has to be aware of the location of the user (context)and has to be capable of using implicit knowledge in orderto relate the user’s eating preferences to the right groceries(semantic information). That’s why, Fraunhofer’s enginewas extended using both types of data.

3 SMART Recommendations EngineThe SMART Recommendations Engine is a generic recom-mender system that enables internet businesses, rich mediaand entertainment services or SMEs deliver a more person-alized experience by providing recommendations in theirrespective application domain. By offering a flexible, gen-eral purpose algorithmic model, the engine makes it possi-ble to formulate application specific recommendation al-gorithms. These algorithms and the application specificdata model are declared at configuration time by assem-

bling selected components. By adding custom componentsthrough the provided API, the capabilities of the recom-mender can be extended in order to meet specific applica-tion demands. These custom components can be built fromfunctional groups, such as basic mathematical operations,similarity and relevance computations, sorting and filter-ing, and data access.

In the system, data is represented in an entity-relationship-like data model. An entity type that includes aset of entities is named domain, whereas relations betweendomain entities are represented by matrices (see Figure 1).A user domain, for example, can contain all users, whilean item domain can comprise all items in a specific appli-cation. Ratings given by a user to a certain item can berepresented by a rating matrix, whose rows and columnsare associated to the these domains.

Figure 1: Recommender Data Model

Recommendation algorithms calculate relevance valuesfor each User x Item pair. Differing on the given applica-tion, the recommendation algorithm is assembled at run-time configuration by defining a network of matrix trans-formation components (e.g. a similarity computation com-ponent). The matrix operations are applied on a data set ina hierarchical manner leading to the estimated utility func-tion at the top node of the tree. Figure 2 shows such ageneric algorithm hierarchy, where the nodes represent ma-trix operations and data is propagated along the edges inform of matrices.

Figure 2: Generic Algorithm Hierarchy

The engine also provides a custom query language calledSugar Query Language (SuQL), which is used to requestrecommendations and related data at runtime. Via SuQLvarious recommendation algorithms can be selected andcombined. Constraints on the item set to be recommendedcan also be specified, so that only items that fulfill theseconstraints are included in the final recommendation. Forthis purpose, the recommender already offers a variety ofsorting and selection filters, which can be used to alter theresult set by certain properties.

One example for a filter is the Proximity Filter, which incombination with a Geo-Location typed domain, an Item xLocation matrix, a given center position and a maximumrange, is capable of selecting items (e.g. shops) in a givengeographic region. By using the lookup capabilties ofthe SMART Ontology Extension (see section 5), items

can be filtered based on location constraints, like findingall grocery products sold in shops located within a givendistance from the user’s current location.

4 SMART Multidimensionality Extension

Contextual information can be exploited in the recom-mendation process by enhancing the level of dimensionsfrom the traditional two-dimensional paradigm to multipledimensions. Therefore the SMART RecommendationsEngine is upgraded by the SMART MultidimensionalityExtension, which is able to handle multidimensionalityand hence provide more precise recommendations.

4.1 Enhanced Data Model

This extension enhances the generalized data model of theSMART Recommendations Engine for multidimensionalitypurposes by binding more than two domains to a rating ma-trix. Figure 3 shows the enhanced data model in an entity-relationship notation.

Figure 3: Enhanced Data Model

However, the recommender is designed to work two-dimensional; a matrix can only consist of two domains (onerow domain and one column domain). That’s why, the en-hanced data model has to be adapted to the standard datamodel of the recommender. By merging several domainsto one domain and hence mapping multiple dimensionsto a two-dimensional matrix, the two-dimensional recom-mender data model can be kept with the advantage thatmultidimensionality features can be utilized at the sametime (see Figure 4).

Figure 4: Merged Domains in the Enhanced Data Model

The two-dimensional matrix representation of multipledimensions can be done through serialization by use ofa hierarchical index. Each slice of the multidimensionalcube is stored into one two-dimensional matrix makingit possible to access the desired element with the help ofan index (e.g. Slice1.User#1). Figure 5 shows a generalMD-2D-Mapping for the enhanced data model.

