Systematic Construction of Goal-Oriented COTS Taxonomies

Informe de investigación realizado a partir de una estancia en el Department of Computer and Information Science de la Norwegian University of Science and Technology (NTNU), Noruega, entre setiembre i diciembre de 2006. El uso de componentes de software llamados Commercial-Off-The-Shelf (COTS) en el desarrollo de sistemas basados en componentes implica varios retos. Uno de ellos es la falta de información disponible y adecuada para dar soporte al proceso de selección de componentes a ser integrados. Para lidiar con estos problemas, se esta desarrollando un trabajo de tesis que propone un método llamado GOThIC (Goal-Oriented Taxonomy and reuse Infrastructure Construction). El método está orientado a construir una infrastructura de reuse para facilitar la búsqueda y reuse de componentes COTS. La estancia en la NTNU, reportada en este documento, tuvo como objetivo primordial las mejora del método y la obtención de datos empíricos para darle soporte. Algunos de los principales resultados fueron la obtención de datos empíricos fundamentando la utilización del método en ámbitos industriales de selección de componentes COTS, así como una nueva estrategia para conseguir de forma factible e incremental, la federación y reuso de los diferentes esfuerzos existentes para encontrar, seleccionar y mantener componentes COTS y Open Source (OSS) -comúnmente llamados componentes Off-The-Shelf (OTS) - en forma estructurada.

Analyzing hidden semantics in social bookmarking of open educational resources

Web 2.0 services such as social bookmarking allow users to manage and share the links they find interesting, adding their own tags for describingthem. This is especially interesting in the field of open educational resources, asdelicious is a simple way to bridge the institutional point of view (i.e. learningobject repositories) with the individual one (i.e. personal collections), thuspromoting the discovering and sharing of such resources by other users. In this paper we propose a methodology for analyzing such tags in order to discover hidden semantics (i.e. taxonomies and vocabularies) that can be used toimprove descriptions of learning objects and make learning object repositories more visible and discoverable. We propose the use of a simple statistical analysis tool such as principal component analysis to discover which tags createclusters that can be semantically interpreted. We will compare the obtained results with a collection of resources related to open educational resources, in order to better understand the real needs of people searching for open educational resources.

Repurposing Social Tagging Data for Extraction of Domain-level Concepts

The World Wide Web, the world¿s largest resource for information, has evolved from organizing information using controlled, top-down taxonomies to a bottom up approach that emphasizes assigning meaning to data via mechanisms such as the Social Web (Web 2.0). Tagging adds meta-data, (weak semantics) to the content available on the web. This research investigates the potential for repurposing this layer of meta-data. We propose a multi-phase approach that exploits user-defined tags to identify and extract domain-level concepts. We operationalize this approach and assess its feasibility by application to a publicly available tag repository. The paper describes insights gained from implementing and applying the heuristics contained in the approach, as well as challenges and implications of repurposing tags for extraction of domain-level concepts.