https://wikipediaquality.com/api.php?action=feedcontributions&user=Audrey&feedformat=atomWikipedia Quality - User contributions [en]2024-03-29T14:35:46ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Adhocratic_Governance_in_the_Internet_Age:_a_Case_of_Wikipedia&diff=23983Adhocratic Governance in the Internet Age: a Case of Wikipedia2020-05-06T05:54:35Z<p>Audrey: Int.links</p>
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<div>'''Adhocratic Governance in the Internet Age: a Case of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Piotr Konieczny]].<br />
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== Overview ==<br />
In recent years, a new realm has appeared for the study of political and sociological phenomena: the Internet. This article will analyze the decision-making processes of one of the largest online communities, [[Wikipedia]]. Founded in 2001, Wikipedia—now among the top-10 most popular sites on the Internet—has succeeded in attracting and organizing millions of volunteers and creating the world's largest encyclopedia. To date, however, little study has been done of Wikipedia's governance. There is substantial confusion about its decision-making structure. The organization's governance has been compared to many decision-making and political systems—from democracy to dictatorship, from bureaucracy to anarchy. It is the purpose of this article to go beyond the earlier simplistic descriptions of Wikipedia's governance in order to advance the study of online governance, and of organizations more generally. As the evidence will show, while Wikipedia's governance shows elements common to many traditional governance mo...</div>Audreyhttps://wikipediaquality.com/index.php?title=Reference_Works_in_the_Age_of_Wikipedia:_a_Review_of_Print_and_Electronic_Holdings_at_the_University_of_Queensland_Library&diff=23982Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library2020-05-06T05:51:55Z<p>Audrey: + embed code</p>
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<div>{{Infobox work<br />
| title = Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library<br />
| date = 2016<br />
| authors = [[Jeanette O'Shea]]<br />
| link = https://espace.library.uq.edu.au/view/UQ:553564<br />
}}<br />
'''Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Jeanette O'Shea]].<br />
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== Overview ==<br />
The purpose of this snapshot project is to review reference works held by the University of Queensland Library (UQ Library). This involves making recommendations regarding their ongoing management, and to provide a methodological framework for the continuous evaluation and strategic management of reference works. It was carried out over a limited 10-day working period.<br />
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O'Shea, Jeanette. (2016). "[[Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library]]". University of Queensland. Library. <br />
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{{cite journal |last1=O'Shea |first1=Jeanette |title=Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library |date=2016 |url=https://wikipediaquality.com/wiki/Reference_Works_in_the_Age_of_Wikipedia:_a_Review_of_Print_and_Electronic_Holdings_at_the_University_of_Queensland_Library |journal=University of Queensland. Library}}<br />
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O'Shea, Jeanette. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Reference_Works_in_the_Age_of_Wikipedia:_a_Review_of_Print_and_Electronic_Holdings_at_the_University_of_Queensland_Library">Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library</a>&amp;quot;. University of Queensland. Library. <br />
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</code></div>Audreyhttps://wikipediaquality.com/index.php?title=Wikipedia-Based_Entity_Semantifying_in_Open_Information_Extraction&diff=23981Wikipedia-Based Entity Semantifying in Open Information Extraction2020-05-06T05:49:50Z<p>Audrey: Int.links</p>
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<div>'''Wikipedia-Based Entity Semantifying in Open Information Extraction''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Qiuhao Lu]] and [[Youtian Du]].<br />
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== Overview ==<br />
In the recent years, Open Information Extraction (OIE), an unsupervised strategy which extracts open-domain facts of knowledge from massive heterogeneous text corpora, has achieved impressive improvements. However, the facts (generally represented by a triple) extracted by OIE systems are in lack of clear semantics and then difficult for computer systems to understand. In this paper, authors present a new method to semantify the facts by mapping the string arguments in the triples to the corresponding real-world entities based on the existing knowledge base [[Wikipedia]]. First, for each query of string argument, authors consider a set of its most likely mapping entities and assign each candidate a fused prior probability. Then authors calculate the graph-based similarity between candidates as the contextual evidence by propagating semantics on the neighborhood graph of candidates. Finally, authors transform the mapping task into an optimization problem and find the maximum a posteriori (MAP) mapping by combining the prior information and contextual evidence through Bayes' theorem. Due to the fusion of multiple cues and the semantics propagation over the graph, approach improves the performance of the entity semantifying. Experimental results demonstrate the effectiveness of approach.</div>Audreyhttps://wikipediaquality.com/index.php?title=Modeling_New_and_Old_Editors%E2%80%99_Behaviors_in_Different_Languages_of_Wikipedia&diff=23980Modeling New and Old Editors’ Behaviors in Different Languages of Wikipedia2020-05-06T05:48:34Z<p>Audrey: Embed</p>
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<div>{{Infobox work<br />
| title = Modeling New and Old Editors’ Behaviors in Different Languages of Wikipedia<br />
| date = 2018<br />
| authors = [[Anita Chandra]]<br />[[Abyayananda Maiti]]<br />
| doi = 10.1007/978-3-030-02925-8_31<br />
| link = https://link.springer.com/openurl?id=doi:10.1007/978-3-030-02925-8_31<br />
}}<br />
'''Modeling New and Old Editors’ Behaviors in Different Languages of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Anita Chandra]] and [[Abyayananda Maiti]].<br />
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== Overview ==<br />
Wikipedia is an [[open-source]] [[multilingual]] encyclopedia which allows users to edit, create and share their knowledge collaboratively. Size of its contents such as articles, editors, links and language editions grows too fast with time. In this paper, authors model the growth of editor-article bipartite network of multilingual [[Wikipedia]]s to investigate behaviors of editors. In this bipartite network, editors and articles are two disjoint sets and if an editor edits an article then it forms an edge between them. The both editors and articles arrive simultaneously into their respective sets and editing is done by editors. The Wiki networks grow by the creation of external edits performed by new editors and/or internal edits done by old editors. These edits are done with a combination of preferential and/or random attachment mechanism. Authors consider two different randomness parameters for new and old editors in their attachment procedures. Authors validate the growth model over 20 largest language editions of Wikipedia and results show good agreement between model and each of the considered languages. After interpreting the values of parameters authors notice contrast in editing behaviors of new and old editors in every language. Authors also notice this non-uniform behavior of editors varies across all the languages. Thus, authors report uncommon growth processes and difference in editing behaviors of editors of [[different language]]s of Wikipedia.<br />
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Chandra, Anita; Maiti, Abyayananda. (2018). "[[Modeling New and Old Editors’ Behaviors in Different Languages of Wikipedia]]". Springer, Cham. DOI: 10.1007/978-3-030-02925-8_31. <br />
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{{cite journal |last1=Chandra |first1=Anita |last2=Maiti |first2=Abyayananda |title=Modeling New and Old Editors’ Behaviors in Different Languages of Wikipedia |date=2018 |doi=10.1007/978-3-030-02925-8_31 |url=https://wikipediaquality.com/wiki/Modeling_New_and_Old_Editors’_Behaviors_in_Different_Languages_of_Wikipedia |journal=Springer, Cham}}<br />
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Chandra, Anita; Maiti, Abyayananda. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Modeling_New_and_Old_Editors’_Behaviors_in_Different_Languages_of_Wikipedia">Modeling New and Old Editors’ Behaviors in Different Languages of Wikipedia</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-030-02925-8_31. <br />
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</code></div>Audreyhttps://wikipediaquality.com/index.php?title=Extracting_Lack_of_Information_on_Wikipedia_by_Comparing_Multilingual_Articles&diff=23979Extracting Lack of Information on Wikipedia by Comparing Multilingual Articles2020-05-06T05:45:56Z<p>Audrey: Category</p>
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<div>{{Infobox work<br />
| title = Extracting Lack of Information on Wikipedia by Comparing Multilingual Articles<br />
| date = 2012<br />
| authors = [[Yuya Fujiwara]]<br />[[Yukio Konishi]]<br />[[Yu Suzuki]]<br />[[Akiyo Nadamoto]]<br />
| doi = 10.1145/2428736.2428808<br />
| link = https://dl.acm.org/citation.cfm?id=2428736.2428808<br />
}}<br />
'''Extracting Lack of Information on Wikipedia by Comparing Multilingual Articles''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Yuya Fujiwara]], [[Yukio Konishi]], [[Yu Suzuki]] and [[Akiyo Nadamoto]].<br />
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== Overview ==<br />
