Digital Libraries - Springer Link

Int J Digit Libr (1999) 2: 124–143

I N T E R N AT I O N A L J O U R N A L O N

Digital Libraries © Springer-Verlag 1999

An adaptive visual environment for digital libraries M.F. Costabile, F. Esposito, G. Semeraro, N. Fanizzi Dipartimento di Informatica, Universit` a di Bari,Via Orabona 4, 70125 Bari, Italy; E-mail: {costabile,esposito,semeraro,fanizzi}@di.uniba.it Received: 15 December 1997/Revised: June 1999

Abstract. CDL (Corporate Digital Library) is a prototypical intelligent digital library service that is currently being developed at the University of Bari, as an evolution of a previous project named IDL (Intelligent Digital Library). Among the characterizing features of CDL there are a retrieval engine and several facilities available for the library users. In this paper, we present the web-based visual environment we have developed with the aim of improving user-library interaction. The CDL environment is equipped with some novel visual tools that are primarily intended for inexperienced users, who represent most of the users that usually have access to digital libraries. Machine Learning techniques have been exploited in CDL for document analysis, classification, and understanding, as well as for building a user modeling module, which is the basic component for providing CDL with user interface adaptivity. This feature is also discussed in the paper. Key words: Visual environment – Topic visualization – Adaptive interface – User classification – Decision tree induction

1 Introduction and motivation Interconnected digital libraries are receiving increasing attention due to the rapid advance of computing power and networked connectivity. Conventional libraries usually support three main functions: 1) collection; 2) organization and representation; 3) access and retrieval [19]. Collection does not mean mere acquisition of information items from any information resource, but includes techniques that enable detecting those information sources that are useful to a client population. Organization and representation require that the information resources are classified and indexed according to criteria relevant to

their potential users. Access and retrieval involve the design and organization of materials within a physical space in order to effectively retrieve information items, when the user requires them. Digital libraries emphasize some aspects of the above functions, due to the wide variety of users who will access, extract, and display information from such systems, and also due to the nature of the stored information that is distributed on various sources which differ in type, form and content. Users need to easily understand what kind of objects in the available sources they have access to, how they can retrieve and organize them in ways that help them to make rapid decisions about what is relevant and which patterns exist among objects. Users also need to manipulate the retrieved information in order to incorporate it in their specific tasks. As a consequence, digital libraries must provide enhanced user interfaces that support this intensive interaction between users and information. In this context, conventional interfaces – based on the view of information retrieval as an isolated task in which the user formulates a query against a homogeneous collection to obtain matching documents – are completely out of date. Indeed, this view does not correspond to the reality of users working with both digital and physical libraries for several reasons. For example, users are often unable to formulate specific questions, and they find out what they are trying to ask and how to ask it by browsing the system. This process has been called progressive querying in [11] and iterative query refinement in [35]. Moreover, users often consult multiple sources with different contents, forms, and methods of access. In order to assist users in such new ways of interaction, we must provide much richer and more flexible environments, that capitalize not only on effective user interfaces, but also on the retrieval engines and intermediate services that are available in the digital library: the

M.F. Costabile et al.: An adaptive visual environment for digital libraries

former will support various operations that allow users to understand properties of entire collections or groups of documents, as well as of specific documents; the latter will provide some meta-services that can reveal the characteristics of both the stored information and the access mechanisms. The work presented in this paper is meant to be a contribution in this direction. Several authors agree that users interacting with huge amounts of unknown and varied information find some meta-information on the following different aspects of the stored data extremely useful [35]: 1) content, that is, what information is stored in the source; 2) provenance, which refers to how the information in the source is generated and maintained, whether it is a public source or a personal archive, how frequently it is maintained, etc.; 3) form, i.e., the schemes for the items in the source, including their attributes and the types of values for these attributes; 4) functionality, which concerns the capabilities of the access services, such as the kinds of search supported with their performance properties; 5) usage statistics, that is, statistics about source usage, including previous use by the same user or other ones. One of the goals of our work is to investigate effective ways for endowing the interaction environment of a digital library with appropriate representations of the above meta-information, particularly about content, in order to provide users with proper cues for locating the desired data. The various paradigms for representing content range from a textual description of what is stored in the information source to structured representations using some knowledge representation language. Our choice is to exploit visual techniques, whose main advantage is the capability of shifting the load from the user’s cognitive system to the perceptual system. Indeed, information may be visualized in an information space in order to be retrieved by users. This visualization can either be carried out by the users in their own mind, in which case it is essentially the users’ conceptualization of that information, or it could be accomplished by the system, in which case the visualization is generated on the display screen. The latter is actually called information visualization, and is defined as “a process of transforming information into a visual form enabling the user to observe information” [5]. Recent research has proved that a suitable visualization can reduce the time to get information, and to make sense out of it [6]. In the digital library context, visualizations have a wide range of applications, they can be used for visualizing various types of meta-information, as well as queries and documents. CDL (Corporate Digital Library) is a prototypical intelligent digital library service that is currently being developed at the University of Bari [16, 39]. Among the characterizing features of CDL there are a retrieval engine and several functionalities that are available to the library users. CDL is the evolution of a previous project named IDL (Intelligent Digital Library); it is called “intelligent” since it exploits artificial intelligence techniques, more

