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  • Modeling Users and Context in Digital Libraries:Interoperability Issues

    Ana Nika1, Tiziana Catarci2, Akrivi Katifori1, Georgia Koutrika3,Natalia Manola1, Andreas Nurnberger4, and Manfred Thaller5

    1 University of Athens, Athens, Greece{a.nika, vivi, natalia}

    2 Sapienza University of Rome, Rome,

    3 Stanford University, California,

    4 Otto-von-Guericke University Magdeburg, Magdeburg,

    5 University of Cologne, Cologne,

    Abstract. The modeling of user characteristics is an important mech-anism for the support and enhancement of personalization in DigitalLibraries (DLs). Contextual information is sometimes considered in theliterature as part of the user model, although it in fact influences user-DLinteraction. However, context modeling and user modeling are stronglyinterrelated. Similar methods are employed for the representation of usercontext and user model and the use of ontologies constitutes a promisingapproach for such a representation because they facilitate the sharingof information and reinforce interoperability. In this paper, we definethe user model and user context for Digital Libraries, we examine iden-tified context dimensions as well as context-independent and context-dependent user model attributes, and we propose a Shareable User andContext Model Ontology (SUCMO) for user representation in DLs thatmay be used to augment personalization and achieve user interoperabil-ity. Furthermore, we present our implemented interoperability processthat includes ontology mapping, generation of the shareable user pro-file, information exchange, and reconciliation rules and we conclude ourpaper by discussing future research directions.

    Keywords: user context dimensions, user model attributes, ontology,user interoperability

    1 Introduction

    Digital Libraries (DLs) are heterogeneous systems that provide different func-tionalities. So far, there is no accepted definition of what a Digital Library is.According to the Digital Library Reference Model [6] a DL is a potentially vir-tual organization that comprehensively collects, manages and preserves for the

  • long depth of time rich digital content, and offers to its targeted user commu-nities specialized functionality on that content, of defined quality and accordingto comprehensive codified policies. This general definition affects also the userattributes that need to be captured by the DL in order to offer a personalizedexperience to different users. Until now, there is no generally accepted user modelfor DLs and the Reference Model does not specify what user characteristics aresignificant to be reflected in such a user model. Furthermore, contextual infor-mation is of particular importance for Digital Libraries because it influences userinteraction with the DL.

    In the last years, much research has been devoted to the understanding,definition, and modeling of the notion of context. Context has been examinedfor general purpose systems as well as specific systems such as mobile and sensornetworks where context changes rapidly. As far as the authors are aware, therehas been no comprehensive study of the notion of user context in the DL field.

    In this paper, our aim is to propose definitions for the user model and theuser context for DLs as well as to provide a clear distinction between user modelattributes and user context dimensions. Context-aware DLs will support person-alized access to users by providing the right information and services. It is evidentthat by identifying and obtaining relevant context, DLs may adapt themselvesto better suit users needs. Nevertheless, user context modeling is conceptuallydifferent from modeling of user attributes. Some context dimensions can hardlybe considered information about the user, but, in the literature, they are oftencaptured and included in user models. Similarly, user attributes are frequentlyregarded as part of different context models. However, context modeling anduser modeling are strongly interrelated. Not only similar methods are used forthe representation of user context and user model, but also context dimensionsinfluence specific user attributes such as preferences and interests that will benamed context-dependent attributes in following sections. For this reason, weinvestigate and propose the use of a Shareable User and Context Model On-tology (SUCMO) for the representation of the user model and the contextualinformation that affect users behavior because ontologies facilitate the sharingof information and enhance interoperability.

    Nowadays, users interact with different Digital Libraries and their informa-tion is scattered throughout them. This raises the need for personalization notto be limited to one system but to be applied across DLs. Cross-Digital Librarypersonalization means sharing and combining user information across differentDL systems so that a DL system may take advantage of data from others. Thisuser information, however, is not easily transferable from one system to another.To achieve cross-Digital Library personalization, DL systems should take intoaccount the precise nature of user profiles and proceed with a new theory forhandling user model and context interoperability. For this reason, we propose theuse of our ontology for the support of user model and context interoperabilityacross DLs and we present our implemented interoperability process.

    The remainder of this paper is structured as follows. Section 2 provides re-lated work on the definition and modeling of context, a review of the current user

  • model representation approaches as well as an investigation of user model andcontext interoperability approaches. Section 3 introduces our definitions for usermodel and user context and the identified context dimensions as well as context-independent and context-dependent user model attributes. Section 4 proposesthe use of a Shareable User and Context Model Ontology for the user repre-sentation in DLs and discusses the advantages of such an approach. Section 5presents our interoperability process for achieving user model and context inter-operability with the use of the proposed ontology. Finally, Section 6 concludesthe paper and introduces future research directions.

    2 Related work

    2.1 Context Dimensions and Modeling

    In recent years, researchers have attempted to better understand context by cre-ating different context definitions. Schilit et al. [24] were among the first thatfocused their attention on the definition of context. They described it as location,identities of nearby people, objects, and changes to those objects. Benerecettiet al. [1] introduced two dimensions of context: physical context and culturalcontext. Physical context is a set of environmental characteristics while cul-tural context includes user information, user preferences, background beliefs,etc. Lieberman and Selker [17] defined context as the state of the user, of thephysical environment, and of the computational environment as well as the his-tory of user-computer-environment interaction. Lucas [18] distinguished threedimensions of contextual information: physical context, device context, and in-formation context. Device context includes characteristics of the device itselfwhile information context is related to the knowledge about information objectswhose existence and identity are distinct from those of the devices that processthem. Brown et al. [4] defined context as location, identities of the people aroundthe user, the time of day, season, temperature, etc. Ryan et al. [23] included intheir work four user context dimensions: users location, environment, identity,and time. Nowadays, Deys and Abowds definition of context [10] is consideredas the most accepted one. They defined context as follows: Context is any in-formation that can be used to characterize the situation of an entity. An entityis a person, place, or object that is considered relevant to the interaction betweena user and an application, including the user and applications themselves.