Figure 5: General MD-Mapping to 2D

4.2 Multidimensional RecommendationAlgorithms

Various standard recommendation algorithms, such as useror item-based collaborative filtering, can be extended bythe extension to multidimensional algorithms.

The standard user-based collaborative filtering algo-rithm applied by the SMART Recommendations Engine, forexample, determines similar users by using ratings givenfor items by them. Based on this similarity and on theratings given to items, predictions for new items are cal-culated. This algorithm can be upgraded to a multidi-mensional user-based collaborative filtering algorithm bymerging several domains to one column domain.

Assume that there is a rating matrix R[U :: C][I] in theUser :: Context x Item space, in which User :: Contextdisplays users with a certain context.

R[U :: C][I] = User :: Context× Item (1)

The similarity between users in the same context is deter-mined based on their ratings for items by applying a simi-larity transformation on R[U :: C][I]. This calculation alsoidentifies similarities between users in different context sit-uations, which can be useful for later recommendations.

Sim(R[U :: C][I], R[U :: C][I]) =

User :: Context× User :: Context(2)

Having a similarity matrix Sim[U :: C][U :: C] and therating matrix R[U :: C][I], a matrix product transformationcan be applied to calculate item predictions for users in acertain context.

PredictionP [U :: C][I] =

Sim[U :: C][U :: C] ∗R[U :: C][I] =

User :: Context× Item

(3)

These predictions can be further processed and sortedto generate new recommendations for users in certain con-texts.

To better illustrate the new multidimensional user-basedcollaborative filtering algorithm, an example query “Johnwants to buy food for a soccer evening” is used. This queryincludes three dimensions: the user (John), food and theevent dimension (here: soccer evening). For that matter,the similarity between users, who bought food for a soccerevening, is calculated using the User :: Event x Food ratingmatrix twice for the similarity computation (see Figure 6).

By applying a matrix product transformation on thecalculated similarity matrix User :: Event x User :: Eventand the rating matrix, food predictions for John in thecontext of a soccer evening can be identified. Thesepredictions can further be filtered and sorted based onJohn’s eating habits, for example, and the rest result can

Figure 6: Exemplary Multidimensional User-based Collab-orative Filtering Algorithm – Part 1

be presented as generated recommendations for John (seeFigure 7).

Figure 7: Exemplary Multidimensional User-based Collab-orative Filtering Algorithm – Part 2

5 SMART Ontology ExtensionData that provides additional and useful information to thetraditional User x Item representation, such as taxonomies,implicit and indirect knowledge about a user’s preferencesor location information can enhance the quality of recom-mendations.

Semantic web ontologies offer complex knowledge rep-resentation possibilites with which such semantic informa-tion can be modeled (the Web Ontology Language (OWL)[Dean and Schreiber, 2004]). Implicit knowledge, i.e.knowledge that is not directly included in an ontology, canbe inferred using reasoning technologies.

The SMART Ontology Extension provides features withwhich the SMART Recommendations Engine is capable ofexploiting semantic information from ontologies includinginformation gained from reasoning mechanisms. In thatway, implicit and indirect knowledge as well as taxonomiesbecome available when generating recommendations.

The extension is divided into two main parts: The firstpart is the Ontology Mapping, where all relevant data avail-able in pre-designed ontologies is mapped onto the data

model of the recommender. The second part performs se-mantic filterings on the previously extracted data set usingthe Ontology Filter in order to generate semantic recom-mendations.

The functionality of the extension is explained usingpre-designed ontologies for the food scenario. It comprisesusers with special eating habits (such as vegetarian, veganor organic), who want to buy food from grocery storesnearby based on their eating preferences.

5.1 Ontology MappingThe main constructs included in OWL ontologies are in-dividuals, classes, a class hierarchy, object properties,datatype properties and restrictions. These constructs canbe mapped onto the data model of the recommender, so thatthe recommendation engine becomes capable of handlingontology information.