Wikipedia has [[multilingual]] articles, the information of which differs, even for articles on the same topic. As described in this paper, authors propose a system to extract and present lack of information of one language on [[Wikipedia]] by comparing two languages on the Wikipedia. When authors compare Wikipedia articles of two languages, the granularity of information between them differs. Therefore, authors propose a method of extracting multiple comparison articles using a Wikipedia link graph. The system extracts lack of information that is included in articles in Wikipedia by comparing one base article with other articles that are found using the link graph.<br />
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Fujiwara, Yuya; Konishi, Yukio; Suzuki, Yu; Nadamoto, Akiyo. (2012). "[[Extracting Lack of Information on Wikipedia by Comparing Multilingual Articles]]".DOI: 10.1145/2428736.2428808. <br />
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{{cite journal |last1=Fujiwara |first1=Yuya |last2=Konishi |first2=Yukio |last3=Suzuki |first3=Yu |last4=Nadamoto |first4=Akiyo |title=Extracting Lack of Information on Wikipedia by Comparing Multilingual Articles |date=2012 |doi=10.1145/2428736.2428808 |url=https://wikipediaquality.com/wiki/Extracting_Lack_of_Information_on_Wikipedia_by_Comparing_Multilingual_Articles}}<br />
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Fujiwara, Yuya; Konishi, Yukio; Suzuki, Yu; Nadamoto, Akiyo. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extracting_Lack_of_Information_on_Wikipedia_by_Comparing_Multilingual_Articles">Extracting Lack of Information on Wikipedia by Comparing Multilingual Articles</a>&amp;quot;.DOI: 10.1145/2428736.2428808. <br />
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[[Category:Scientific works]]</div>Audreyhttps://wikipediaquality.com/index.php?title=From_Wikipedia_to_the_Classroom:_Exploring_Online_Publication_and_Learning&diff=23978From Wikipedia to the Classroom: Exploring Online Publication and Learning2020-05-06T05:43:06Z<p>Audrey: Adding embed</p>
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<div>{{Infobox work<br />
| title = From Wikipedia to the Classroom: Exploring Online Publication and Learning<br />
| date = 2006<br />
| authors = [[Andrea Forte]]<br />[[Amy Bruckman]]<br />
| link = http://dl.acm.org/citation.cfm?id=1150061<br />
}}<br />
'''From Wikipedia to the Classroom: Exploring Online Publication and Learning''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Andrea Forte]] and [[Amy Bruckman]].<br />
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== Overview ==<br />
Wikipedia represents an intriguing new publishing paradigm---can it be used to engage students in authentic collaborative writing activities? How can authors design wiki publishing tools and curricula to support learning among student authors? Authors suggest that wiki publishing environments can create learning opportunities that address four dimensions of authenticity: personal, real world, disciplinary, and assessment. Authors have begun a series of design studies to investigate links between wiki publishing experiences and writing-to-learn. The results of an initial study in an undergraduate government course indicate that perceived audience plays an important role in helping students monitor the quality of writing; however, students' perception of audience on the Internet is not straightforward. This preliminary iteration resulted in several guidelines that are shaping efforts to design and implement new wiki publishing tools and curricula for students and teachers.<br />
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Forte, Andrea; Bruckman, Amy. (2006). "[[From Wikipedia to the Classroom: Exploring Online Publication and Learning]]". International Society of the Learning Sciences. <br />
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{{cite journal |last1=Forte |first1=Andrea |last2=Bruckman |first2=Amy |title=From Wikipedia to the Classroom: Exploring Online Publication and Learning |date=2006 |url=https://wikipediaquality.com/wiki/From_Wikipedia_to_the_Classroom:_Exploring_Online_Publication_and_Learning |journal=International Society of the Learning Sciences}}<br />
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Forte, Andrea; Bruckman, Amy. (2006). &amp;quot;<a href="https://wikipediaquality.com/wiki/From_Wikipedia_to_the_Classroom:_Exploring_Online_Publication_and_Learning">From Wikipedia to the Classroom: Exploring Online Publication and Learning</a>&amp;quot;. International Society of the Learning Sciences. <br />
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</code></div>Audreyhttps://wikipediaquality.com/index.php?title=A_Semantic_Web-Based_Approach_for_Harvesting_Multilingual_Textual_Definitions_from_Wikipedia_to_Support_Icd-11_Revision&diff=23977A Semantic Web-Based Approach for Harvesting Multilingual Textual Definitions from Wikipedia to Support Icd-11 Revision2020-05-06T05:40:28Z<p>Audrey: + wikilinks</p>
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<div>'''A Semantic Web-Based Approach for Harvesting Multilingual Textual Definitions from Wikipedia to Support Icd-11 Revision''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Guoqian Jiang]], [[Harold R. Solbrig]] and [[Christopher G. Chute]].<br />
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== Overview ==<br />
In</div>Audreyhttps://wikipediaquality.com/index.php?title=Improving_Human-Agent_Conversations_by_Accessing_Contextual_Knowledge_from_Wikipedia&diff=23976Improving Human-Agent Conversations by Accessing Contextual Knowledge from Wikipedia2020-05-06T05:37:54Z<p>Audrey: Infobox work</p>
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<div>{{Infobox work<br />
| title = Improving Human-Agent Conversations by Accessing Contextual Knowledge from Wikipedia<br />
| date = 2010<br />
| authors = [[Alexa Breuing]]<br />
| doi = 10.1109/WI-IAT.2010.231<br />
| link = https://dl.acm.org/citation.cfm?id=1913981<br />
}}<br />
'''Improving Human-Agent Conversations by Accessing Contextual Knowledge from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Alexa Breuing]].<br />
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== Overview ==<br />
In order to talk to each other meaningfully, conversational partners utilize different types of conversational knowledge. Due to the fact that speakers often use grammatically incomplete and incorrect sentences in spontaneous language, knowledge about conversational and terminological context turns out to be as much important in language understanding as traditional linguistic analysis. In the context of the KnowCIT project authors want to improve human-agent conversations by connecting the agent to an adequate representation of such contextual knowledge drawn from the online encyclopedia [[Wikipedia]]. Thereby authors make use of additional components provided by Wikipedia which goes beyond encyclopedical information to identify the current dialog topic and to implement human like look-up abilities.</div>Audreyhttps://wikipediaquality.com/index.php?title=Tweet_Contextualization_based_on_Wikipedia_and_Dbpedia&diff=23975Tweet Contextualization based on Wikipedia and Dbpedia2020-05-06T05:36:13Z<p>Audrey: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Tweet Contextualization based on Wikipedia and Dbpedia<br />
| date = 2016<br />
| authors = [[Meriem Amina Zingla]]<br />[[Chiraz Latiri]]<br />[[Yahya Slimani]]<br />[[Catherine Berrut]]<br />[[Philippe Mulhem]]<br />
| link = https://hal.archives-ouvertes.fr/hal-01346224<br />
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'''Tweet Contextualization based on Wikipedia and Dbpedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Meriem Amina Zingla]], [[Chiraz Latiri]], [[Yahya Slimani]], [[Catherine Berrut]] and [[Philippe Mulhem]].<br />
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== Overview ==<br />
Bound to 140 characters, tweets are short and not written maintaining formal grammar and proper spelling. These spelling variations increase the likelihood of vocabulary mismatch and make them difficult to understand without context. This paper falls under the tweet contextualization task that aims at providing, automatically, a summary that explains a given tweet, allowing a reader to understand it. Authors propose different tweet expansion approaches based on Wikipeda and Dbpedia as external knowledge sources. These proposed approaches are divided into two steps. The first step consists in generating the candidate terms for a given tweet, while the second one consists in ranking and selecting these candidate terms using a</div>Audreyhttps://wikipediaquality.com/index.php?title=Wikipedia_Chemical_Structure_Explorer:_Substructure_and_Similarity_Searching_of_Molecules_from_Wikipedia&diff=23974Wikipedia Chemical Structure Explorer: Substructure and Similarity Searching of Molecules from Wikipedia2020-05-06T05:34:34Z<p>Audrey: + links</p>
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<div>'''Wikipedia Chemical Structure Explorer: Substructure and Similarity Searching of Molecules from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Peter Ertl]], [[Luc Patiny]], [[Thomas Sander]], [[Christian Rufener]] and [[Michaël Zasso]].<br />
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== Overview ==<br />
Background</div>Audreyhttps://wikipediaquality.com/index.php?title=Agglomerationen_Mit_Nutzergenerierten_Inhalten_Neu_Definiert_Visualisierung_Der_Nordostschweiz_Mithilfe_Von_Wikipedia&diff=23973Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia2020-05-06T05:32:58Z<p>Audrey: + category</p>
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<div>{{Infobox work<br />
| title = Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia<br />
| date = 2013<br />
| authors = [[André Bruggmann]]<br />[[Marco M. Salvini]]<br />[[Sara Irina Fabrikant]]<br />
| doi = 10.1080/02513625.2013.892789<br />
| link = http://www.tandfonline.com/doi/ref/10.1080/02513625.2013.892789<br />
}}<br />
'''Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[André Bruggmann]], [[Marco M. Salvini]] and [[Sara Irina Fabrikant]].<br />