    Apart from the aforementioned attempts to define context, much research hasbeen devoted to the area of context modeling. A well designed context modelconstitutes a critical step for the development of context-aware systems. Severalapproaches have been proposed for the representation of context. Strang andLinnhoff-Popien [25] performed a comparative study of different context model-ing approaches. The authors identified six categories for context representation:a) Key-value models, b) Markup scheme models, c) Graphical models, d) Objectoriented models, e) Logic-based models, and f) Ontology-based models. They ar-rived at the conclusion that the most promising context modeling method can

  • be found in the ontology category [26] because it provides a good sharing ofinformation with common semantics.

    2.2 User Model Attributes and Representation Approaches

    User models and the attributes captured in these models have been studied ex-tensively. The number of features represented in a user model depends to a largeextent on the kind of adaptation that the respective system wishes to provide.Rich [22] proposed a three-dimensional space of user models: 1) canonical vs.collection of individual user models, 2) explicit vs. implicit user models, and3) long-term vs. short-term user models. Brusilovsky and Millan [5] collectedthe five most popular and useful user characteristics: the users knowledge, in-terests, goals, background, and individual traits. Preferences [15] and interests[28] are considered as very important attributes for most systems that incorpo-rate user profiles. Tazari et al. [27] defined the user model as a set of parame-ters such as personal information, general characteristics, education, occupation,interaction-related info, and user state. General characteristics consist of phys-ical factors such as weight and height, physical disabilities, and abilities likereading, speaking and writing. Personal information includes the users name,birthday, address, bank account, and credit card information. The state of theuser is described by a set of parameters like current activity, current terminal,location, and orientation.

    There are several approaches related to the structure and representation ofinformation in user models. Overlay models [31] are used for modeling usersknowledge as a subset of the domain model. The overlay model stores an esti-mation of the users knowledge level for each part of the domain. For the repre-sentation of users interests a common model is the weighed vector of keywords[19]. Weights are related to keywords and express numerical description of usersinterests. A user model may be represented by a weighted semantic network[12] in which each node expresses a concept. The use of ontologies constitutes apromising approach for the representation of user models because they facilitatethe sharing of information and enhance interoperability. Significant proposedontologies produced for user and context modeling are the General User ModelOntology [14], the Unified User Context Model [20], and the Ontology basedUser Model [21]. Finally, Golemati et al. [13] created a user profile ontology thatincludes mainly static information without modeling dynamic characteristics likethe current user position.

    2.3 Interoperability of User and Context Models

    Interoperability of user and context models is a subject that has been studiedextensively during the last decade. Numerous approaches exist that attempt toprovide effective solutions to this problem [8]. Recent advances in user modelinteroperability reveal three basic approaches: a shared-format approach, a con-version approach, and an intermediate approach.

  • The shared-format approach enforces the use of a shared syntax and seman-tics to represent user models. More specifically, the user modeling communityfocuses on ontology based approaches as the basis for the shared-format ap-proach in order to achieve user model interoperability. Typical representativesof this category are the General User Model Ontology [14] and the Unified UserContext Model [20]. Heckmann et al. proposed the General User Model Ontol-ogy (GUMO) in order to manage the syntactic and semantic variations betweenuser modeling systems. The General User Model Ontology is based on OWLand is used for modeling user attributes and their interrelationships. Niedereeet al. introduced a Unified User Context Model (UUCM) that can be used formodeling attributes of the user and his environment, i.e., the user context.

    It is apparent that by adopting a shared format approach by systems, thereare no syntactic or semantic heterogeneity issues to be solved. All the systems usethe shared model that is easily exchangeable and interpretable. Nevertheless, thesystems that exist nowadays are very heterogeneous and dynamic. This makes itimpractical, and in some cases even impossible, to use a shared user model andto enforce systems to adhere to a shared vocabulary. The conversion approach,as does not use a shared representation for user models, employs appropriatemethods to transform the syntax and semantics of the user model used in onesystem into those of another system. An example of this approach is given inBerkovsky et al. [2] where the authors introduced a generic framework for usermodel mediation. This work also focuses on resolving the heterogeneity of theavailable user model information, as pays particular attention on the resolutionof inconsistencies and conflicts among the information obtained from varioussystems.

    Finally, an intermediate approach integrates the advantages of both ap-proaches to enable adaptability in describing user models and to offer a map-ping of user information from one system to another. Concrete approaches thatfall in this category are the Generic User model Component [29], [30] and theFramework for User Model Interoperability [7]. Van der Sluijs et al. introducedthe Generic User model Component (GUC) that applies Semantic Web tech-nologies to retain user models and to share user profiles between applications.Carmagnola proposed a Framework for User Model Interoperability that uses ashareable user model preserved by every system in order to participate in theinteroperability procedure.

    3 Identified User Model Attributes and User ContextDimensions

    After having examined the various definitions of user model and user context,we propose in this section our definitions for these two important notions of theDigital Library field.

    Definition 1: A user model for Digital Libraries is a collection of the most im-portant user attributes, either context-independent or context-dependent, that

  • are captured in order for DLs to behave differently to different users. Context-independent are the attributes that do not change if the context is different.Context-dependent attributes are those that are affected by context variations.



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