U-Search: A meta engine for creation of knowledge paths on the web

[9] . The main limitations of those tools are the pertinence of found information with the user search (mainly for search engines) and the potential information overload due to the amount of found results. This is due to the inability of search tools to understand user specific needs and starting knowledge. Moreover, the Web has evolved towards the concept of collective participation to site contents and in Web 2.0 we find many environments oriented to information and knowledge sharing The understanding of user needs together with the evolution of the Web is bringing, as a natural consequence, to the development of new research fields that innovate the search strategies. For example, Natural Language Processing To this end, we consider different searcher categories such as a "basic searcher" who knows little about a topic and will look for more information, a "deep searcher" who will look for specific details on a topic that he/she already knows and a "wide searcher" who will look for expanding his/her knowledge domain with topics that are loosely related to the starting topic. The meta engine will suggest words and web pages correlated to each of those searcher categories thus creating new knowledge paths tailored to the real user needs

Aplicación de la Inteligencia Competitiva y el Benchmarking de nuevas teorías para el desarrollo de un Plan Estratégico y Sostenible para la Industria Naval

Since their beginning, companies establish procedures to observe their competitors. Methods for obtaining this kind of information have evolved with the internet era; a plethora of tools is nowadays available for this job. As a consequence, a new problem has emerged: documentary noise, keeping companies from being able to process and benefit from the huge amount of information gathered. Strategic planning mainly relies on obtaining environmental knowledge, so companies need help on dealing with this documentary noise; technological surveillance and benchmarking are preferred methodologies to achieve this objective, coping with data produced by automatic internet tools like search engines and others. Qualified results of better nature are produced by bringing new theories on information gathering and processing intoboth tools. This article exposes empirical results on the application of a demonstrative technological surveillance system based on different R&D management structures, relying on benchmarking indicators for the naval and aeronautics industries.Desde su inicio, las empresas establecen procedimientos para observar a sus competidores. Los métodos para obtener este tipo de información han evolucionado con la era del internet; una gran cantidad de herramientas está disponible en la actualidad para esta tarea. En consecuencia, ha surgido un nuevo problema: ruido documental, que evita que las empresas procesen y se beneficien de la gran cantidad de información recolectada. La planeación estratégica principalmente se apoya en el conocimiento ambiental obtenido, así que las empresas necesitan ayuda para tratar con este ruido documental; la vigilancia tecnológica y el benchmarking son metodologías preferidas para lograr este objetivo, y hacerfrente a los datos producidos por herramientas automáticas del internet como motores de búsqueda y otras. Este artículo expone resultados empíricos acerca de la aplicación de un sistema demostrativo de vigilancia tecnológica basado en diferentes estructuras de gestión de I&D, confiando en indicadores de benchmarking para las industrias navales y aeronáuticas

A Web Search Methodology for Different User Typologies

Search engines and directories are the main tools used to find desired information into the ocean of digital contents that is the Web. However, they are not presently able to understand the user specific needs and starting knowledge because their inability to simulate the processes of human mind. Natural Language Processing, Folksonomy, Semantic Web and Serendipitous Surfing are some of the recent research fields towards understanding of human natural language and in general of real user needs. This work aims to add one step more to this evolution path by presenting a new search methodology that allows users to create new knowledge paths on the web based on their specific requirements. Thus, we consider different web-learners typologies such as “basic searchers", "deep searchers" and "wide searchers" with different starting knowledges and search expectations. This methodology has been used to run some preliminary experiments and the results are more than encouraging

Selecting and tailoring of images for online news content: a mixed-methods investigation of the needs and behaviour of image users in online journalism

This mixed-methods investigation explores how image professionals in online journalism search for, select and use images from large online collections. Further, findings from this exploration are used to devise and evaluate a needs-based practical solution for improvement to image retrieval. The exploratory stage included semi-structured interviews and observations in situ and provided several important contributions to the current understanding of the needs and behaviour of image users in fully disintermediated environment of the online newsroom. This study found that these image users are creative professionals and self-taught, yet, confident image searchers. When illustrating news content, they apply a shared knowledge of how a specific image function (e.g., dominant image) must be presented visually to reach its full communication potential. This common understanding of image communicative functions has two implications on how these professionals search for and select images. Firstly, they begin searches with clear image needs pre-defined on multiple levels of image description, including visual image features, and their behaviour is consistent with targeted searching. This contradicts previously reported preference for browsing as the typical mode of searching in online image collections. Secondly, they do not easily compromise on image needs related to visual features. When searches prove ineffective, they resort to editing skills and tailor the available images to match their original needs. Further, it was found that the choice of images for headline content can in fact be predicted by a set of 11 visual image features. The features were extracted from a collection of artefacts created in the observation sessions and described by means of the Visual Social Semiotics (VSS) framework. The feature set was implemented as a filtering mechanism in a prototype and evaluated in a within-subjects experimental design study with image professionals. This experiment showed a significant positive change in the behaviour of users when interacting with images pre-filtered strictly to their visual needs, not observed in the baseline system. This was demonstrated through users’ ability to immediately engage in the inspection of images on a level of detail, and to make straightforward selections. Images from the experimental sets required no or only minimal tailoring as confirmed in the final VSS-based survey with independent image experts. Other important contributions of this investigation include the updated models. Firstly, the illustration task process framework, originally proposed in Markkula and Sormunen (2000), has been refined to include the image tailoring phase where creative professionals apply editorial treatment before publication. Further, the observations revealed that verifying of images, consistent with the feature in Ellis et al.’s model (Ellis et al., 1993), was an activity critical to making selection decision in online journalism. Therefore, Conniss et al.’s model of the image searching process (Conniss et al., 2000) has been updated to include the verifying phase. The investigation concludes that in order to meet the needs of creative image professionals in online journalism, image retrieval systems must support targeted searching, and facilitate direct access to required images that can be easily verified for authenticity. The proposed multi-feature filtering system firmly rooted in the image users’ needs, appears to be a step towards automating image retrieval