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== Overview ==<br />
Commuters have had an important role in shaping the spatial organization of Switzerland, as commuter flows have been one of the most significant criteria to delineate urban agglomeration zones. Even though urban areas and respective agglomerations have continuously gained in importance in Switzerland to this day, the Swiss national population census will no longer include commuter data at high spatial resolution. Hence, the definition of the rapidly evolving urban agglomeration concept will have to be modified for future urban research and planning purposes.Authors propose a crowdsourcing approach to overcome this data gap, and employ the open and web-based [[Wikipedia]] encyclopedia as a new resource to delineate agglomeration areas. Using the North Eastern parts of Switzerland in this case study, authors systematically evaluate whether user-generated content can serve as an option to fill the commuter data gap in future Swiss national population censuses to define agglomeration areas. In a second step, authors evaluate th...<br />
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Bruggmann, André; Salvini, Marco M.; Fabrikant, Sara Irina. (2013). "[[Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia]]". Routledge. DOI: 10.1080/02513625.2013.892789. <br />
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{{cite journal |last1=Bruggmann |first1=André |last2=Salvini |first2=Marco M. |last3=Fabrikant |first3=Sara Irina |title=Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia |date=2013 |doi=10.1080/02513625.2013.892789 |url=https://wikipediaquality.com/wiki/Agglomerationen_Mit_Nutzergenerierten_Inhalten_Neu_Definiert_Visualisierung_Der_Nordostschweiz_Mithilfe_Von_Wikipedia |journal=Routledge}}<br />
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Bruggmann, André; Salvini, Marco M.; Fabrikant, Sara Irina. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Agglomerationen_Mit_Nutzergenerierten_Inhalten_Neu_Definiert_Visualisierung_Der_Nordostschweiz_Mithilfe_Von_Wikipedia">Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia</a>&amp;quot;. Routledge. DOI: 10.1080/02513625.2013.892789. <br />
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[[Category:Scientific works]]</div>Audreyhttps://wikipediaquality.com/index.php?title=Detecting_Biased_Statements_in_Wikipedia&diff=23972Detecting Biased Statements in Wikipedia2020-05-06T05:30:25Z<p>Audrey: + wikilinks</p>
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<div>'''Detecting Biased Statements in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Christoph Hube]] and [[Besnik Fetahu]].<br />
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== Overview ==<br />
Quality in [[Wikipedia]] is enforced through a set of editing policies and guidelines recommended for [[Wikipedia editors]]. Neutral point of view (NPOV) is one of the main principles in Wikipedia, which ensures that for controversial information all possible points of view are represented proportionally. Furthermore, language used in Wikipedia should be neutral and not opinionated. However, due to the large number of Wikipedia articles and its operating principle based on a voluntary basis of Wikipedia editors; quality assurances and Wikipedia guidelines cannot always be enforced. Currently, there are more than 40,000 articles, which are flagged with NPOV or similar quality tags. Furthermore, these represent only the portion of articles for which such quality issues are explicitly flagged by the Wikipedia editors, however, the real number may be higher considering that only a small percentage of articles are of good quality or featured as categorized by Wikipedia. In this work, authors focus on the case of language bias at the sentence level in Wikipedia. Language bias is a hard problem, as it represents a subjective task and usually the linguistic cues are subtle and can be determined only through its context. Authors propose a supervised classification approach, which relies on an automatically created lexicon of bias words, and other syntactical and semantic characteristics of biased statements. Authors experimentally evaluate approach on a dataset consisting of biased and unbiased statements, and show that authors are able to detect biased statements with an accuracy of 74%. Furthermore, authors show that competitors that determine bias words are not suitable for detecting biased statements, which authors outperform with a relative improvement of over 20%.</div>Audreyhttps://wikipediaquality.com/index.php?title=Motivations_of_Contributors_to_Wikipedia&diff=23971Motivations of Contributors to Wikipedia2020-05-06T05:28:07Z<p>Audrey: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Motivations of Contributors to Wikipedia<br />
| date = 2006<br />
| authors = [[Stacey Kuznetsov]]<br />
| doi = 10.1145/1215942.1215943<br />
| link = http://dl.acm.org/citation.cfm?id=1215943<br />
}}<br />
'''Motivations of Contributors to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Stacey Kuznetsov]].<br />
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== Overview ==<br />
This paper aims to explain why people are motivated to contribute to the [[Wikipedia]] project. A comprehensive analysis of the motivations of [[Wikipedians]] is conducted using the iterative methodology developed by Batya Friedman and Peter Kahn in Value Sensitive Design and Information Systems and co-developed by Nissenbaum and Friedman in Bias in Computer Systems . The Value Sensitive Design (VSD) approach consists of three stages: Empirical Investigation, Conceptual Investigation, and Technical Investigation. During the empirical phase, motivations of the contributors to Wikipedia are identified through analysis of data from two published surveys and a pilot survey conducted at New York University. The underlying values behind these motivations are then defined in the conceptual phase of the study. Finally, a technical investigation is conducted in order to determine how [[features]] of the Wiki technology support and facilitate these values.</div>Audreyhttps://wikipediaquality.com/index.php?title=Context-Based_Disambiguation_Using_Wikipedia&diff=23970Context-Based Disambiguation Using Wikipedia2020-05-06T05:26:01Z<p>Audrey: Wikilinks</p>
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<div>'''Context-Based Disambiguation Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Hugo Batista]], [[David Carrega]], [[Rui S. V. Rodrigues]] and [[Joaquim Filipe]].<br />
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== Overview ==<br />
This paper addresses the problem of semantic ambiguity, identified in a previous work where authors presented</div>Audreyhttps://wikipediaquality.com/index.php?title=Revisiting_Reverts:_Accurate_Revert_Detection_in_Wikipedia&diff=23969Revisiting Reverts: Accurate Revert Detection in Wikipedia2020-05-06T05:24:05Z<p>Audrey: Infobox work</p>
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<div>{{Infobox work<br />
| title = Revisiting Reverts: Accurate Revert Detection in Wikipedia<br />
| date = 2012<br />
| authors = [[Fabian Flöck]]<br />[[Denny Vrandecic]]<br />[[Elena Simperl]]<br />
| doi = 10.1145/2309996.2310000<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=2310000&amp;type=pdf<br />
}}<br />
'''Revisiting Reverts: Accurate Revert Detection in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Fabian Flöck]], [[Denny Vrandecic]] and [[Elena Simperl]].<br />
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== Overview ==<br />
Wikipedia is commonly used as a proving ground for research in collaborative systems. This is likely due to its popularity and scale, but also to the fact that large amounts of data about its formation and evolution are freely available to inform and validate theories and models of online collaboration. As part of the development of such approaches, revert detection is often performed as an important pre-processing step in tasks as diverse as the extraction of implicit networks of editors, the analysis of edit or editor [[features]] and the removal of noise when analyzing the emergence of the content of an article. The current state of the art in revert detection is based on a rather naive approach, which identifies revision duplicates based on MD5 hash values. This is an efficient, but not very precise technique that forms the basis for the majority of research based on revert relations in [[Wikipedia]]. In this paper authors prove that this method has a number of important drawbacks - it only detects a limited number of reverts, while simultaneously misclassifying too many edits as reverts, and not distinguishing between complete and partial reverts. This is very likely to hamper the accurate interpretation of the findings of revert-related research. Authors introduce an improved algorithm for the detection of reverts based on word tokens added or deleted to adresses these drawbacks. Authors report on the results of a user study and other tests demonstrating the considerable gains in accuracy and coverage by method, and argue for a positive trade-off, in certain research scenarios, between these improvements and algorithm's increased runtime.</div>Audreyhttps://wikipediaquality.com/index.php?title=A_Simple_Scheme_for_Book_Classification_Using_Wikipedia&diff=23968A Simple Scheme for Book Classification Using Wikipedia2020-05-06T05:22:39Z<p>Audrey: infobox</p>
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<div>{{Infobox work<br />
| title = A Simple Scheme for Book Classification Using Wikipedia<br />
| date = 2011<br />
| authors = [[Andromeda Yelton]]<br />
| doi = 10.6017/ital.v30i1.3040<br />
| link = http://ejournals.bc.edu/ojs/index.php/ital/article/view/3040<br />
}}<br />
'''A Simple Scheme for Book Classification Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Andromeda Yelton]].<br />
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== Overview ==<br />