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specifically, machine learning techniques, for several activities such as document analysis, classification, and understanding, as well as for user classification and modeling. In this paper we describe the web-based visual environment we have developed for CDL, in order to improve user-library interaction. The main contributions of this paper are twofold. On one hand, it presents, in an integrated environment, some novel visual tools complementing the typical form-based user interface of digital libraries; these new tools address the needs of most users not familiar with the content and organization of the digital library, by allowing them to visualize some overviews of the stored data in order to be guided in the process of information retrieval. On the other hand, it describes how user modeling can be managed through machine learning techniques, thus providing the basic component for user interface adaptivity. The remainder of this paper is organized as follows. We give an overview of the main features of CDL in Sect. 2. Section 3 briefly describes the CDL architecture. Section 4 presents the visual interaction environment of CDL, while Sects. 5 and 6 illustrate the process of user modeling through machine learning techniques, also describing an experiment we have performed. Related work is reported in Sect. 7, while Sect. 8 concludes the paper and outlines some future work. 2 Overview of CDL According to Lesk, “a digital library is not merely a collection of electronic information” [25]. It is “a distributed technology environment that dramatically reduces barriers to the creation, dissemination, manipulation, storage, integration and reuse of information by individuals and groups” [24]. Based on this definition, we developed CDL as a prototypical digital library service, whose primary goal is to provide a common infrastructure that facilitates the process of creating, updating, searching, and managing corporate digital libraries. Here, the word corporate means that the different libraries are not necessarily perceived by the user as a single federated library, as in the Illinois Digital Library Initiative Project [38]. Nevertheless, all the libraries share common mechanisms for searching information, updating content, controlling user access, charging users, etc., independently of the meaning and the internal representation of information items in each digital library. Indeed, the CDL project focuses on the development of effective middleware services for digital libraries, and on their interoperability across heterogeneous hardware and software platforms [3]. The main features of CDL are strictly related to the library functions of i) collection, ii) organization, iii) access, as mentioned in Sect. 1: 1. support for information capture – supervised learning systems are used to overcome the problem of cheaply

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and effectively setting information items free of the physical medium on which they are stored [14, 16]. 2. support for semantic indexing – again, supervised learning systems are used to automatically perform the tasks of document classification and document understanding (reconstruction of the logical structure of a document), that are necessary steps to index information items according to their content [15, 16]. 3. support for content understanding and interface adaptivity – CDL provides users with an added value service, which helps novice users to understand the content and the organization of a digital library through a suitable visual environment, and supports skilled users (supposed to be familiar with the digital library) in making easy and fast retrievals of desired information items by means of an appropriate interface modality. As to interface adaptivity, this is achieved through automated user classification based on machine learning techniques, as will be described in Sect. 5.

3 The CDL architecture A digital library service is a set of modules that can be classified as either resource managers or application enablers. A resource manager is a program that represents the only access path to the data contained in a protected resource and is accessible to multiple, concurrent clients. Intuitively, a protected resource is a data collection. An application enabler is a software module that allows a class of users to make application programming easy and quick or to avoid it completely. CDL is a digital library service, whose architecture is shown in Fig. 1. It is the typical client/server architecture of a hypertextual service on the Internet. More specifically, the current version of CDL adopts a thin-client stateful architecture [29]. In such a model, the application runs with only one program resident on the personal com-

Fig. 1. Thin-client stateful architecture of CDL

puter of the user: a Web browser. Moreover, there is no need to store data locally, therefore no DBMS is present on the client-side of the architecture. This justifies the attribute thin-client given to this model. Data are simply grabbed from the library host, and then presented to the user through HTML screens. These screens are dynamically generated by means of Java applets, since their content must mirror the current content of the repository in the library host or simply they must be generated according to the user choices. Furthermore, the architecture is called stateful since it is characterized by the presence of a Learning Server, which performs several activities. The activity we describe in this paper is the modeling of users of the digital library service from data collected in log files during the interaction sessions, and managed by the CDL Application Server (see Fig. 1). A thorough description of how the CDL Learning Server works is given in Sect. 5. The reasons that led us to adopt a thin-client stateful architecture, rather than a fat-client model, are several: – the user can enter CDL from any PC connected to Internet through either a permanent or a dial-up connection; – the cost of using CDL for users without a permanent Internet connection is just a telephone call to the Internet Service Provider, rather than to the remote host of the library; – there is no software – to be downloaded, maintained, updated – on the PC of the user, with the exception of the client browser; – there is no need to download/upload data from/to the server of the library. In the design of the CDL architecture, we primarily focused on what we considered the key issue in the development of efficient digital library services, that is, an effective integration of specific technologies for information compression, storage, organization, retrieval, and navigation with user interfaces and multimedia technology. As a consequence, CDL architecture consists of a collection of modules/components, that can be organized in a multilayer structure, including: – an adaptive visual environment, for user-library interaction; – a set of task specific tools, such as a generator of document layout in HTML format, a document manager/ editor, a standard mailing system, an optical character recognition system, a search engine; – a multi-purpose Learning Server, that has the ability of adding value to data items coming from other components devoted to specific tasks, such as information capture, information classification/categorization/ cataloguing, semantic indexing of document components, user classification/modeling, interaction modeling, information filtering/summarization; – a document storage subsystem, that is, the document storage and access software involved in both storing


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and retrieving items to and from the library collection as well as in updating and searching the library catalogs. This component takes care of creating a library abstraction [19], that integrates multiple (and probably heterogeneous) information sources. Dedicated modules, called mediators or translators [4, 33], data model wrappers [43] or simply wrappers [22], are in charge of mapping user queries into the specific query languages of the underlying DBMSs [12]; a set of database management systems, which currently includes two commercial database management systems, with different data models. More specifically, we use both an object-oriented database management system (OODBMS), namely ObjectStore 2.0 by Object Design, Inc., running on a Sun SPARCstation 10 with SunOS 4.1.3, and a relational database management system (RDBMS), namely Microsoft Access, running on a Pentium 133 MHz, under Microsoft Windows95; a set of operating system functions, which allow the digital library Administrator to control the users’ access rights, to map names of the users who issued a request to the proper locations through a Name Server, and to limit each user to what the Administrator permits by means of an Authorization Server ; a set of networking functions and protocols, that allows remote users to visit CDL via Internet; a set of repositories, that contain the actual distributed collection of data, represented as highly structured items, in the system of corporate digital libraries.

