Adaptive and Personalized Semantic Web

In this work a twofold algorithmic framework for the adaptation of web content to the users’ choices is presented. The main merits discussed are a) an optimal offline site adaptation – reorganization approach, which is based on a set of different popularity metrics and, additionally, b) an online personalization mechanism to emerge the most “hot” (popular and recent) site subgraphs in a recommendation list adaptive to the users” individual preferences.

Data on the Web is noisy, huge, and dynamic. This poses enormous challenges to most data mining techniques that try to extract patterns from this data. While scalable data mining methods are expected to cope with the size challenge, coping with evolving trends in noisy data in a continuous fashion, and without any unnecessary stoppages and reconfigurations is still an open challenge. This dynamic and single pass setting can be cast within the framework of mining evolving data streams. Furthermore, the heterogeneity of the Web has required Web-based applications to more effectively integrate a variety of types of data across multiple channels and from different sources such as content, structure, and more recently, semantics. Most existing Web mining and personalization methods are limited to working at the level described to be the lowest and most primitive level, namely discovering models of the user profiles from the input data stream. However, in order to improve understanding of the real intention and dynamics of Web clickstreams, we need to extend reasoning and discovery beyond the usual data stream level. We propose a new multi-level framework for Web usage mining and personalization, consisting of knowledge discovery at different granularities: (i) session/user clicks, profiles, (ii) profile life events and profile communities, and (iii) sequential patterns and predicted shifts in the user profiles. One of the most promising features of the proposed framework address the challenging dynamic scenarios, including (i) defining and detecting events in the life of a synopsis profile, such as Birth, Death and Atavism, and (ii) identifying Node Communities that can later be used to track the temporal evolution of Web profile activity events and dynamic trends within communities, such as Expansion, Shrinking, and Drift.

The paper focuses on evaluating and refactoring the conceptual schemas of Web applications. The authors introduce the notion of model clones, as partial conceptual schemas that are repeated within a broader application model and the notion of model smells, as certain blocks in the Web applications model, that imply the possibility of refactoring. A methodology is illustrated for detecting and evaluating the existence of potential model clones, in order to identify problems in an application’s conceptual schema by means of efficiency, consistency, usability and overall quality. The methodology can be deployed either in the process of designing an application or in the process of re-engineering it. Evaluation is performed according to a number of inspection steps. At first level the compositions used in the hypertext design are evaluated, followed by a second level evaluation concerning the manipulation and presentation of data to the user. Next, a number of metrics is defined to automate the detection and categorization of candidate model clones in order to facilitate potential model refactoring. Finally, the paper proposes a number of straightforward refactoring rules based on the categorization and discusses the aspects affecting the automation of the refactoring procedure.

Web based systems are extremely popular to both end users and developers thanks to their ease of use and cost effectiveness respectively. Two of the most popular applications of web based systems nowadays are e-learning and e-commerce. Despite their differences, both types of applications are facing similar challenges: they rely on a “pull” model of information flow, they are hypermedia based, they use similar techniques for adaptation and they benefit from semantic technologies [3]. The underlying business models also share the same basic principle: users access digital resources from a distance without the physical presence of a teacher or a seller. The above mentioned similarities suggest that, at least, some user behavioral patterns are similar to both applications.

In order to support knowledge workers during their tasks of searching, locating and manipulating information, a system that provides information suitable for a particular user’s needs, and that is also able to facilitate the sharing and reuse information is essential. This paper presents Adaptive Personal Information Environment (a-PIE); a service-oriented framework using Open Hypermedia and Semantic Web technologies to provide an adaptive web-based system. a-PIE models the information structures (data and links), context and behaviour as Fundamental Open Hypermedia Model (FOHM) structures which are manipulated by using the Auld Linky contextual link service. a-PIE provides an information environment that enables users to search an information space based on ontologically defined domain concepts. The users can add and manipulate (delete, comment, etc) interesting data or parts of information structures into their information space, leaving the original published data or information structures unchanged. a-PIE facilitates the shareability and reusability of knowledge according to users’ requirements.

The wealth and diversity of information available in the internet or local hyper-media corpora has increased while our ability to search and retrieve relevant information is being reduced. Searching in distributed hypermedia has received increased attention by the scientific community in the past few years where integration solutions which rely on semantics are considered as efficient. In this paper we propose a new scheme for integrated searching in distributed hypermedia sources using a multi-layer ontology. Searching tasks are carried out in the metadata level, where information concerning hypermedia objects is published, managed and stored in the form of a scalable description of knowledge domains.

The htmlButler project aims at enhancing the usability of visual wrapper technology while preserving versatility. htmlButler will allow, for an untrained user who has only the most basic web knowledge, to visually specify simple but useful wrappers and, for a more tech-savvy user, to visually or otherwise specify more complex wrappers. htmlButler was started 2005/2 and is based on visual wrapping technology research carried out in the Lixto project since 2000. What is new in htmlButler is that (a) the application is entirely server based, the user accessing it through his or her standard browser, (b) because of the centralized wrapper configuration and processing, the knowledge about popular wrappers can be leveraged to facilitate the specification of wrappers for new users, and (c) users can contribute narrow and precise ontologies that help the system in recognizing potential meaning in web pages, thereby alleviating the complexity of future wrapper configurations

One of the most prominent features that the Semantic Web promises to enable is the discovery of new and implied knowledge from existing information that is scattered over the Internet. However, adding reasoning capabilities to the existing Web infrastructure is by no means a trivial task. Current methods and / or implementations do not seem to be declarative and expressive enough to provide the kind of reasoning support that the Semantic Web users will benefit from. In this paper we propose a methodology based on which, the user can construct and pose intelligent queries to Semantic Web documents in an intuitive manner, without prior knowledge of the document’s contents or structure. This methodology is implemented through a knowledge discovery prototype interface that relies on an existing inference engine found suitable for this task.