Because the rate at which documents are being generated outstrips librarians’ ability to catalog them, an accurate, automated scheme of subject classification is desirable. However, simplistic word-counting schemes miss many important concepts; librarians must enrich algorithms with background knowledge to escape basic problems such as polysemy and synonymy. Author have developed a script that uses [[Wikipedia]] as context for analyzing the subjects of nonfiction books. Though a simple method built quickly from freely available parts, it is partially successful, suggesting the promise of such an approach for future research.</div>Audreyhttps://wikipediaquality.com/index.php?title=Updating_Wikipedia_via_Dbpedia_Mappings_and_Sparql&diff=23967Updating Wikipedia via Dbpedia Mappings and Sparql2020-05-06T05:21:26Z<p>Audrey: Int.links</p>
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<div>'''Updating Wikipedia via Dbpedia Mappings and Sparql''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Albin Ahmeti]], [[Javier D. Fernández]], [[Axel Polleres]] and [[Vadim Savenkov]].<br />
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== Overview ==<br />
DBpedia crystallized most of the concepts of the Semantic Web using simple mappings to convert [[Wikipedia]] articles (i.e., [[infoboxes]] and tables) to RDF data. This “semantic view” of wiki content has rapidly become the focal point of the Linked Open Data cloud, but its impact on the original Wikipedia source is limited. In particular, little attention has been paid to the benefits that the semantic infrastructure can bring to maintain the wiki content, for instance to ensure that the effects of a wiki edit are consistent across infoboxes. In this paper, authors present an approach to allow [[ontology]]-based updates of wiki content. Starting from [[DBpedia]]-like mappings converting infoboxes to a fragment of OWL 2 RL ontology, authors discuss various issues associated with translating SPARQL updates on top of semantic data to the underlying Wiki content. On the one hand, authors provide a formalization of DBpedia as an Ontology-Based Data Management framework and study its computational properties. On the other hand, authors provide a novel approach to the inherently intractable update translation problem, leveraging the pre-existent data for disambiguating updates.</div>Audreyhttps://wikipediaquality.com/index.php?title=Wikipedia_and_the_Ecosystem_of_Knowledge&diff=23966Wikipedia and the Ecosystem of Knowledge2020-05-06T05:20:02Z<p>Audrey: Creating a new page - Wikipedia and the Ecosystem of Knowledge</p>
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<div>'''Wikipedia and the Ecosystem of Knowledge''' - scientific work related to Wikipedia quality published in 2015, written by Christian Vandendorpe.<br />
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== Overview ==<br />
Normal Wikipedia as 0 a course assignment and make sure that 21 the topics related to false their false discipline are fairly false presented in this encyclopedia. FR JA X-NONE Thanks to a vibrant community united by a few core principles, plus detailed policies and safeguards against trolls and vandalism, Wikipedia has already become a piece of the knowledge ecosystem. Like science, its aim is to propose a synthesis of existing knowledge and conflicting interpretations of reality. It also changes the way people interact with knowledge thanks to its extensive use of hyperlinks, portals, and categories. As a consequence, Author suggest academics contribute to articles in their field. They could also use</div>Audreyhttps://wikipediaquality.com/index.php?title=Utilizing_Wikipedia_in_Categorizing_Topic_Related_Blogs_into_Facets&diff=23965Utilizing Wikipedia in Categorizing Topic Related Blogs into Facets2020-05-06T05:17:59Z<p>Audrey: + infobox</p>
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<div>{{Infobox work<br />
| title = Utilizing Wikipedia in Categorizing Topic Related Blogs into Facets<br />
| date = 2011<br />
| authors = [[Daisuke Yokomoto]]<br />[[Kensaku Makita]]<br />[[Takehito Utsuro]]<br />[[Yasuhide Kawada]]<br />[[Tomohiro Fukuhara]]<br />
| doi = 10.1016/j.sbspro.2011.10.595<br />
| link = http://www.sciencedirect.com/science/article/pii/S1877042811024220<br />
}}<br />
'''Utilizing Wikipedia in Categorizing Topic Related Blogs into Facets''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Daisuke Yokomoto]], [[Kensaku Makita]], [[Takehito Utsuro]], [[Yasuhide Kawada]] and [[Tomohiro Fukuhara]].<br />
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== Overview ==<br />
Abstract Given a search query, most existing search engines simply return a ranked list of search results. However, it is often the case that those search result documents consist of a mixture of documents that are closely related to various sub- topics. This paper proposes a framework of categorizing blog posts according to their sub-topics. In framework, the sub-topic of each blog post is identified by utilizing [[Wikipedia]] entries as a knowledge source and each Wikipedia entry title is considered as a sub-topic label. Authors achieve to quickly overview the distribution of sub-topics over the whole collected blog posts.</div>Audreyhttps://wikipediaquality.com/index.php?title=Result_Diversity_and_Entity_Ranking_Experiments:_Anchors,_Links,_Text_and_Wikipedia&diff=23964Result Diversity and Entity Ranking Experiments: Anchors, Links, Text and Wikipedia2020-05-06T05:15:38Z<p>Audrey: infobox</p>
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<div>{{Infobox work<br />
| title = Result Diversity and Entity Ranking Experiments: Anchors, Links, Text and Wikipedia<br />
| date = 2010<br />
| authors = [[Rianne Kaptein]]<br />[[Marijn Koolen]]<br />[[Jaap Kamps]]<br />
| link = http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA517853&amp;Location=U2&amp;doc=GetTRDoc.pdf<br />
}}<br />
'''Result Diversity and Entity Ranking Experiments: Anchors, Links, Text and Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Rianne Kaptein]], [[Marijn Koolen]] and [[Jaap Kamps]].<br />
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== Overview ==<br />
In this paper, authors document efforts in participating to the TREC 2009 Entity Ranking and Web Tracks. Authors had multiple aims: For the Web Track’s Adhoc task authors experiment with document text and anchor text representation, and the use of the link structure. For the Web Track’s Diversity task authors experiment with using a top down sliding window that, given the top ranked documents, chooses as the next ranked document the one that has the most unique terms or links. Authors test sliding window method on a standard document text index and an index of propagated anchor texts. Authors also experiment with extreme query expansions by taking the top n results of the initial ranking as multi-faceted aspects of the topic to construct n relevance models to obtain n sets of results. A final diverse set of results is obtained by merging the n results lists. For the Entity Ranking Track, authors also explore the effectiveness of the anchor text representation, look at the co-citation graph, and experiment with using [[Wikipedia]] as a pivot. Authors main findings can be summarized as follows: Anchor text is very effective for diversity. It gives high early precision and the results cover more relevant sub-topics than the document text index. Authors baseline runs have low diversity, which limits the possible impact of the sliding window approach. New link information seems more effective for diversifying text-based search results than the amount of unique terms added by a document. In the entity ranking task, anchor text finds few primary pages , but it does retrieve a large number of relevant pages. Using Wikipedia as a pivot results in large gains of P10 and NDCG when only primary pages are considered. Although the links between the Wikipedia entities and pages in the Clueweb collection are sparse, the precision of the existing links is very high.</div>Audreyhttps://wikipediaquality.com/index.php?title=Battle_over_Media_Choice:_Multiplex_Tensions_in_the_Online_Community_of_Wikipedia&diff=23963Battle over Media Choice: Multiplex Tensions in the Online Community of Wikipedia2020-05-06T05:14:02Z<p>Audrey: Starting a page: Battle over Media Choice: Multiplex Tensions in the Online Community of Wikipedia</p>
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<div>'''Battle over Media Choice: Multiplex Tensions in the Online Community of Wikipedia''' - scientific work related to Wikipedia quality published in 2015, written by Arto Lanamäki, Netta Iivari, Mikko Rajanen and Henrik Hedberg.<br />
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== Overview ==<br />
Media choice theories conceptualize decisions people make when they are faced with communication media alternatives to fit a communicative need. In this paper authors address two gaps in extant research on media choice. First, authors show that media choices may be intimately intertwined with the questions of power. The second contribution comes from situating an online community as the focus of media choice research – a novel combination. Authors conducted an interpretive case study on how power is intermingled with the choice of Internet Relay Chat (IRC) in the Finnish Wikipedia. Authors found that IRC was viewed in starkly different ways by different actors. Moreover, the IRC was largely associated with the notions of power. In particular, it was related to accession and ability to influence decisionmaking in the community. One party perceived IRC as a useful and open channel for quick-tempo collaborations and informal interactions, while others saw it as an arena for “the elite” to scheme against “the proletariat”. Overall, IRC was a source of “multiplex tensions”: conflicts originating from communication being dispersed into multiple media and from different perceptions towards a medium. The study provides several important implications for theory and practice.</div>Audreyhttps://wikipediaquality.com/index.php?title=Content_Volatility_of_Scientific_Topics_in_Wikipedia:_a_Cautionary_Tale&diff=23962Content Volatility of Scientific Topics in Wikipedia: a Cautionary Tale2020-05-06T05:12:31Z<p>Audrey: + category</p>
