User and document group approach of clustering in tagging systems

In this paper, we propose a spectral clustering approach for users and documents group modeling in order to capture the common preference and relatedness of users and documents, and to reduce the time complexity of similarity calculations. In experiments, we investigate the selection of the optimal amount of clusters. We also show a reduction of the time consuming in calculating the similarity for the recommender systems by selecting a centroid first, and then compare the inside item on behalf of each group. keywords: User Profile, Document Profile, Spectral Clustering, Group Profile, Modularity Metric

Using tag-neighbors for query expansion in medical information retrieval

In the context of medical document retrieval, users often under-specified queries lead to undesired search results that suffer from not containing the information they seek, inadequate domain knowledge matches and unreliable sources. To overcome the limitations of under-specified queries, we utilize tags to enhance information retrieval capabilities by expanding users' original queries with context-relevant information. We compute a set of significant tag neighbor candidates based on the neighbor frequency and weight, and utilize the most frequent and weighted neighbors to expand an entry query that has terms matching tags. The proposed approach is evaluated using MedWorm medical article collection and standard evaluation methods from the text retrieval conference (TREC). We compared the baseline of 0.353 for Mean Average Precision (MAP), reaching a MAP 0.491 (+39%) with the query expansion. In-depth analysis shows how this strategy is beneficial when compared with different ranks of the retrieval results. © 2011 IEEE

Identifying candidate datasets for data interlinking

One of the design principles that can stimulate the growth and increase the usefulness of the Web of data is URIs linkage. However, the related URIs are typically in different datasets managed by different publishers. Hence, the designer of a new dataset must be aware of the existing datasets and inspect their content to define sameAs links. This paper proposes a technique based on probabilistic classifiers that, given a datasets S to be published and a set T of known published datasets, ranks each Ti ∈ T according to the probability that links between S and Ti can be found by inspecting the most relevant datasets. Results from our technique show that the search space can be reduced up to 85%, thereby greatly decreasing the computational effort. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39200-9_29

Personalizing the interface in rich Internet applications

Recently, existing design methodologies targeting traditional Web applications have been extended for Rich Internet Application modeling support. These extended methodologies currently cover the traditionally well-established design concerns, i.e. data and navigation design, and provide additional focus on user interaction and presentation capabilities. However, there is still a lack of design support for more advanced functionality that now is typically offered in state-of-the-art Web applications. One yet unsupported design concern is the personalization of content and presentation to the specific user and his/her context, making use of the extra presentational possibilities offered by RIAs. This article addresses this concern and presents an extension of the RIA design approach OOH4RIA, to include presentation personalization support. We show how to extend the RIA development process to model the required personalization at the correct level of abstraction, and how these specifications can be realized using present RIA technology

Using UML and XMI for Generating Adaptive Navigation Sequences in Web-Based Systems

Navigation in information intensive applications to retrieve required information is time consuming task. Without appropriate navigation support the browsing need not lead to required information. One of the navigation support is guiding a user through the information. The guidance has to be adapted according to different requirements and different goals the user can have. A system can adapt the appearence of information and links or links can lead to different alternatives of information based on observed user preferences, goals, level of knowledge. Parametrizing allow us to capture such variability already in the modelling of such applications. Such parametrized models can serve as an input to a generator which will generate appropriate navigation sequence based on the parameters' values. In this paper we discuss a method for generating such adaptive navigation sequences from the UML state diagrams. The method is discussed on a case of adaptive e-course. Latest advances in UML model representation by means of XML based metadata interchange format can be succesfully utilized for adaptive generation of the adaptive navigation sequences and can speed up a prototyping of navigation support in adaptive web-based systems. Adaptive generation means that generator can be parametrized. The parameters' values are set by user intercation with a system. According to the parameters the generator can generate modified navigation support and appearance of information based on the observed user features. The widely accepted standard based means and tools for XML technology are used for implementing a method for transforming UML state diagrams into web site graph and visualisation of that graph

Second International Workshop on Web Science and Information Exchange in the Medical Web (MedEx 2011)

In April 2010, the First International Workshop on Web Science and Information Exchange in the Medical Web took place in Raleigh / North Carolina. This workshop was devoted to the technologies for dealing with social- and multi media data for medical information gathering and exchange. The workshop served as a forum for the confluence of new and multidisciplinary ideas that will help to drive research in the areas of medical web text and data mining.</jats:p