Abstract
In Portugal, e-Gov is considered to be a key feature in the process of continuously developing an Information Society becoming itself a vital process in the context of modernizing public administration. Its higher priority it's to improve the quality of public services through the use of ICT (Information and Communications Technology). Although some great examples, Portugal still presents some low values in the main measures of ICT, when compared to the majority of European countries, The low investment, a almost inexistence in ICT qualification and the reduced knowledge that enterprises and citizens have about e-Gov potentiality's, are some of the obstacles to a higher success of e-Gov. In this context, we consider important to verify, as they are going to be the futures employees of public administration, the knowledge of public administration high students have about e-Gov, what it's the importance they gave to it, what services they now, use and propose.

Abstract
Typically, search engines are low precision in response to a query, retrieving lots of useless web pages, and missing some other important ones. In this paper, we study the problem of the hierarchical clustering of web pages search results. In particular, we propose an architecture called FUSE [1], a meta-search engine that automatically builds clusters of related web pages embodying one meaning of the query. These clusters are then hierarchically organized and labeled with a phrase representing the key concept of the cluster and the corresponding web documents. The system which is a web-based interface (soon available at wise.di.ubi.pt), introduces some interesting new ideas, such as the pre-selection of the retrieved web pages, the capacity to statistically detect phrases within documents and the representation of documents based on their most relevant key concepts by using web content mining techniques. The final step of the system is supported by a graph-based overlapping clustering algorithm which groups the selected documents into a hierarchy of clusters.

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Abstract
With the growing popularity of research in Temporal Information Retrieval (T-IR), a large amount of temporal data is ready to be exploited. The ability to exploit this information can be potentially useful for several tasks. For example, when querying "Football World Cup Germany", it would be interesting to have two separate clusters {1974,2006} corresponding to each of the two temporal instances. However, clustering of search results by time is a non-trivial task that involves determining the most relevant dates associated to a query. In this paper, we propose a first approach to flat temporal clustering of search results. We rely on a second order co-occurrence similarity measure approach which first identifies top relevant dates. Documents are grouped at the year level, forming the temporal instances of the query. Experimental tests were performed using real-world text queries. We used several measures for evaluating the performance of the system and compared our approach with Carrot Web-snippet clustering engine. Both experiments were complemented with a user survey.

Abstract
In this paper, we present an approach to identify top relevant dates in Web snippets with respect to a given implicit temporal query. Our approach is two-fold. First, we propose a generic temporal similarity measure called GTE, which evaluates the temporal similarity between a query and a date. Second, we propose a classification model to accurately relate relevant dates to their corresponding query terms and withdraw irrelevant ones. We suggest two different solutions: a threshold-based classification strategy and a supervised classifier based on a combination of multiple similarity measures. We evaluate both strategies over a set of real-world text queries and compare the performance of our Web snippet approach with a query log approach over the same set of queries. Experiments show that determining the most relevant dates of any given implicit temporal query can be improved with GTE combined with the second order similarity measure InfoSimba, the Dice coefficient and the threshold-based strategy compared to (1) first-order similarity measures and (2) the query log based approach. © 2012 ACM.