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<div>{{Infobox work<br />
| title = Content Volatility of Scientific Topics in Wikipedia: a Cautionary Tale<br />
| date = 2015<br />
| authors = [[Adam M. Wilson]]<br />[[Adam M. Wilson]]<br />[[Gene E. Likens]]<br />
| doi = 10.1371/journal.pone.0134454<br />
| link = https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537301/<br />
}}<br />
'''Content Volatility of Scientific Topics in Wikipedia: a Cautionary Tale''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Adam M. Wilson]], [[Adam M. Wilson]] and [[Gene E. Likens]].<br />
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== Overview ==<br />
Wikipedia has quickly become one of the most frequently accessed encyclopedic references, despite the ease with which content can be changed and the potential for ‘edit wars’ surrounding controversial topics. Little is known about how this potential for controversy affects the accuracy and stability of information on scientific topics, especially those with associated political controversy. Here authors present an analysis of the [[Wikipedia]] edit histories for seven scientific articles and show that topics authors consider politically but not scientifically “controversial” (such as evolution and global warming) experience more frequent edits with more words changed per day than pages authors consider “noncontroversial” (such as the standard model in physics or heliocentrism). For example, over the period authors analyzed, the global warming page was edited on average (geometric mean ±SD) 1.9±2.7 times resulting in 110.9±10.3 words changed per day, while the standard model in physics was only edited 0.2±1.4 times resulting in 9.4±5.0 words changed per day. The high rate of change observed in these pages makes it difficult for experts to monitor accuracy and contribute time-consuming corrections, to the possible detriment of scientific accuracy. As society turns to Wikipedia as a primary source of scientific information, it is vital authors read it critically and with the understanding that the content is dynamic and vulnerable to vandalism and other shenanigans.<br />
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Wilson, Adam M.; Wilson, Adam M.; Likens, Gene E.. (2015). "[[Content Volatility of Scientific Topics in Wikipedia: a Cautionary Tale]]". Public Library of Science. DOI: 10.1371/journal.pone.0134454. <br />
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{{cite journal |last1=Wilson |first1=Adam M. |last2=Wilson |first2=Adam M. |last3=Likens |first3=Gene E. |title=Content Volatility of Scientific Topics in Wikipedia: a Cautionary Tale |date=2015 |doi=10.1371/journal.pone.0134454 |url=https://wikipediaquality.com/wiki/Content_Volatility_of_Scientific_Topics_in_Wikipedia:_a_Cautionary_Tale |journal=Public Library of Science}}<br />
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Wilson, Adam M.; Wilson, Adam M.; Likens, Gene E.. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Content_Volatility_of_Scientific_Topics_in_Wikipedia:_a_Cautionary_Tale">Content Volatility of Scientific Topics in Wikipedia: a Cautionary Tale</a>&amp;quot;. Public Library of Science. DOI: 10.1371/journal.pone.0134454. <br />
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[[Category:Scientific works]]</div>Audreyhttps://wikipediaquality.com/index.php?title=Wikipedia_Chemical_Structure_Explorer:_Substructure_and_Similarity_Searching_of_Molecules_from_Wikipedia&diff=23961Wikipedia Chemical Structure Explorer: Substructure and Similarity Searching of Molecules from Wikipedia2020-05-06T05:10:56Z<p>Audrey: Wikipedia Chemical Structure Explorer: Substructure and Similarity Searching of Molecules from Wikipedia - basic info</p>
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<div>'''Wikipedia Chemical Structure Explorer: Substructure and Similarity Searching of Molecules from Wikipedia''' - scientific work related to Wikipedia quality published in 2015, written by Peter Ertl, Luc Patiny, Thomas Sander, Christian Rufener and Michaël Zasso.<br />
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== Overview ==<br />
Background</div>Audreyhttps://wikipediaquality.com/index.php?title=Wikipedia-Based_Semantic_Similarity_Measurements_for_Noisy_Short_Texts_Using_Extended_Naive_Bayes&diff=23960Wikipedia-Based Semantic Similarity Measurements for Noisy Short Texts Using Extended Naive Bayes2020-05-06T05:07:57Z<p>Audrey: + embed code</p>
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<div>{{Infobox work<br />
| title = Wikipedia-Based Semantic Similarity Measurements for Noisy Short Texts Using Extended Naive Bayes<br />
| date = 2015<br />
| authors = [[Masumi Shirakawa]]<br />[[Kotaro Nakayama]]<br />[[Takahiro Hara]]<br />[[Shojiro Nishio]]<br />
| doi = 10.1109/TETC.2015.2418716<br />
| link = http://ieeexplore.ieee.org/document/7073627/<br />
}}<br />
'''Wikipedia-Based Semantic Similarity Measurements for Noisy Short Texts Using Extended Naive Bayes''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Masumi Shirakawa]], [[Kotaro Nakayama]], [[Takahiro Hara]] and [[Shojiro Nishio]].<br />
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== Overview ==<br />
This paper proposes a [[Wikipedia]]-based [[semantic similarity]] measurement method that is intended for real-world noisy short texts. Authors method is a kind of explicit semantic analysis (ESA), which adds a bag of Wikipedia entities (Wikipedia pages) to a text as its semantic representation and uses the vector of entities for computing the semantic similarity. Adding related entities to a text, not a single word or phrase, is a challenging practical problem because it usually consists of several subproblems, e.g., key term extraction from texts, related entity finding for each key term, and weight aggregation of related entities. Authors proposed method solves this aggregation problem using extended naive Bayes, a probabilistic weighting mechanism based on the Bayes' theorem. Authors method is effective especially when the short text is semantically noisy, i.e., they contain some meaningless or misleading terms for estimating their main topic. Experimental results on [[Twitter]] message and Web snippet clustering revealed that method outperformed ESA for noisy short texts. Authors also found that reducing the dimension of the vector to representative Wikipedia entities scarcely affected the performance while decreasing the vector size and hence the storage space and the processing time of computing the cosine similarity.<br />
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Shirakawa, Masumi; Nakayama, Kotaro; Hara, Takahiro; Nishio, Shojiro. (2015). "[[Wikipedia-Based Semantic Similarity Measurements for Noisy Short Texts Using Extended Naive Bayes]]".DOI: 10.1109/TETC.2015.2418716. <br />
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{{cite journal |last1=Shirakawa |first1=Masumi |last2=Nakayama |first2=Kotaro |last3=Hara |first3=Takahiro |last4=Nishio |first4=Shojiro |title=Wikipedia-Based Semantic Similarity Measurements for Noisy Short Texts Using Extended Naive Bayes |date=2015 |doi=10.1109/TETC.2015.2418716 |url=https://wikipediaquality.com/wiki/Wikipedia-Based_Semantic_Similarity_Measurements_for_Noisy_Short_Texts_Using_Extended_Naive_Bayes}}<br />
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Shirakawa, Masumi; Nakayama, Kotaro; Hara, Takahiro; Nishio, Shojiro. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia-Based_Semantic_Similarity_Measurements_for_Noisy_Short_Texts_Using_Extended_Naive_Bayes">Wikipedia-Based Semantic Similarity Measurements for Noisy Short Texts Using Extended Naive Bayes</a>&amp;quot;.DOI: 10.1109/TETC.2015.2418716. <br />
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</code></div>Audreyhttps://wikipediaquality.com/index.php?title=Specifying_a_Wikipedia-Centric_Explanatory_Model_for_Online_Group_Evolution_and_Structural_Differentiation&diff=23959Specifying a Wikipedia-Centric Explanatory Model for Online Group Evolution and Structural Differentiation2020-05-06T05:05:01Z<p>Audrey: Links</p>
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<div>'''Specifying a Wikipedia-Centric Explanatory Model for Online Group Evolution and Structural Differentiation''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Sorin Adam Matei]] and [[Brian C. Britt]].<br />
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== Overview ==<br />
In the previous chapter, authors set up the broader sociological underpinnings of argument. This chapter will focus on the more immediate and tangible mechanisms that shape the emergence and evolution of social media groups, such as those that build [[Wikipedia]]. Authors theoretical argument will devote particular attention to the role played by contributing elites in organizing and sustaining collaborative activity.</div>Audreyhttps://wikipediaquality.com/index.php?title=Title_Named_Entity_Recognition_Using_Wikipedia_and_Abbreviation_Generation&diff=23958Title Named Entity Recognition Using Wikipedia and Abbreviation Generation2020-05-06T05:03:09Z<p>Audrey: + embed code</p>
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<div>{{Infobox work<br />
| title = Title Named Entity Recognition Using Wikipedia and Abbreviation Generation<br />
| date = 2014<br />
| authors = [[Youngmin Park]]<br />[[Sangwoo Kang]]<br />[[Jungyun Seo]]<br />
| doi = 10.1109/BIGCOMP.2014.6741430<br />
| link = https://www.computer.org/web/csdl/index/-/csdl/proceedings/bigcomp/2014/3919/00/06741430-abs.html<br />
}}<br />
'''Title Named Entity Recognition Using Wikipedia and Abbreviation Generation''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Youngmin Park]], [[Sangwoo Kang]] and [[Jungyun Seo]].<br />
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== Overview ==<br />