Of course, the latter services are more related to the machinery used to implement the digital library service, while the first ones are closer to user needs. As depicted in Fig. 2, the current version of the CDL architecture evolved from a two-tier client/server model [16] to a three-tier client/server one, in which a Client-Interface Layer (CIL), a Daemon Layer (DL), and an Application-Server Layer (ASL) can be identified. The CIL corresponds to the tier that provides user services, the DL makes available the business services, while the ASL is responsible for the implementation of the data services. The first layer, the CIL, includes all the Web-based applications that can be used through a browser, provided that it implements the Java Virtual Machine 1.1.6. Users interact with the system through this layer, having several functions available. All these functions are supported by the intermediate layer of services (DL), which accepts user requests from the CIL and codes them into a proprietary communication protocol based on the HTTP standard. The DL collects all the requests coming from the CIL, decodes them according to the request type, and either supplies the corresponding services directly to the CIL or redirects these requests to the hosts of the ASL through

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Fig. 2. CDL multi-layer system architecture

routing techniques. In this last case, the DL takes care of getting results from each host and returning them to the CIL. DL is the layer that contains all the information on the physical location of the digital libraries and the document classes that constitute each of them. The services implemented directly in the DL are related to the management of the user’s personal records, of data concerning the custodians of specific libraries (librarians), and of the Corporate Digital Library “metaschema”. This layer also includes a Web Server, a Daemon managing the Learning Server and the corporate organization of the libraries, and a Router (see Fig. 2). The core of this layer takes care of controlling the automated activation of learning processes related to some of the librarian’s functions. Specifically, it sets up the learning problems (coming from actions such as class/document insertion, new index addition etc.), identifies the proper learning system among those available in the Learning Server, schedules the learning processes, and reports the results by activating the appropriate function of the lower layer. The Router manages the distribution of the libraries on differently located repositories; indeed, it is the only one that is aware of its actual topology. In fact, the system allows the user to interact with distributed databases on several Internet servers. Indeed, documents in a single digital library can be physically stored on various hosts, as well as a host being able to accommodate more than one digital library or parts of it. The database distribution and the techniques used to connect to the various servers are transparent to the users, who have a centralized vision of the databases containing the documents. The ASL groups all the hosts on which the documents of the digital libraries are physically distributed. Each

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host is endowed with an interface application to the DL. It translates each request into a corresponding query in the language of the database server, and produces results to be communicated to the higher layer (DL). There are many advantages in implementing a threetier architecture. Keeping the data access functions separate from the client interface is not only a safety rule, but also a great aid to maintenance, due to the low cohesion between the layers. The change of a database access technique involves only the ASL level. Specifically, it requires just the change of the driver that accesses the data. The benefits have an impact on all the clients that indirectly have access to the particular host of the ASL. Another advantage of this model is that it enables satisfying the service requests forwarded to the ASL in a concurrent way. Indeed, a query raised by a user is sent to all the servers containing the documents of the chosen digital library. CDL is programmed in several languages, ranging from C and C++ to Java, and it exploits the various services offered by the World Wide Web.

4 Visual interaction environment of CDL In this section, we present the interaction environment of CDL, to which remote users may have access on the

WWW. The homepage of the system is shown in Fig. 3, accessible at URL http://idl.di.uniba.it. We first illustrate a form-based interface that was developed with the first prototype. Then we describe the new visual tools, namely the topic map and the tree-based interface that have been incorporated more recently, in order to help a wide variety of users to search, browse, and select information from the library sources. 4.1 Form-based interface Since its first implementation [16, 39], CDL has been equipped with a Web-based user interface, so that it is accessible by means of any Web browser. The general activities that can be carried out through this interface are: 1) creation/deletion of a digital library; 2) management of specific digital libraries; 3) browsing/querying of a selected digital library. Such activities are performed by three different kinds of persons, who interact with CDL according to the different role they play in the system and, as a consequence, according to the different access rights they own. Thus, we can identify and define a hierarchy of roles, whose prerogatives range from the mere usage of the digital libraries (e.g., querying and/or retrieving by content the documents they are interested in), up to the global management of the whole corporate system, possibly performing structural modifications in it.

Fig. 3. CDL homepage


At the top level of this hierarchy, we find the CDL Administrator, who is unique and is at the head of the system in its entirety. The Administrator’s fundamental task consists in managing and supervising access to the various libraries involved in the system. The Administrator, in particular, is the only person who has the power of allowing a new library to join the service, or, conversely, of eliminating an already existing one (activity 1 above). Each digital library involved in the system has its own manager, which constitutes the second role in the hierarchy and is called the Library’s Custodian or Librarian. Each Librarian is responsible for his/her own digital library, and can have access to the system only after identifying him/herself through a proper (client) digital certificate. A thorough description of the management of secure transactions in CDL can be found in [40]. The certification process is necessary for the sake of security, since Librarians have the power of modifying both the content and the structure of the libraries they manage, i.e., to add, delete, or update not only documents belonging to any class in the library, but even the classes themselves, with all their search indexes (attributes) or the definition of the single search indexes. At the bottom end of the hierarchy we find the Generic or End User (user for short), who is any person entering the system through Internet with the aim of consulting its content. The user can query the library in a number of ways, in order to retrieve the documents he/she is interested in. Then, if it is the case, the user can see, in a digital format, any of the found documents. Of course, the user cannot change anything in the system, except for local copies of the documents. Each user will automatically get a personal ID when accessing the system for the first time, and which will identify such a user in all the future interaction sessions. A typical interaction session with CDL is as follows. After reaching the CDL’s homepage on the Internet, it is possible to choose the role on entering the library service by clicking on the button corresponding to their role (see Fig. 3). Both the Library Administrator and the Librarians, as we said before, must identify themselves through their own digital certificate and enter their own password before starting to work. Any other user will be asked by the system to provide his/her ID. If such a user is consulting the CDL for the first time, a registration procedure starts, showing a form in which the user must insert his/her private data in order to obtain the above mentioned ID. In the following, we describe an example of interaction for the Librarian, and one for a Generic User. After the preliminary steps of certification and password validation, a page is presented to the Librarian showing the name of the library he/she is responsible for, and containing the buttons for selecting the activities that can be performed. For example, the Librarian can insert a new class of documents. In this case, the names of all the classes already existing in the repository are displayed and the Librarian may insert the name and num-