Each class hierarchy in OWL is represented by a domainwith the name of its respective root class (e.g. Food, Useror GroceryStore). Individuals are items of a certain classand are mapped as items of their respective class hierarchyrecommender domain. Furthermore, all classes in an ontol-ogy become elements of the general Class domain. Whilea Food x Class matrix, for example, shows to which classesa food product belongs to, taxonomies become clearly rec-ognizable in a Class x Class matrix. Figure 8 shows anexemplary ontology class structure mapping for the foodscenario.

Figure 8: Root Class Representation

Object properties in OWL form a relation between in-dividuals, whereas datatype properties build a relation be-tween individuals and data types, such as Integer, String orBoolean. Matrices display relationships between domainsand are therefore equal to object and datatype properties.The domain and range an OWL property has are both repre-sented as domains in the recommender and their values areelements of these domains. If a property’s domain or rangeis defined as an ontology class, the recommender domainname corresponds to the class’s name as well. Otherwise,it corresponds to the property’s name.

In the example of Figure 9, the property eatingHabit hasa domain named User and data range values, such as Ve-gan, Vegetarian or Halal. Therefore the recommender do-mains are User and EatingHabit (name of the OWL prop-erty) defining a matrix. The values in the matrix are also di-rectly mapped from the ontology and represent the assignedproperty values to individuals (e.g. John eatingHabit Vege-tarian).

Based on the Ontology Mapping naming conven-tions, the Ontology Mapping tool automatically createsa recommender-compliant database including domains,

Figure 9: Datatype Property Representation

matrices and filters. Knowledge gained by reasoningmechanisms is also exported via the tool making it possi-ble to exploit implicit knowledge in the recommendationprocess. As seen in the screenshot of the tool (Figure 10),the left part of the GUI represents the Ontology Importand Ontology Export functions, whereas the right partcomprises all functions concerning the recommender.

Figure 10: Ontology Mapping Tool

5.2 Ontology FilterAfter successfully exporting ontology data into the recom-mender’s database, the Ontology Filter can process the on-tology information in the engine by performing a semanticfiltering. This new filter extends the available filter list ofthe SMART Recommendations Engine and makes it possi-ble to query implicit and indirect ontology knowledge atruntime.

Two different matrix operations are provided by the On-tology Filter, the Concept Lookup and the Matrix Lookup.In order to accomplish their task, both lookups use stan-dard set operations, which are also features of the OntologyFilter. The Concept Lookup uses the Union and Intersec-tion set operations, while the Existential quantification andUniversal quantification are utilized by the Matrix Lookupoperation. If needed, the result set delivered by the Ontol-ogy Filter can be inversed by the Not set operation. Thiscan be helpful in order to determine all products that canbe eaten by John instead of all that are forbidden for him,for example.

Ontology concepts can be looked up in the recommenderby using the Concept Lookup, which needs at least two dif-ferent matrices for the operation. Hereby, the column do-main of the first matrix has to be the row domain of thesecond matrix. Applied on the first matrix, the ConceptLookup filters certain column elements for one single row

element based on given constraints. These constraints canbe generated by any available SMART RecommendationsEngine filter, such as the Existing or Feature filters. Thefiltered column elements – now selected rows in the rowdomain of the second matrix – build the basis for furtheroperations. All selected rows will be processed again indi-vidually depending on given filters. The calculated resultsets of each selected row will be returned and will then beeither unified or intersected based on the selected set oper-ation (Union or Intersection). Figure 11 shows a ConceptLookup example, in which ingredients are looked up thatare not allowed to be eaten by John.

Figure 11: Concept Lookup Example

The Matrix Lookup filters information in a matrix basedon a given column domain result set of another matrix.Therefore, it also requires the use of two different matrices,whereas the column domain of the first matrix remains thecolumn domain of the second matrix. Rows of the secondmatrix will be filtered based on the given column domainresult set and a predefined set operation (existential quan-tification or universal quantification). The result is one setof filtered row elements. In Figure 12, an exemplary Ma-trix Lookup can be seen. All food products are looked upthat are forbidden for organic eating people.