In this paper, authors propose a title [[named entity recognition]] model using [[Wikipedia]] and abbreviation generation. The proposed title [[named entity]] recognition model automatically extracts title [[named entities]] from Wikipedia so constant renewal is possible without additional costs. Also, in order to establish a dictionary of title named entity abbreviations, generation rules are used to generate abbreviation candidates and abbreviations are selected through web search methods. In this paper, authors propose a statistical model that recognizes title named entities using CRFs (Conditional Random Fields). The proposed model uses lexical information, a named entity dictionary, and an abbreviation dictionary, and provides title named [[entity recognition]] performance of 82.1% according to experimental results.<br />
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Park, Youngmin; Kang, Sangwoo; Seo, Jungyun. (2014). "[[Title Named Entity Recognition Using Wikipedia and Abbreviation Generation]]".DOI: 10.1109/BIGCOMP.2014.6741430. <br />
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{{cite journal |last1=Park |first1=Youngmin |last2=Kang |first2=Sangwoo |last3=Seo |first3=Jungyun |title=Title Named Entity Recognition Using Wikipedia and Abbreviation Generation |date=2014 |doi=10.1109/BIGCOMP.2014.6741430 |url=https://wikipediaquality.com/wiki/Title_Named_Entity_Recognition_Using_Wikipedia_and_Abbreviation_Generation}}<br />
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Park, Youngmin; Kang, Sangwoo; Seo, Jungyun. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Title_Named_Entity_Recognition_Using_Wikipedia_and_Abbreviation_Generation">Title Named Entity Recognition Using Wikipedia and Abbreviation Generation</a>&amp;quot;.DOI: 10.1109/BIGCOMP.2014.6741430. <br />
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</code></div>Audreyhttps://wikipediaquality.com/index.php?title=Modeling_New_and_Old_Editors%E2%80%99_Behaviors_in_Different_Languages_of_Wikipedia&diff=23957Modeling New and Old Editors’ Behaviors in Different Languages of Wikipedia2020-05-06T05:00:39Z<p>Audrey: Infobox work</p>
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<div>{{Infobox work<br />
| title = Modeling New and Old Editors’ Behaviors in Different Languages of Wikipedia<br />
| date = 2018<br />
| authors = [[Anita Chandra]]<br />[[Abyayananda Maiti]]<br />
| doi = 10.1007/978-3-030-02925-8_31<br />
| link = https://link.springer.com/openurl?id=doi:10.1007/978-3-030-02925-8_31<br />
}}<br />
'''Modeling New and Old Editors’ Behaviors in Different Languages of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Anita Chandra]] and [[Abyayananda Maiti]].<br />
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== Overview ==<br />
Wikipedia is an [[open-source]] [[multilingual]] encyclopedia which allows users to edit, create and share their knowledge collaboratively. Size of its contents such as articles, editors, links and language editions grows too fast with time. In this paper, authors model the growth of editor-article bipartite network of multilingual [[Wikipedia]]s to investigate behaviors of editors. In this bipartite network, editors and articles are two disjoint sets and if an editor edits an article then it forms an edge between them. The both editors and articles arrive simultaneously into their respective sets and editing is done by editors. The Wiki networks grow by the creation of external edits performed by new editors and/or internal edits done by old editors. These edits are done with a combination of preferential and/or random attachment mechanism. Authors consider two different randomness parameters for new and old editors in their attachment procedures. Authors validate the growth model over 20 largest language editions of Wikipedia and results show good agreement between model and each of the considered languages. After interpreting the values of parameters authors notice contrast in editing behaviors of new and old editors in every language. Authors also notice this non-uniform behavior of editors varies across all the languages. Thus, authors report uncommon growth processes and difference in editing behaviors of editors of [[different language]]s of Wikipedia.</div>Audreyhttps://wikipediaquality.com/index.php?title=Credibility_Assessment_Using_Wikipedia_for_Messages_on_Social_Network_Services&diff=23956Credibility Assessment Using Wikipedia for Messages on Social Network Services2020-05-06T04:58:13Z<p>Audrey: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Credibility Assessment Using Wikipedia for Messages on Social Network Services<br />
| date = 2011<br />
| authors = [[Yu Suzuki]]<br />[[Akiyo Nadamoto]]<br />
| doi = 10.1109/DASC.2011.149<br />
| link = http://dl.acm.org/citation.cfm?id=2115599<br />
}}<br />
'''Credibility Assessment Using Wikipedia for Messages on Social Network Services''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Yu Suzuki]] and [[Akiyo Nadamoto]].<br />
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== Overview ==<br />
Authors propose methods for calculating [[credibility]] values of messages in Social Network Services (SNSs), such as Linked In and Face book. Many users post messages on SNSs, however, not all of these messages are credible. Authors method is based on two assumptions: an SNS message is credible (1) if the SNS message is similar to information from other resources and (2) if the information is confirmed as credible. For assumption (1), authors developed a method to retrieve similar descriptions from [[Wikipedia]] articles. For assumption (2), authors developed a method for assessing Wikipedia articles using the edit history. Using these two methods, authors can calculate accurate credibility values for SNS messages. In an experiment, authors confirmed that method can calculate appropriate credibility values for SNS messages if Wikipedia has credible articles related to the SNS messages.</div>Audreyhttps://wikipediaquality.com/index.php?title=Bridging_the_Gap_Between_Wikipedia_and_Academia&diff=23740Bridging the Gap Between Wikipedia and Academia2020-03-18T09:14:41Z<p>Audrey: Links</p>
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<div>'''Bridging the Gap Between Wikipedia and Academia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Dariusz Jemielniak]] and [[Eduard Aibar]].<br />
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== Overview ==<br />
In this opinion piece, authors would like to present a short literature review of perceptions and reservations towards [[Wikipedia]] in academia, address the common questions about overall [[reliability]] of Wikipedia entries, review the actual practices of Wikipedia usage in academia, and conclude with possible scenarios for a peaceful coexistence. Because Wikipedia is a regular topic of JASIST publications Lim, 2009; Meseguer-Artola, Aibar, Llados, Minguillon, & Lerga, ; Mesgari, Okoli, Mehdi, Nielsen, & Lanamaki, ; Okoli, Mehdi, Mesgari, Nielsen, & Lanamaki, , authors hope to start a useful discussion with the right audience.</div>Audreyhttps://wikipediaquality.com/index.php?title=A_Combined_Approach_to_Minimizing_Vandalisms_on_Wikipedia&diff=23739A Combined Approach to Minimizing Vandalisms on Wikipedia2020-03-18T09:11:46Z<p>Audrey: Adding infobox</p>
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<div>{{Infobox work<br />
| title = A Combined Approach to Minimizing Vandalisms on Wikipedia<br />
| date = 2010<br />
| authors = [[Songrit Maneewongvatana]]<br />[[Suthathip Maneewongvatana]]<br />
| doi = 10.1109/ISCIT.2010.5664872<br />
| link = http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000005664872<br />
}}<br />
'''A Combined Approach to Minimizing Vandalisms on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Songrit Maneewongvatana]] and [[Suthathip Maneewongvatana]].<br />
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== Overview ==<br />
Vandalism is a main problem of the [[Wikipedia]] and other content management systems that have open-edit policy. Due to its popularity, Wikipedia has been attacked very regularly. To detect and clean up these bad contents, it requires a lot of human time to inspect the changes between revisions which should not be much wasted for such task. There have been many attempts to alleviate vandalisms. Some early work proposed automatic vandalism detection based on various methods. In this work, authors emphasize the user is a key parameter used to detect vandalism. Together with [[reputation]] system and spell checker, they can be used to flag or ban the update. Authors also evaluate the reduction of the time at which vandalized revisions appear to public when authors implement some of the proposed approach.</div>Audreyhttps://wikipediaquality.com/index.php?title=This_is_the_First_Citation,_and_as_Such_Reference_to_Proof,_Used_in_a_Wiki*_Site_I%27Ve_Ever_Seen_Which_Links_Back_to_Wikipedia.Https://De.Wiktionary.Org/Wiki/Mannigfaltigkeit&diff=23738This is the First Citation, and as Such Reference to Proof, Used in a Wiki* Site I'Ve Ever Seen Which Links Back to Wikipedia.Https://De.Wiktionary.Org/Wiki/Mannigfaltigkeit2020-03-18T09:10:14Z<p>Audrey: + wikilinks</p>
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<div>'''This is the First Citation, and as Such Reference to Proof, Used in a Wiki* Site I'Ve Ever Seen Which Links Back to Wikipedia.Https://De.Wiktionary.Org/Wiki/Mannigfaltigkeit#Cite_Note-2Yes!''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[jon richter]].<br />
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== Overview ==<br />
This is the first citation, and as such reference to proof, used in a Wiki* site I've ever seen which links back to [[Wikipedia]].</div>Audreyhttps://wikipediaquality.com/index.php?title=Wikipedia-Type_Disambiguation_Functionality_in_Lcsh:_a_Recommendation&diff=23737Wikipedia-Type Disambiguation Functionality in Lcsh: a Recommendation2020-03-18T09:08:38Z<p>Audrey: Wikilinks</p>
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<div>'''Wikipedia-Type Disambiguation Functionality in Lcsh: a Recommendation''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Daniel CannCasciato]].<br />
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== Overview ==<br />