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ber of search indexes of the class being created, as well as the index names. Another button refers to the addition of a new index to an already existing class of documents. A menu with all classes is presented, from which it is possible to choose the one the new index must be added to. Subsequently, all indexes related to that class are listed, followed by an area in which the name of the new index can be entered. The next button allows the Librarian to add one – or more – new documents to the library’s repository. Such documents can be acquired from a number of different sources, showed in a menu: from a text file containing data about the layout structure of the document, which can be stored in the local host as well as in a remote one, or from a list of such layout files, which can be either newly created or already existing. In any case, a new screen is displayed in which it is possible to enter the corresponding file name. Conversely, another button permits the Librarian to delete an existing document. Some of the other buttons allow the Librarian to perform several kinds of queries on the content of the digital library, by visualizing various summaries. The interaction of a Generic User is completely different. As shown in Fig. 4, the current CDL Web environment allows him/her to interact through three different interfaces, namely one map-based (topic map), one form-based, and the third one tree-based. The user will first choose the library he/she is interested in, and then select the preferred interface by clicking on the corresponding image at the bottom of the page. By choosing the form-based interface, the page in Fig. 5 is presented to the user. In this interface, in order to perform the query, the user must fill some fields in a form, hence the name form-based. This is the typical interaction modality for searching documents in digital libraries. As we can see, in the CDL the user may choose, through a pull-down menu, a document class to query, and the available indexes for that class will be visualized in the main area of the page. The default value is ALL, which means that all document classes will be considered. In Fig. 5, the class ICML in the library AI_in_DL has been chosen. For each index, a field is shown. To further specify a query, the user is required to insert the appropriate search values in at least one index field. In our example, the user has inserted the values “Machine Learning” OR “Decision Tree” in the index field Title. As a final remark, note that the page presents a button on the top right side, allowing the user to call Help, while two other buttons on the left side allow the user to shift to another interaction modality (buttons TOPIC MAP and TREE). The result of the query in Fig. 5 is shown in Fig. 6. Fifteen documents have been retrieved, and they are listed in a sequence that can also be ordered by selecting an appropriate item in the menu Order by at the bottom of the page. Data of the first document are shown in Fig. 6; they report the values for each search index. Data of the other documents can be visualized through the scroll bar on the

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Fig. 4. The three types of interface supported by CDL

Fig. 5. Example of query in the form-based interface

right. By clicking on the button VIEW, the corresponding document is displayed in an HTML page generated on the fly by the HTML generator module, as shown in Fig. 7. It is worth noting that all the HTML pages are dynamically generated, in order to reflect the current content of the digital library.

From the description above, it should be clear that, even if such a form-based interface is powerful and flexible in that it permits a search by a combination of fields, it is more appropriate for users who are already acquainted with the library structure, and who also have some information about the library content. By observing casual


Fig. 6. Query results

Fig. 7. Visualization of a retrieved document as an HTML page

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users interacting with our prototype, we realized that users often performed queries whose result was null, simply because they did not have any idea of the kind of documents stored in the library. Therefore, we decided to enrich the CDL interaction environment by developing some novel visual tools, aimed at allowing users to easily grasp the nature of the information stored in the available sources and the possible patterns among the objects, so that they can make rapid decisions about what they really need and how to get it.

4.2 The topic map One of the new features of the CDL environment, that users appreciate the most, is the possibility of getting a rapid overview of the content of the stored data through the topic map. Such a visualization is actually an interactive dynamic map (interactive map for short), as has been proposed in [44]. An interactive map gives a global view of either the semantic content of a set of documents or the set of documents themselves. The semantic content reflects the topics contained within the set of documents and the way they are organized to relate to each other; it is represented by a thesaurus that is built automatically from a full-text analysis. Interactive maps exploit the metaphor of exploring a geographic territory. A collection of topics, as well as a collection of documents, is considered to be a geographical territory that contains resources, which metaphorically represent either topics or documents; maps of these territories can be drawn, where regions, cities, and roads are used to convey the structure of the set of documents: a region represents a set of topics (documents), and the size of the region reflects the number of topics (documents) in that region. Similarly, the distance between two cities reflects the similarity relationship between them: if two cities are close to each other, then the topics (documents) are strongly related (for example, documents have related contents). Topic maps are very effective since they provide an overview of the topics identified in a collection of documents, their importance, and similarities and correlations among them. The regions of the map are the classes of the thesaurus, each class containing a set of topics represented by cities on the map. Roads between cities represent relationships between topics. In this way, topic maps provide, at a glance, the semantic information about a large number of documents. Moreover, they allow users to perform some queries by direct manipulation of the visual representation. Document maps represent collections of documents generated from a user query that may be issued on the topic map by selecting regions, cities, and roads. The cities of these maps are documents, and they are laid out such that similar or highly correlated documents are placed close to each other.

In order to generate the topic map in CDL, we need to identify the set of topics or descriptors defining the semantic content of the stored documents; such topics constitute the CDL thesaurus. There are several thesauri used in the information retrieval literature; most of them are built manually and their descriptors are selected depending on specific goals. An example is Roget’s thesaurus, which contains general descriptors. When building the CDL thesaurus, we have used standard techniques, also taking into account the type of documents currently stored in CDL. These are scientific papers that have been published in the journal IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), in the Proceedings of the International Symposium on Methodologies for Intelligent Systems (ISMIS), and in the Proceedings of the International Conference on Machine Learning (ICML). Therefore, we have used the INSPEC thesaurus containing specific terms in the field of Artificial Intelligence. This thesaurus contains 629 keywords, that are either single words or expressions made up of more words (up to five). We have represented documents and keywords (topics) by vectors, which is a common practice in information retrieval [23, 36]. The coordinates of the document vectors and those of the topic vectors are computed in the following way: the coordinate di of the vector representing document D is 1 if the topic Ti was found in D, and 0 otherwise; the coordinate ti of the vector representing topic T is 1 if document Di contains T, and 0 otherwise. A number of correlations can be easily computed from these vectors, and then visualized in the topic or document map. In particular, for the topic map, we are interested in the number of documents to which a topic is assigned (the so-called term frequency), and also in the correlation between pairs of topics, which is the number of documents to which both topics of the pair are assigned. Moreover, clustering techniques are applied to the descriptors of the thesaurus in order to generate classes of similar descriptors, which will be visualized close together in a region of the topic map. Like in [36, 44], the similarity between two topics is computed by the following formula: Sim(Ti , Tk ) = NTi Tk /(NTi + NTk − NTi Tk ) where: NTi Tk is the number of documents to which both topics Ti and Tk are assigned, NTi is the number of documents to which topic Ti is assigned, NTk is the number of documents to which topic Tk is assigned. The thesaurus is then partitioned in a set of classes A1 , A2 , . . . , Ap , where each Ai contains descriptors that are similar, and p is a user-settable parameter. In the current CDL prototype, p has been set to 5, and the partition has been computed very simply. As the centroid of each one of the five classes we have chosen the five topics in