Figure 12: Matrix Lookup Example

An overview of all supported operations of the OntologyFilter is given in Table 1.

Complex recommendation queries require combiningboth lookups to single a Concept and Matrix Lookupoperation, which is exemplary demonstrated in section 6.

6 DemonstrationPowerful contextual/semantic recommendations can becomputed by the recommender using the SMART OntologyExtension in combination with the Proximity Filter. Theontology data present in the database of the SMART Recom-mendations Engine facilitates the recommendation query

Filter Matrix Ops. Set Ops.

Ontology FilterConcept Lookup Union

Intersection

Matrix Lookup Existentialquantification

Universalquantification

Not

Table 1: Ontology Filter Operations

requests of a user by reducing his effort to manually de-scribing his personal needs and interests in a detailed way.In conjunction with the Proximity Filter, the recommenda-tions are also filtered by their location leading to a greatuser experience when using a mobile shopping service, forexample. This function is going to be demonstrated in thissection by taking the following mobile grocery shoppingservice scenario as a basis:

Assume that John is vegetarian and eats only organicfood products. He wants to buy groceries nearby his lo-cation and that’s why he wants his shopping application togive him recommendations based on the food categories heprefers. After the selection of food categories for his shop-ping cart, John gets food products recommended for eachcategory fitting his eating preferences and sorted by theirrelevance based on John’s previous purchases. The grocerystores that sell these products and are in a certain range toJohn are also listed.

Eating preferences, such as eating vegetarian or organic,include some sort of implicit and indirect knowledge. Forexample, in order to be able to specify vegetarian prefer-ences, the system must know what kind of food vegetar-ians do not eat. It further has to have the information,which ingredients are included in each grocery, so that non-vegetarian food can be filtered based on the list of non-consumable ingredients. Since semantic ontologies are pre-destined to model this kind of dependencies, three ontolo-gies were designed including a complete data set to maprelevant information given in the food scenario.

The food-ingredient-shop ontology consists of a classi-fication of food categories, concrete grocery products in-cluding their ingredients and shops, in which they can befound. The eatinghabit-ingredient ontology, on the otherhand, models relations between certain eating preferencesand the ingredients that are either allowed or forbiddento eat. User profiles are stored in the user-profile ontol-ogy. All data modeled in these ontologies is exported intothe recommender database via the Ontology Mapping tool.Figure 13 shows parts of the created data model after a suc-cessful export.

Using the SuQL, the SMART Recommendations En-gine can now provide contextual/semantic recommen-dations based on the available data set and the im-plemented recommendation algorithms. For the foodscenario, assume that John wants to buy snacks andbread in the range of 2 kms from his location andthat he already purchased the brown bread productNaturkind SonnenblumenVollkornbrot Geschnitten. TheSuQL query is constructed as follows: At first several se-mantic filterings are performed using the lookup operationsseveral times in order to identify all snack and bread prod-

Figure 13: Food Scenario Ontology Data Model

ucts that fit John’s eating preferences and his location. Af-terwards, these elements are sorted by their relevance andlimited to a certain number depending on the relevance pre-dictions calculated by the recommendation algorithms.

Figure 14: Concept and Matrix Lookup

Figure 14 shows a semantic filtering example for John, inwhich the Ontology Filter first performs a Concept Lookupin the User x EatingHabit matrix that looks up his eatingpreferences. In the second matrix (EatingHabit x Ingre-dient), John’s eating preferences (vegetarian and organic)are mapped to the ingredients. (Note: Due to the factthat organic food products cannot be identified by theiringredients, artificial ingredients, such as Ing Organic orIng NotOrganic were created and related to the food prod-ucts.) The Matrix Lookup then looks up all groceries in theFood x Ingredient matrix for vegetarians and organic eatingpeople individually. Finally, both result sets are unified toone single result set and inversed by the Not set operationin order to get a set of groceries, which can be eaten byJohn. These groceries are also filtered by their categories,so that only snacks and bread products remain.