SalsaIf a patron speaks or types the word "Salsa", without further qualification, then a confident definition of the term cannot be achieved. Salsa might be a dance, a musical style, or sauce of Spanish, Italian or Latin American origin. In other words, without some clarification, one is left with ambiguity. The process of clarification is generally called disambiguation. In the work of cataloging, especially subject analysis, disambiguation plays a prominent role in the establishment of terms in the Library of Congress Subject Headings authority file (LCSH).Disambiguation by the use of qualifiers or other modifications is commonly used when a term can have multiple senses: the salsa example is one type. In LCSH there are three headings:Salsa (Dance)Salsa (Music)Salsas (Cooking); this has a see reference from of Salsa (Cooking)Additionally, the reason many LCSH terms have qualifiers (e.g.: Ground reaction force (Biomechanics)) is to clarify the context for the term and to disambiguate (clarify in context) its meaning. In the example given, a ground reaction force might be some type of Special Forces military group. That there is no existing heading for such a group or practice at this time does not eliminate the need for the qualifier; it is added just the same. (This practice is covered specifically in the Subject Heading Manual instruction sheet H357.)However, neither of these examples achieves what a utilization of a clearer disambiguation practice would. The purpose of this paper is to recommend that when RDA tackles subject access, it institute a practice of providing [[Wikipedia]]-type disambiguation see-references of all ambiguous terms, rather than the somewhat inconsistent or non-existent practices currently in evidence in LCSH. Further, Author propose that it is already possible for LCSH policies to be changed to implement such a practice and that such a change happen. Using Salsa again as the example, a Wikipedia-type disambiguation practice would make a reference presentation to patrons of:Salsa1-> See Salsa (Dance)2-> See Salsa (Music)3 -> See Salsas (Cooking)at the top of a results page to a search query. Enabling and supporting such a presentation of choices and guidance to patrons assists them, and supports traditional and new cataloging objectives.CATALOGING'S OBJECTIVESCataloging's objectives have remained steady more or less since Cutter first described them.Resource description and access (RDA) carries them through in some manner. (Cf. RDA 0.2.)To paraphrase Cutter's Objects, the catalog's purpose is:To enable a person to find a book of which the subject is known ... To show what the library has on a given subject, and ... To assist in the choice of a book as to its character (literary or topical)To paraphrase from RDA's purpose and scope:RDA provides a set of guidelines and instructions on formulating data to support resource discovery. The data created using RDA to describe a resource are designed to assist users performing the following tasks: find-i.e., to find resources that correspond to the user's stated search criteria (0.0)... The data should enable the user to: find resources that correspond to the user's stated search criteria ... find all resources on a given subject (0.4.2.1) ... The data should meet functional requirements for the support of user tasks in a cost-efficient manner. (0.4.2.2).Very [[different language]] is used across the years, but the overall intent (perhaps mandate is a better word) is very clear: connect the patron with the information resources available based on the user's stated search criteria. When that stated criteria is vague, or demonstrates a lack of awareness of the vastness of resources potentially available, then it falls to us to assist the user, the patron, to be successful.DISAMBIGUATIONThis issue of ambiguity of terminology (including those due to homonymy and synonymy) has been well noted in [[information retrieval]]. …</div>Audreyhttps://wikipediaquality.com/index.php?title=Expanding_the_Sum_of_All_Human_Knowledge:_Wikipedia,_Translation_and_Linguistic_Justice&diff=23736Expanding the Sum of All Human Knowledge: Wikipedia, Translation and Linguistic Justice2020-03-18T09:07:20Z<p>Audrey: Embed</p>
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<div>{{Infobox work<br />
| title = Expanding the Sum of All Human Knowledge: Wikipedia, Translation and Linguistic Justice<br />
| date = 2017<br />
| authors = [[Julie McDonough Dolmaya]]<br />
| doi = 10.1080/13556509.2017.1321519<br />
| link = http://www.tandfonline.com/doi/abs/10.1080/13556509.2017.1321519<br />
}}<br />
'''Expanding the Sum of All Human Knowledge: Wikipedia, Translation and Linguistic Justice''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Julie McDonough Dolmaya]].<br />
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== Overview ==<br />
ABSTRACTThe [[Wikimedia Foundation]] is arguably the quintessential example of a socially responsible organisation: not-for-profit, dedicated to the growth, development and distribution of free, [[multilingual]] content, the Foundation operates openly editable projects like [[Wikipedia]], which now has anywhere from one to several million articles available in more than 280 languages. [[Wikimedia]] – the organisation that hosts and operates Wikipedia – does not appear to have an explicit translation policy. This paper therefore begins by assessing the organisation’s Language Proposal Policy and Wikipedia’s translation guidelines. Then, drawing on statistics from the Content Translation tool recently developed by Wikipedia to encourage translation within the various [[language versions]], this paper applies the concept of linguistic justice to help determine how any future translation policies might achieve a better balance between fairness and efficiency, arguing that a translation policy can be both fair and efficient, whil...<br />
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Dolmaya, Julie McDonough. (2017). "[[Expanding the Sum of All Human Knowledge: Wikipedia, Translation and Linguistic Justice]]". Routledge. DOI: 10.1080/13556509.2017.1321519. <br />
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{{cite journal |last1=Dolmaya |first1=Julie McDonough |title=Expanding the Sum of All Human Knowledge: Wikipedia, Translation and Linguistic Justice |date=2017 |doi=10.1080/13556509.2017.1321519 |url=https://wikipediaquality.com/wiki/Expanding_the_Sum_of_All_Human_Knowledge:_Wikipedia,_Translation_and_Linguistic_Justice |journal=Routledge}}<br />
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Dolmaya, Julie McDonough. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Expanding_the_Sum_of_All_Human_Knowledge:_Wikipedia,_Translation_and_Linguistic_Justice">Expanding the Sum of All Human Knowledge: Wikipedia, Translation and Linguistic Justice</a>&amp;quot;. Routledge. DOI: 10.1080/13556509.2017.1321519. <br />
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<div>{{Infobox work<br />
| title = Collective Annotation of Wikipedia Entities in Web Text<br />
| date = 2009<br />
| authors = [[Sayali Kulkarni]]<br />[[Amit Singh]]<br />[[Ganesh Ramakrishnan]]<br />[[Soumen Chakrabarti]]<br />
| doi = 10.1145/1557019.1557073<br />
| link = https://dl.acm.org/citation.cfm?id=1557073<br />
}}<br />
'''Collective Annotation of Wikipedia Entities in Web Text''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Sayali Kulkarni]], [[Amit Singh]], [[Ganesh Ramakrishnan]] and [[Soumen Chakrabarti]].<br />
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== Overview ==<br />
To take the first step beyond keyword-based search toward entity-based search, suitable token spans ("spots") on documents must be identified as references to real-world entities from an entity catalog. Several systems have been proposed to link spots on Web pages to entities in [[Wikipedia]]. They are largely based on local compatibility between the text around the spot and textual metadata associated with the entity. Two recent systems exploit inter-label dependencies, but in limited ways. Authors propose a general collective disambiguation approach. Authors premise is that coherent documents refer to entities from one or a few related topics or domains. Authors give formulations for the trade-off between local spot-to-entity compatibility and [[measures]] of global coherence between entities. Optimizing the overall entity assignment is NP-hard. Authors investigate practical solutions based on local hill-climbing, rounding integer linear programs, and pre-clustering entities followed by local optimization within clusters. In experiments involving over a hundred manually-annotated Web pages and tens of thousands of spots, approaches significantly outperform recently-proposed algorithms.<br />
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Kulkarni, Sayali; Singh, Amit; Ramakrishnan, Ganesh; Chakrabarti, Soumen. (2009). "[[Collective Annotation of Wikipedia Entities in Web Text]]".DOI: 10.1145/1557019.1557073. <br />
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{{cite journal |last1=Kulkarni |first1=Sayali |last2=Singh |first2=Amit |last3=Ramakrishnan |first3=Ganesh |last4=Chakrabarti |first4=Soumen |title=Collective Annotation of Wikipedia Entities in Web Text |date=2009 |doi=10.1145/1557019.1557073 |url=https://wikipediaquality.com/wiki/Collective_Annotation_of_Wikipedia_Entities_in_Web_Text}}<br />
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Kulkarni, Sayali; Singh, Amit; Ramakrishnan, Ganesh; Chakrabarti, Soumen. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Collective_Annotation_of_Wikipedia_Entities_in_Web_Text">Collective Annotation of Wikipedia Entities in Web Text</a>&amp;quot;.DOI: 10.1145/1557019.1557073. <br />
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[[Category:Scientific works]]</div>Audreyhttps://wikipediaquality.com/index.php?title=Wikipedia_as_a_Platform_for_Impactful_Learning:_a_New_Course_Model_in_Higher_Education&diff=23734Wikipedia as a Platform for Impactful Learning: a New Course Model in Higher Education2020-03-18T09:03:28Z<p>Audrey: + Embed</p>