our thesaurus with maximum term frequency; they are: learning, classification, framework, noise, and training. For any other topic in the thesaurus, by using the above formula we have computed its similarity with the centroid of each class, and we have assigned the topic to the class with the highest similarity. Then, we have added a sixth class, that is, the special class “miscellaneous”, gathering topics dissimilar to every other topic. It is worth making two remarks about the generation of the CDL thesaurus and its class partitioning. The first is that, if the documents stored in CDL were of a general nature, we could have used other well-known techniques for building the thesaurus as well as for computing the relevance of documents to topics. In our current research, the main interest is in effectively representing the topics and the related documents once a classification has been somehow performed, rather than in identifying new document classification techniques, for which we rely on already ascertained research. The other remark is that the generation of the thesaurus is computationally expensive, but it needs to be performed only once from scratch. If the number of documents is large, it is unlikely that a new document will change the classes of the thesaurus. Therefore, adding a new document to the initial collection will only involve the re-computation of the correlations. The classes will be re-computed only after adding a large number of documents.

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4.3 Interacting with the topic map As already mentioned, our topic map design borrows some ideas from the interactive maps proposed in [44]. However, in the CDL environment we have used some color-based coding techniques and added several widgets to this initial design, that make the overall visualization more effective and provide adequate mechanisms for flexible interaction in a data intensive context, such as that of an online digital library. In the CDL topic map, cities represent topics of the thesaurus, and a region represents a set of topics, i.e., topics in a class Ai . The distance between two cities reflects the similarity relationship between them: if two cities are close to each other, then the topics are strongly related. As a novel feature, we have adopted a color-based technique to code the importance of a topic, that depends on the number of documents that topic has been assigned to. Therefore, the rectangle used to represent a city will be drawn in an appropriate color. Figure 8 shows the topic map for the current version of CDL. As we can see, there are six regions on the map, in which topics are concentrated around the region centroid. Topics are visualized in ten colors, ranging from green to red, where green is used to represent the less important topics, and red the most important ones. The color scale, reproduced in the widget shown at the top left of Fig. 8, has two important functions: 1) it shows the col-

Fig. 8. Topic map giving the overview of the topics in a library of CDL

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ors used and the progression from the least to the most important one; 2) it is a very useful interaction mechanism that gives users the possibility of quickly filtering the information on the map. Indeed, the user can filter out the less important topics by simply clicking on the small rectangle below the colored bar. This kind of filter is very useful, especially when the map is very cluttered up, as often happens in digital libraries that, by definition, contain huge amounts of information. Figure 9 is similar to Fig. 8 but, by clicking on the Topics widget, less important topics of the three colors on the left (and all their links) have been eliminated; the resulting map is much less cluttered. A similar color-based technique has been adopted for coding a relation between pairs of topics through a link connecting two topics on the map. In our design, the link between two topics has a different color, depending on the importance of the link, which is the number of documents assigned to both linked topics. The colors are those used for the topic importance, and a similar widget, visible in Fig. 8, is used to act as a filter on the map. By clicking in the rectangle below each bar, links of that color will be eliminated from the map, thus allowing users to concentrate on specific links. The topic map is visualized in a small window on a screen; hence, in a general overview many topics are hidden. A zoom mechanism is essential for allowing users proper browsing. We designed a widget which is capable

of giving feedback about what portion of the map is currently zoomed with reference to the whole map, in a way that is similar to a moving lens. Such a widget is a rectangle located next to the Links widget in the area above the map. In the situation in Fig. 8, the whole map is shown, so that the border of the red rectangle, that indicates the portion of the map currently visualized, overlaps the border of the rectangle representing the whole map. By clicking one of the five small buttons below this rectangle, it is possible to resize the area of the graph that will be visualized. If we look at Fig. 10, we see that the red rectangle is much smaller than the rectangle representing the whole map. Indeed, the topic map now visualizes a zoomed portion of the map. As indicated by the red rectangle in the widget, the visualized area is part of the top region of the map. The topics of such region are now far more visible and the red rectangle provides a useful feedback of where we are in the context of the whole map. The user can browse the zoomed map and visualize the area of interest by either acting on the scroll bars at the bottom and at the right of the window showing the topic map or dragging the red rectangle in the Zoom widget. Figure 10 also illustrates the query facility that has been implemented in the topic map. We allow users to perform some queries by direct manipulation of the map. In the area above the topic map, we see six buttons next to the Zoom widget. Such buttons, together with the pull-

Fig. 9. Same as in Fig. 8, but less important topics (and related links) have been eliminated by acting on the Topics widget


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Fig. 10. A zoomed view of the map, with an example of the composition of a query

down menu next to them, are used for performing a query. Comparing this area with the same area in Fig. 8, we see that in Fig. 8 only the button Compose Query is enabled. By clicking on this button, the user can compose a query. Indeed, all the other buttons are now enabled, and the user can compose the query by simply clicking on a topic in the map to select it, and on the buttons AND, OR, (, ), if they are needed. The pull-down menu to the right of the buttons is used for choosing the document class to be queried. The default item is ALL, standing for all classes. The classes are listed in a menu dynamically generated, since they can be modified by the Librarian. During its composition, the query is shown in a proper area, as we can see in Fig. 10, where the string “INDUCTIVE LEARNING” is visible between the widget area and the map. Such a string is also editable. In this example, the user is interested in retrieving all documents containing that keyword. The user can now submit the query by clicking on the button Submit Query. The results will be visualized in the same way as in Fig. 6. By observing users interacting with our prototype, we realized that often they were unable to find a topic of interest in the map. This may be due to different reasons, such as the map is too cluttered or the topic is partially overlapped by another topic. We then decided to add another feature to our tool: the user may click on the button search and type the value, or at least some characters, of the topic of interest in a form that will appear. The system then lists all topics whose initial characters match the inserted ones. The user can select one, and this topic will be shown in dark blue in the map to better localize it.