Another semantic operation in form of a Matrix Lookupis utilized, so that all grocery stores near to John’s location

that sell these food products can be looked up. This is doneby using the Proximity Filter, where the location of John isspecified as well as the range to look for. All grocery storesnear to John’s position are delivered to the Matrix Lookup,which then performs a lookup to find all filtered snacks andbread products that are available in these shops.

In a final step, the recommendation algorithm is used inthe recommendation process. Even though the engine canalso generate recommendations based on different collab-orative filtering algorithms, this paper focuses on an ex-tended version of the content-based filtering approach us-ing the ontology taxonomy as content meta-data. This ap-proach – named as the ontology-based filtering algorithm –calculates relevance predictions using the similarity of foodproducts based on their cagetory as content meta-data (e.g.brown bread is more similar to white bread than to snacks)and the implicit user feedback given by users automaticallywhen purchasing items. This user feedback affects the finalrecommendations in that way that groceries of a categorythe user has already bought products before become morerelevant to him than products of other nodes in the tree.

The ontology taxonomy stored in the Food x Class ma-trix is used to compute a similarity between groceries bymeans of their categories leading to a food similarity ma-trix with the dimensions Food x Food. User x Food rele-vance predictions based on the taxonomy information canbe gained by applying a matrix product transformation onthe User x Food feedback matrix and the Food x Food sim-ilarity matrix (see Figure 15).

Figure 15: Ontology-based Filtering Algorithm

Figure 16 shows the SuQL recommenderresponse to John’s query. John boughtNaturkind SonnenblumenVollkornbrot Geschnitten before,which is from the bread product category BrownBread.This and all other BrownBread products are most relevantto him. The relevance value decreases the more vegetarianand organic bread products are in other nodes of the Breadtree. There is only one snack fitting his eating preferenceswith a low relevance value since John did not purchaseany snacks yet. As a result, five bread products and onesnack are presented in combination with the grocery storesnearby that sell these products.

7 Discussion of the ApproachOne way to compare the presented approach withwidespread memory-based and model-based recommenda-tion algorithms is to compare the accuracy of these ap-proaches based on a common metric such as the mean av-

Figure 16: Ontology-based SuQL Recommender Response

erage error (MAE). For that, a suitable set of data pointsfor training and testing is needed. The chosen applicationdomain of the presented demonstration scenario was pre-determined by the project’s context in which our approachwas developed. This context did not provide us with a suit-able data set for this kind of practical testing. However,a closer examination of our approach shows that a bench-mark recommendation algorithm, which has been adaptedto make use of the product hierarchy that is stored in theontology, provides better relevance values by taking the re-lation of different food items into account. The relevancevalues of unrelated items decrease in less relevant or un-suitable nodes of the product hierarchy, so that the overallrecommendation quality in terms of the achieved accuracyincreases.

8 Conclusion

This paper introduced the generic recommender sys-tem, the SMART Recommendations Engine of FraunhoferFOKUS, which is capable of providing recommendationsfor different types of applications. In order to make theengine meet the recommendation quality requirements ofmodern mobile services, the recommender was extendedby two new extensions. The SMART MultidimensionalityExtension provides multidimensionality capabilities to theso far two-dimensional recommender system. This upgradeenables the recommender to deal with additional contextdimensions in order to generate more accurate recommen-dations taking user’s current contextual situation into ac-count. The SMART Ontology Extension, on the hand, ex-ploits semantic ontology data by exporting all relevant in-formation given in pre-designed application-dependent on-tologies into the data structure of the SMART Recommen-dations Engine enabling the recommender to use semanticknowledge or ontology taxonomy information for recom-mendation purposes.

The demonstration showed that in conjunction with theProximity Filter, the SMART Ontology Extension providesan added-value to the engine by generating semantic andcontextual recommendations.

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