| Fernando S. Nifrário Rodrigues|
(Alternative names for this author)
|Co-authors||Alves A.O., Oliveirinha J., Pereira F.C.|
|Authorship||Publications (2), datasets (0), tools (0)|
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PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Mass Collaboration or Mass Amateurism? A comparative study on the quality of scientific information produced using Wiki tools and concepts||Mass Collaboration
|Universidade Évora||Portuguese||December 2012||With this PhD dissertation, we intend to contribute to a better understanding of the Wiki phenomenon as a knowledge management system which aggregates private knowledge. We also wish to check to what extent information generated through anonymous and freely bestowed mass collaboration is reliable as opposed to the traditional approach.
In order to achieve that goal, we develop a comparative study between Wikipedia and Encyclopaedia Britannica with regard to accuracy, depth and detail of information in both, in order to confront the quality of the knowledge repository produced by them. That will allow us to reach a conclusion about the efficacy of the business models behind them.
We will use a representative random sample which is composed by the articles that are comprised in both encyclopedias. Each pair of articles was previously reformatted and then graded by an expert in its subject area. At the same time, we collected a small convenience sample which only integrates Management articles. Each pair of articles was graded by several experts in order to determine the uncertainty associated with having diverse gradings of the same article and apply it to the evaluations carried out by just one expert. The conclusion was that the average quality of the Wikipedia articles which were analysed was superior to its peers’ and that this difference was statistically significant.
An inquiry was conducted within the academia which certified that traditional information sources were used by a minority as the first approach to seeking information. This inquiry also made clear that reliance on these sources was considerably larger than reliance on information obtained through Wikipedia. This quality perception, as well as the diametrically opposed results of its evaluation through a blind test, reinforces the evaluating panel’s exemption.
However much the chosen sample is representative of the universe to be studied, results have depended on the evaluators’ personal opinion and chosen criteria. This means that the reproducibility of this study’s conclusions using a different grading panel cannot be guaranteed. Nevertheless, this is not enough of a reason to reject the study results obtained through more than five hundred evaluations.This thesis is thus an attempt to help clarifying this topic and contributing to a better perception of the quality of a tool which is daily used by millions of people, of the mass collaboration which feeds it and of the collaborative software that supports it.
|Place in perspective: Extracting online information about points of interest||Lecture Notes in Computer Science||English||2010||During the last few years, the amount of online descriptive information about places has reached reasonable dimensions for many cities in the world. Being such information mostly in Natural Language text, Information Extraction techniques are needed for obtaining the meaning of places that underlies these massive amounts of commonsense and user made sources. In this article, we show how we automatically label places using Information Extraction techniques applied to online resources such as Wikipedia, Yellow Pages and Yahoo!.||0||0|