4.4 Tree-based interface The tree-based interface provides another visual modality to both browse the CDL and perform queries. The main advantage of the interaction through this modality is that the user gets familiar with the organization of the different libraries in CDL, i.e., classes and indexes for each class are shown progressively in a tree structure. The user navigates into CDL along such a tree structure, starting from the root and expanding the tree step by step, so that at each node of the tree the user can make a decision whether to further explore that path or not. Initially, the system shows the root tree, namely the root node CDL. By selecting each node, a pop-up menu appears with two items: the first item explodes the selected node, the second item provides an explanation of the meaning of the node, in order to orient the user in his/her choice. When expanding the root node CDL, its offspring will show the digital libraries available in CDL (see Fig. 11). Node CDL in Fig. 11 is not expansible anymore; the expansible nodes are shown in a blue rectangle with the label inside. If the user now selects another node among those that are still expansible, the pop-up menu appears again and the user can explode the selected node. Figure 11 shows a situation in which the user has expanded the node AI_in_DL. In this way, he/she has implicitly selected such a library, and the classes of documents have been displayed as further level of the tree. Then, the user has selected the class ICML and expanded this node, so that all available indexes on this class of documents are now displayed, while the other classes are not visualized anymore to allow the user to concentrate on the selected one. The user may now perform a query by entering appropriate values for one or more of the displayed indexes.

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Fig. 11. Query submission with the tree-based interface: the user has inserted a search value for the index Title

Fig. 12. Example of query with a tree-based interface

The search values are input through a pop-up window, such as the one shown in Fig. 11, which appears once the user clicks on a specific index node. In this example, the user has clicked on node Title and has inserted “learning” OR “tree” as search value in the window. Figure 12 shows the situation obtained by clicking on the button Close of the pop-up window in Fig. 11. The query can now be submitted by clicking on the button Submit at the bottom-right corner of the window.

Several options are available for improving the usability of this interface. As an example, the query can be visualized in different ways by clicking on some buttons at the bottom of the window. For example, by clicking on the button View NaturalQuery, a window will appear in which the composed query will be visualized in natural language. For example, such a view for the query in Fig. 12 will be: RETRIEVE (ANY AI_in_DL Document WHICH has_class ICML AND has_title “learning OR tree”).


Such a query visualization is a useful feedback for the user. The three different interfaces described above, namely the form-based, the topic map, and the tree-based make up the CDL interaction environment. Thanks to the CDL adaptivity feature, which will be discussed in the next section, the system proposes to the users the interface that it considers the more appropriate for them. Of course, users have the freedom to shift anytime to another interface, at will. 5 Interface adaptivity through CDL Learning Server When developing a system exploited by several users, a fundamental problem to cope with is to make it adaptive to the various kinds of users that can be recognized, with the aim of improving the overall usability of the system [30]. A prototype of an intelligent component, working in a client/server way and able to automatically classify a user, is currently embedded in the Application Server of CDL. In the overall architecture of CDL, such a prototype is part of the Learning Server. CDL Learning Server can be defined as a suite of learning systems that can be exploited concurrently by clients for performing several tasks, such as document analysis/classification/understanding, and inference of user models. In this paper, we focus on this last task, understood as inferring the user models by means of supervised learning methods. Indeed, each user of a system possesses special capabilities, skills, knowledge, preferences, and goals. This holds for most software systems. In the case of a service meant to be publicly available on the World Wide Web, like CDL, this is certainly true. The reasons why users consult CDL may range from real bibliographic search needs to checking the orthography of a word. Each user has his/her own profile thus, when using the system, he/she will behave differently from any other user. It is desirable that an intelligent system is able to understand which kind of remote user it is interacting with and tries to help him/her by making the accomplishment of his/her own goals easier (through contextual help, different interaction modalities, personalized items, etc). As a consequence, one of the main problems concerns the definition of classes of users meaningful for the system, and the identification of a language that properly describes each class and the interactions of users in that class. Among all the possible CDL generic users, we defined three classes of users, namely Novice, Expert and Teacher. They are characterized by a different (growing) degree of familiarity with CDL and, more generally, with the domain of libraries and with online access to document collections. Once a user has been classified, the next problem to cope with is how to track out potential changes of the class the user belongs to, in case his/her behavior

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changes. This last problem is a critical one, since it is plausible to foresee a transitional user, understood as a user becoming more and more skilled as he/she becomes familiar with the system. To cope with this problem, the system assigns each CDL user with an identity code, given to the user the first time he/she enters the system, and monitors the user interaction, storing the values of meaningful features in the user log file. From the log file, the system generates a user model, represented as a feature vector. The user model is later updated by the system on the grounds of the log files computed at the successive interaction sessions of that user. As for the problem of choosing a language appropriate to describe user interactions, we have studied those characteristics that could be useful for recognizing the type of user, and that could be easily computed from the data stored in the log file associated with each user interaction session. Most of the identified characteristics are application dependent, that is, they are related to the digital library domain, while others are system dependent, i.e., strictly related to CDL. For instance, relevant characteristics are those concerning the way users exploit the capabilities of CDL search engine, such as date and time of session beginning, class of documents chosen, search indexes chosen, criterion for sorting the search results, number of documents obtained as results of the search, types of errors arising during the interaction with CDL. The formal description of these characteristics is stored in the Attribute Description File. For inferring user models, the main function of the Learning Server is to automatically assign each CDL user to one of the predefined classes, on the grounds of information drawn from real interaction sessions with CDL. This activity is known as interaction modeling [2]. The classification performed by the learning server can be exploited in several ways. In the CDL, it is used to associate each class of users with an interface that is suitable to the user’s degree of familiarity with the system. The purpose is to speed up the process of understanding the organization and the content of the chosen digital library, and to properly assist the user during all the steps necessary to retrieve the desired information. As pointed out in recent work in the literature [28], interaction modeling may take advantage of machine learning methods and techniques. In our approach, interaction modeling is cast as a supervised learning problem by considering some user interactions with CDL as training examples for a learning system, whose goal is to induce a theory for classifying the users who will interact with CDL. Two phases characterize the classification process. In a preliminary phase of system training, data stored in the log files of some users are exploited to train the learning system. The training phase will induce a decision tree and a set of rules that makes up the theory used by the system in order to classify the users who will interact with CDL. As shown in Fig. 13, from the training set

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Fig. 13. Training phase of the CDL Learning Server

and the Attribute Description File, the learning system C4.5/C4.5RULES [34] generates the rules to classify the users. In the successive phase of system use, whenever a user connects to CDL through a client, the log file generated during the interaction session of that user is exploited to generate a new example that the learning server classifies on the ground of the inferred theory. The way in which rules are consulted by the learning server (and the existence of the classification rules itself) is completely transparent to the user of CDL. Figure 14 illustrates the CDL Learning Server that, when a user connects to CDL, consults the available rules and compares them to the set of characteristics extracted from the log file in order to assign the user to a specific class.

