| |  | Ebner, Martin | E-Learning 2.0 = e-Learning 1.0 + Web 2.0? read moreAbstract: You" has been elected as person of the year by the Time Magazine, Web 2.0 is the most quoted article of Wikipedia in 2006 and e-Learning 2.0 is the buzzword of today. This article likes to point out what are the advantages and disadvantages of this hype. Is e-Learning 1.0 a thing of the past or still necessary for the learning future. The question whether Web 2.0 will change the education of tomorrow or not will be answered in a very critical way. The summary of this article pointed out that there is considerably more than using new applications and bring them together with the experiences of e-Learning 1.0. Due to the fact that the importance of e-Learning 2.0 is growing very fast it can be summarized that a lot of more research work must be done in future | 2007 |
| | | Marchionini, Gary | Exploratory search: from finding to understanding read moreAbstract: From the earliest days of computers, search has been a fundamental application that has driven research and development. For example, a paper published in the inaugural year of the IBM journal 36 years ago outlined challenges of text retrieval that continue to the present [4] . Today's data storage and retrieval applications range from database systems that manage the bulk of the world's structured data to Web search engines that provide access to petabytes of text and multimedia data. As computers have become consumer products and the Internet has become a mass medium, searching the Web has become a daily activity for everyone from children to research scientists. | 2006 |
| | | Han, H. | Two supervised learning approaches for name disambiguation in author citations read moreAbstract: Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, Web search, database integration, and may cause improper attribution to authors. We investigate two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses support vector machines (SVMs) [V. Vapnik (1995)] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: coauthor names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the Web, mainly publication lists from homepages, the other collected from the DBLP citation databases. | 2004 |
| | | Kaestle, G. | Sharing experiences from scientific experiments read moreAbstract: The ESP2Net project is developing technologies that enable effective collaborative scientific data sharing to support collaboration among scientists, accelerate production of scientific data products, and improve understanding of the science. We have defined a Scientific Experiment Markup Language (SEML) to capture scientific experiments in hypermedia documents as a basic unit of information sharing. A collection of SEML documents can be viewed as an online electronic experiment logbook that captures the entire experiment experience by including the process and interrelationships between experiments to allow an experiment to be re-created. Complementary means of sharing the experiences from scientific experiments (browsing, searching, dissemination, and mining) are provided by integrating OASIS transparent distributed scientific object access, Conquest dynamic distributed query processing services, and active information dissemination services introduced in semantic multicast | 1999 |
| | | Montebello, M. | Information overload-an IR problem? read moreAbstract: Information overload on the World Wide Web (WWW) is a well recognised problem. Research to subdue this problem and extract maximum benefit from the Internet is still in its infancy. With huge amounts of information connected to the Internet, efficient and effective discovery of resources and knowledge has become an imminent research issue. A vast array of network services is growing up around the Internet and a massive amount of information is added everyday. Despite the potential benefits of existing indexing, retrieving and searching techniques in assisting users in the browsing process, little has been done to ensure that the information presented is of a high recall and precision standard. Therefore, search for specific information on this massive and exploding information resource base becomes highly critical. The author discusses the issues involved in resolving the information overload over the WWW and argues that this is solely an information retrieval problem. As a contribution to the field he proposes a general architecture to subdue information overload and describes how this architecture has been instantiated in a functional system he developed | 1998 |
| | | Chen, Hsinchun | Internet Categorization and Search: A Self-Organizing Approach read moreAbstract: The problems of information overload and vocabulary differences have become more pressing with the emergence of increasingly popular Internet services. The main information retrieval mechanisms provided by the prevailing Internet WWW software are based on either keyword search (e.g., the Lycos server at CMU, the Yahoo server at Stanford) or hypertext browsing (e.g., Mosaic and Netscape). This research aims to provide an alternative concept-based categorization and search capability for WWW servers based on selected machine learning algorithms. Our proposed approach, which is grounded on automatic textual analysis of Internet documents (homepages), attempts to address the Internet search problem by firstcategorizingthe content of Internet documents. We report results of our recent testing of a multilayered neural network clustering algorithm employing the Kohonen self-organizing feature map to categorize (classify) Internet homepages according to their content. The category hierarchies created could serve to partition the vast Internet services into subject-specific categories and databases and improve Internet keyword searching and/or browsing.
| 1996 |
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