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comparison is included as keyword or extra keyword in 0 datasets, 0 tools and 6 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Exploiting the wisdom of the crowds for characterizing and connecting heterogeneous resources||Kawase R.
Pereira Nunes B.
|HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media||English||2014||Heterogeneous content is an inherent problem for cross-system search, recommendation and personalization. In this paper we investigate differences in topic coverage and the impact of topics in different kinds of Web services. We use entity extraction and categorization to create fingerprints that allow for meaningful comparison. As a basis taxonomy, we use the 23 main categories of Wikipedia Category Graph, which has been assembled over the years by the wisdom of the crowds. Following a proof of concept of our approach, we analyze differences in topic coverage and topic impact. The results show many differences between Web services like Twitter, Flickr and Delicious, which reflect users' behavior and the usage of each system. The paper concludes with a user study that demonstrates the benefits of fingerprints over traditional textual methods for recommendations of heterogeneous resources.||0||0|
|Extracting complementary information from Wikipedia articles of different languages||Akiyo Nadamoto
|International Journal of Business Intelligence and Data Mining||English||2013||In Wikipedia, users can create and edit information freely. Few editors take responsibility for editing the articles. Therefore, information of many Wikipedia articles is lacking. Furthermore, Wikipedia has different levels of value of its information depending on the language version of the site. In this paper, we propose the extraction of complementary information from different language Wikipedia and its automatic presentation. The important points of our method are: 1) extraction of comparison articles from different language Wikipedia; 2) extraction of complementary information; 3) presentation of complementary information.||0||0|
|Assessing the accuracy and quality of Wikipedia entries compared to popular online encyclopaedias||Imogen Casebourne
|English||2 August 2012||8||0|
|Identifying controversial articles in Wikipedia: A comparative study||Hoda Sepehri Rad
|WikiSym||English||August 2012||Wikipedia articles are the result of the collaborative editing of a diverse group of anonymous volunteer editors, who are passionate and knowledgeable about specific topics. One can argue that this plurality of perspectives leads to broader coverage of the topic, thus benefitting the reader. On the other hand, differences among editors on polarizing topics can lead to controversial or questionable content, where facts and arguments are presented and discussed to support a particular point of view. Controversial articles are manually tagged by Wikipedia editors, and span many interesting and popular topics, such as religion, history, and politics, to name a few. Recent works have been proposed on automatically identifying controversy within unmarked articles. However, to date, no systematic comparison of these efforts has been made. This is in part because the various methods are evaluated using different criteria and on different sets of articles by different authors, making it hard for anyone to verify the efficacy and compare all alternatives. We provide a first attempt at bridging this gap. We compare five different methods for modelling and identifying controversy, and discuss some of the unique difficulties and opportunities inherent to the way Wikipedia is produced.||0||0|
|The world within wikipedia: An Ecology of Mind||Olney A.M.
|Information (Switzerland)||English||2012||Human beings inherit an informational culture transmitted through spoken and written language. A growing body of empirical work supports the mutual influence between language and categorization, suggesting that our cognitive-linguistic environment both reflects and shapes our understanding. By implication, artifacts that manifest this cognitive-linguistic environment, such asWikipedia, should represent language structure and conceptual categorization in a way consistent with human behavior. We use this intuition to guide the construction of a computational cognitive model, situated in Wikipedia, that generates semantic association judgments. Our unsupervised model combines information at the language structure and conceptual categorization levels to achieve state of the art correlation with human ratings on semantic association tasks including WordSimilarity-353, semantic feature production norms, word association, and false memory.||0||0|
|Exploring linguistic points of view of Wikipedia||Paolo Massa
|WikiSym||English||2011||The 3 million articles of the English Wikipedia has been written since 2011 by more than 14 million volunteers. On each article, the community of editors strive to reach a neutral point of view, representing all significant views fairly, proportionately, and without bias. However, beside the English one, there are more than 270 Wikipedias in different languages and their relatively isolated communities of editors are not forced by the platform to discuss and negotiate their points of view. So the empirical question is: do communities on different languages editions of Wikipedia develop their own diverse Linguistic Points of View (LPOV)? To answer this question we created Manypedia, a web tool whose goal is to ease cross-cultural comparisons of Wikipedia language communities by analyzing their different representations of the same topic.||0||1|