Fig. 14. User classification phase of the CDL Learning Server

C4.5/C4.5RULES, which originally works in an interactive way, has been customized in order to work in a batch way to infer the classification theory from the set of log files corresponding to the users whose interactions have been selected to train the system. Furthermore, we are currently investigating the possibility of using incremental learning systems [15] that avoid the drawback of starting the learning process from scratch each time new log examples become available to train the system.

6 The classification experiment In this section, we describe a classification experiment that demonstrates the validity of our approach to user modeling as a supervised learning problem. The experiment concerning the classification of CDL users consisted

in collecting 500 examples of log files associated with interaction sessions with the form-based interface, and generating from them a training set for the system component C4.5. As previously mentioned, we identified three fundamental classes of users, namely Novice, Expert, and Teacher. Each log file is used to draw the values taken by the attributes exploited to describe user interactions, whose description is available in the file DF.names (Attribute Description File in Fig. 15). The 126 attributes exploited to describe the interaction of a user, identified by a unique User ID, concern: – the total number of user connections to CDL; – the average number of user daily connections to CDL; – for each available digital library DL, the number and the frequency of the queries on DL; – for each class of documents C in DL, the number and the frequency of the queries on C; – for each search index I valid for the class C in DL, the number and the frequency of the queries performed using I; – for each search index I valid for the class C in DL, the number and the frequency of times index I has been used as sorting criterion of the query results. A portion of the file DF.names is depicted in Fig. 15. The file specifies the classes, namely Novice, Expert, Teacher, then the name and description of each attribute. As we can see from Fig. 15, each listed attribute has a numeric value and is described as continuous. Each training example is made up of a set of 126 values, corresponding to the attributes in the file DF.names, and is labeled with one of the three classes Novice, Expert, Teacher. All the 500 training examples used in our experiment are stored in the file DF.data (Training Set in Fig. 13) given in input to C4.5. A portion of this file is shown in Fig. 16. Only three of the 500 examples are shown. Each example consists of the values taken by the 126 attributes, separated by a comma, and followed by the example’s class. Figure 17 shows the output of C4.5, which contains the induced decision tree. The number in parentheses indicates the number of training examples associated with each leaf. Following the tree, there is an evaluation of the performance of the tree on the training set of 500 examples used for inducing it. Since C4.5 also contains heuristic techniques for simplifying decision trees, this evaluation is reported both for the original tree – Before Pruning – and the simplified one – After Pruning. In our case, pruning has no effect on the tree, which is made up of 7 nodes – 4 leaves and 3 internal nodes. The tree makes no misclassification on the 500 training examples and C4.5 predicts that it will have an error rate of 1.1% on unseen examples. Figure 18 shows the corresponding set of rules generated by the system component C4.5RULES. It examines the decision tree produced by C4.5 and generates a set of production rules of the form L -> R, where the left-


Fig. 15. Portion of the file DF.names, given in input to C4.5

Fig. 16. A portion of the file DF.data, given in input to C4.5

Fig. 17. The decision tree induced by C4.5

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Fig. 18. The rules introduced by C4.5RULES

hand side L is a conjunction of Boolean tests, based on the attributes in the file DF.names, while the right-hand side R denotes a class. One of the classes is also designated as default, in order to classify those examples for which no rule’s left-hand side is satisfied. Rule generation is done with the aim of improving the comprehensibility of the induced results. Indeed, in our experiment, C4.5RULES produced only 4 rules from the 7-node decision tree, which seem to be more readable than the original tree-structured classification model. The numbers in brackets, following the right-hand side of each rule, indicate the percentage of unseen examples that will be correctly classified by that rule. The designated default class is Teacher. The current CDL interaction environment integrates three different types of interfaces, namely topic map, treebased interface, and form-based interface. With this new environment, on the grounds of the performed user classification, the Learning Server may select a distinct type of visual user interface, regarded as the proper one for each class of users. Specifically, the Learning Server prompts any user recognized as a member of the class Novice with the topic map interface, while the tree-based interface is proposed to a user in the class Expert, and Teacher users have the form-based interface as their default. Moreover, the system has stored in the user model some other parameters characterizing that specific user, such as the preferred library, the most frequently queried document class, etc. These parameters are exploited to further customize the interface to the user. For example, if the user model asserts that the user is of type teacher, frequently consulting the library “Architecture”, the interface that will be presented to the user entering the system for a new interaction session will be the form-based one, having already selected that library. The main idea underlying the mapping between user classes and types of interfaces is that users being unfamiliar with the system need an environment that allows them to preliminarily understand the content of a (probably unknown) digital library, just like a fellow who goes into a library for the first time. Conversely, skilled users are supposed to already know both the organization and the type of content in the digital library, thus they want

a powerful tool that allows them to speed up the search and retrieval process of the specific data they are looking for. Furthermore, the choice performed by the Learning Server is not constraining for the user, who is allowed to shift to another interface whenever he/she wants. Based on the work described in this paper, the system will be able to follow up the user’s evolution since a single user is classified whenever he/she enters CDL. Thus, after a certain number of interactions, it might happen that CDL proposes a different interface to the same user.

7 Related work Research on digital libraries has recently received a great impulse, mainly due to the breakthrough of the Internet and the World Wide Web. The recent Special Issue on Digital Libraries in [17] points out that thousands of digital libraries, either local or national, are emerging around the world. They extend and augment their physical counterparts in many ways, and primarily they provide new levels of access to broader audiences of users. As a consequence, the main digital library research projects share the key issue of providing users with efficient tools to “search and display desired selections from and across large collections” [37], as well as “locate and access desired information” [1]. This issue was already described in the National Science Foundation Announcement about Research on Digital Libraries [31], that gave rise to the Digital Library Initiative, sponsored by NSF, ARPA and NASA (whose URL is http://www.grainger.uiuc.edu/dli/national.html). The CDL visual interaction environment has been influenced by these guidelines and also by recent research on information visualization as a way of improving the intensive interaction between users and information [5, 6, 8, 13, 18]. In [35], a variety of studies, tools, and systems developed at Xerox PARC illustrates the style of rich interaction that users will have with digital libraries. The work proposed in this paper is a contribution in that direction.


The proposed approaches of performing queries by visual means capitalize on some previous work on visual query systems [7, 10]. In [9], a framework for multiparadigmatic visual access to databases, exploiting formbased, diagram-based, and icon-based paradigms, has been proposed. This interface also presents some adaptivity features, and proposes to the users the most appropriate visual interaction paradigm on the basis of a stored user model, even if the users always have the possibility of shifting to a different paradigm. However, user classification and modeling do not exploit machine learning techniques, that appear very appropriate for this task, as has been shown in this paper. The advantage of visual information representations is also advocated in [21], where the authors propose using previews and overviews to allow users to quickly locate objects of interest when interacting with large collections of data. The user can simply enter the values of some parameters he/she is most interested in, and the system generates an overview of the collection of objects matching those parameters in order to give an idea of both type and size of that collection. A preview provides quick, even if not detailed, representation of a single object of interest, so that the user can easily decide on the relevance of that object for his/her purposes. Previews are analogous to bibliographic records and overviews are analogous to catalogs. The visualizations proposed in CDL by the topic map and the tree-based interface are types of overviews. Indeed, the topic map and the tree-based interface have been designed with the aim of assisting those users who are unable to formulate specific questions, and who can decide what they really want and how to retrieve it only by browsing the system. The process of formulating a query progressively, i.e. step by step, by first asking general questions, obtaining preliminary results, and then revisiting such outcomes to further direct the query in order to extract the result the user is interested in, has been called progressive querying in [11]. The idea is similar to the iterative query refinement in [35]. This is an interesting feature which a query system should have, and it has been exploited by the visual query tools implemented in CDL. The interactive dynamic maps proposed in [44] were the main source of inspiration for the topic map, as we have described in Sect. 4.2. However, in the CDL environment we have improved the visualization by exploiting some color-based coding techniques and added several widgets with the aim of providing interaction mechanisms suitable for a data intensive context, such as the one of online digital libraries. Furthermore, in our system the topic map allows users to perform queries by direct manipulation, a facility that was not very well developed in [44]. The interaction through the topic map is in accordance with Shneiderman’s Visual Information Seeking Mantra “Overview first, zoom and filter, then details

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on demand” [41]. We have implemented mechanisms for zooming and filtering working on the overview provided by the topic map. Once some documents have been retrieved, we will have detailed information on each document, up to a complete view of the whole document. In many systems, search results usually provide long lists of documents, most of which are unrelated to the user’s interest. Therefore, tools that can analyze search results and visually manipulate them are needed. The work in [27] is a contribution in this sense. Search results are clustered and properly visualized to provide relevance information. We are also working to incorporate in CDL a document map that visualizes the retrieved documents with colored icons, where the color indicates the relevance of the retrieved documents concerning the query, in a way similar to that done for the document topics. As to the problem of presenting the results of the queries performed, several manipulation tools have been proposed. A study concerning the evaluation of such visual environments proposes a metric for measuring the amount of structure of the organization [26]. Moreover, it gives methods for modifying the organization in order to achieve more quality (e.g., increasing the dimensionality) exploiting information coming from the users. Interface adaptivity, in the context of digital libraries, is receiving increasing attention in the literature. In our work, we have devised a fixed number of possible user classes (Novice, Expert and Teacher). A methodology for organizing users in order to group them on the ground of common interests (“communities”) could be a further improvement [32]. This could help in filtering information. However, building meaningful communities is a critical issue. Another possible approach to classifying users for the purpose of making the system adaptive to user needs relies on formally measuring their characteristics by means of a battery of tests, and then exploiting a statistical mapping of such factors on the adjustable characteristics of the (adaptive) system [20]. In such a case, the user is aware of being monitored, while in the case of log files he/she is not. Once user profiles are obtained, document classification systems – e.g., [27] – should be exploited for ranking the retrieved documents according to real interests. A survey, specific to digital libraries, can be found in [42] concerning which characteristics and techniques are particularly important for system adaptivity with respect to user needs.

8 Conclusions and future work Online digital libraries pose several challenges due to the large number and variety of their users, and also to the nature of the stored data that is distributed on autonomous information sources that differ in content, form, and type. One of the consequences is that digital li-

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braries must be equipped with environments that permit a new style of rich interactions with such informationdense systems. The work presented in this paper is a contribution in this direction. We are aware that usability is an extremely important requirement for applications having a large variety of users, such us online digital libraries. We have already performed some usability evaluations, by observing a restricted sample of users interacting with the CDL prototypes developed so far and recording the main difficulties they had. The new visual tools incorporated in the CDL environment were developed in order to cope with some problems we noticed novice users had. Such users were mainly students of our undergraduate courses. We are currently conducting more accurate usability tests with a larger sample of users, with a different background and expertise. As to user modeling, we are designing a framework that allows us to add information about the user’s topics of interests to the log files. Such additional information items will allow the CDL learning server to infer user profiles that will be profitably exploited both by personalized components for information filtering and by modules based on push technology. Concerning the learning server, we are completing its development in order to integrate inductive reasoning capabilities in first-order logic. Acknowledgements. The support provided by the MURST ex 40%, INTERDATA Project, is acknowledged. We would like to thank also M. Santoro and A. Carbutti for their contribution in designing and developing the visual environment of CDL.

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