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| title = Wikipedia as a Platform for Impactful Learning: a New Course Model in Higher Education<br />
| date = 2017<br />
| authors = [[Shani Evenstein Sigalov]]<br />[[Rafi Nachmias]]<br />
| doi = 10.1007/s10639-016-9564-z<br />
| link = https://link.springer.com/content/pdf/10.1007%2Fs10639-016-9564-z.pdf<br />
}}<br />
'''Wikipedia as a Platform for Impactful Learning: a New Course Model in Higher Education''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Shani Evenstein Sigalov]] and [[Rafi Nachmias]].<br />
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== Overview ==<br />
In January 2014, 62 students graduated from the first for-credit course dedicated to [[Wikipedia]]. Learning focused on improved consumption of information, and collaborative construction of knowledge using the Wikipedia platform. This paper investigates the design and implementation of this course model, while highlighting the benefits and challenges to students & faculty. In addition to 128 medical articles in Hebrew Wikipedia, already viewed over 1.4 million times, students reported a unique learning experience that sharpened their collaborative skills as well as academic skills. This paper also presents the findings of a related study that focused on students’ learning experience, long-term impact and productive teaching practices. The study results helped fine-tune the pedagogical and administrative aspects of the course, influencing both teaching practices and the learning experience. Finally, the course is discussed in a wider educational perspective, presenting insights regarding reuse of the model, scaling possibilities and suggestions for further research.<br />
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Sigalov, Shani Evenstein; Nachmias, Rafi. (2017). "[[Wikipedia as a Platform for Impactful Learning: a New Course Model in Higher Education]]". Springer US. DOI: 10.1007/s10639-016-9564-z. <br />
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{{cite journal |last1=Sigalov |first1=Shani Evenstein |last2=Nachmias |first2=Rafi |title=Wikipedia as a Platform for Impactful Learning: a New Course Model in Higher Education |date=2017 |doi=10.1007/s10639-016-9564-z |url=https://wikipediaquality.com/wiki/Wikipedia_as_a_Platform_for_Impactful_Learning:_a_New_Course_Model_in_Higher_Education |journal=Springer US}}<br />
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Sigalov, Shani Evenstein; Nachmias, Rafi. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_as_a_Platform_for_Impactful_Learning:_a_New_Course_Model_in_Higher_Education">Wikipedia as a Platform for Impactful Learning: a New Course Model in Higher Education</a>&amp;quot;. Springer US. DOI: 10.1007/s10639-016-9564-z. <br />
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<div>{{Infobox work<br />
| title = The Wikipedia Adventure: Evaluating and Interactive Tutorial for Newcomers<br />
| date = 2017<br />
| authors = [[Sneha Narayan]]<br />
| doi = 10.6084/m9.figshare.5339425.v3<br />
| link = https://search.datacite.org/works/10.6084/m9.figshare.5339425.v3<br />
}}<br />
'''The Wikipedia Adventure: Evaluating and Interactive Tutorial for Newcomers''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Sneha Narayan]].<br />
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== Overview ==<br />
Integrating new users into a community with complex norms presents a challenge for peer production projects like [[Wikipedia]]. Authors present The Wikipedia Adventure (TWA): an interactive tutorial that offers a structured and gamified introduction to Wikipedia. In addition to describing the design of the system, authors present two empirical evaluations. First, authors report on a survey of users, who responded very positively to the tutorial. Second, authors report results from a large-scale invitation-based field experiment that tests whether using TWA increased newcomers' subsequent contributions to Wikipedia. Authors find no effect of either using the tutorial or of being invited to do so over a period of 180 days. Authors conclude that TWA produces a positive socialization experience for those who choose to use it, but that it does not alter patterns of newcomer activity. Authors reflect on the implications of these mixed results for the evaluation of similar social computing systems.</div>Audreyhttps://wikipediaquality.com/index.php?title=Filling_the_Gaps:_Improving_Wikipedia_Stubs&diff=20958Filling the Gaps: Improving Wikipedia Stubs2019-10-05T19:30:21Z<p>Audrey: infobox</p>
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| title = Filling the Gaps: Improving Wikipedia Stubs<br />
| date = 2015<br />
| authors = [[Siddhartha Banerjee]]<br />[[Prasenjit Mitra]]<br />
| doi = 10.1145/2682571.2797073<br />
| link = http://dl.acm.org/citation.cfm?id=2797073<br />
}}<br />
'''Filling the Gaps: Improving Wikipedia Stubs''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Siddhartha Banerjee]] and [[Prasenjit Mitra]].<br />
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== Overview ==<br />
The availability of only a limited number of contributors on [[Wikipedia]] cannot ensure consistent growth and improvement of the online encyclopedia. With information being scattered on the web, goal is to automate the process of generation of content for Wikipedia. In this work, authors propose a technique of improving stubs on Wikipedia that do not contain comprehensive information. A classifier learns [[features]] from the existing comprehensive articles on Wikipedia and recommends content that can be added to the stubs to improve the [[completeness]] of such stubs. Authors conduct experiments using several classifiers - Latent Dirichlet Allocation (LDA) based model, a deep learning based architecture (Deep belief network) and TFIDF based classifier. Authors experiments reveal that the LDA based model outperforms the other models (~6% F-score). Authors generation approach shows that this technique is capable of generating comprehensive articles. ROUGE-2 scores of the articles generated by system outperform the articles generated using the baseline. Content generated by system has been appended to several stubs and successfully retained in Wikipedia.</div>Audreyhttps://wikipediaquality.com/index.php?title=There_is_No_Deadline:_Time_Evolution_of_Wikipedia_Discussions&diff=20957There is No Deadline: Time Evolution of Wikipedia Discussions2019-10-05T19:28:55Z<p>Audrey: + infobox</p>
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<div>{{Infobox work<br />
| title = There is No Deadline: Time Evolution of Wikipedia Discussions<br />
| date = 2012<br />
| authors = [[Andreas Kaltenbrunner]]<br />[[David Laniado]]<br />
| doi = 10.1145/2462932.2462941<br />
| link = https://dl.acm.org/citation.cfm?id=2462941<br />
| plink = https://arxiv.org/abs/1204.3453<br />
}}<br />
'''There is No Deadline: Time Evolution of Wikipedia Discussions''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Andreas Kaltenbrunner]] and [[David Laniado]].<br />
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== Overview ==<br />
Wikipedia articles are by definition never finished: at any moment their content can be edited, or discussed in the associated [[talk pages]]. In this study authors analyse the evolution of these discussions to unveil patterns of collective participation along the temporal dimension, and to shed light on the process of content creation on different topics. At a micro-scale, authors investigate peaks in the discussion activity and authors observe a non-trivial relationship with edit activity. At a larger scale, authors introduce a measure to account for how fast discussions grow in complexity, and authors find speeds that span three orders of magnitude for different articles. Authors analysis should help the community in tasks such as early detection of controversies and assessment of discussion maturity.</div>Audreyhttps://wikipediaquality.com/index.php?title=Wikipedia%E2%80%99s_Top-Cited_Scholarly_Articles_%E2%80%94_Revealed&diff=20956Wikipedia’s Top-Cited Scholarly Articles — Revealed2019-10-05T19:26:06Z<p>Audrey: + Embed</p>
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| title = Wikipedia’s Top-Cited Scholarly Articles — Revealed<br />
| date = 2018<br />
| authors = [[Giorgia Guglielmi]]<br />
| doi = 10.1038/d41586-018-05161-6<br />
| link = http://www.nature.com/articles/d41586-018-05161-6<br />
}}<br />
'''Wikipedia’s Top-Cited Scholarly Articles — Revealed''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Giorgia Guglielmi]].<br />
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== Overview ==<br />
Gene collections and astronomy studies dominate the list of the most-cited publications with DOIs on the popular online encyclopaedia. Gene collections and astronomy studies dominate the list of the most-cited publications with DOIs on the popular online encyclopaedia.<br />
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{{cite journal |last1=Guglielmi |first1=Giorgia |title=Wikipedia’s Top-Cited Scholarly Articles — Revealed |date=2018 |doi=10.1038/d41586-018-05161-6 |url=https://wikipediaquality.com/wiki/Wikipedia’s_Top-Cited_Scholarly_Articles_—_Revealed |journal=Nature Publishing Group}}<br />
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Guglielmi, Giorgia. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia’s_Top-Cited_Scholarly_Articles_—_Revealed">Wikipedia’s Top-Cited Scholarly Articles — Revealed</a>&amp;quot;. Nature Publishing Group. DOI: 10.1038/d41586-018-05161-6. <br />
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</code></div>Audreyhttps://wikipediaquality.com/index.php?title=Extending_the_Coverage_of_Dbpedia_Properties_Using_Distant_Supervision_over_Wikipedia&diff=20955Extending the Coverage of Dbpedia Properties Using Distant Supervision over Wikipedia2019-10-05T19:23:49Z<p>Audrey: cats.</p>
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<div>{{Infobox work<br />
| title = Extending the Coverage of Dbpedia Properties Using Distant Supervision over Wikipedia<br />
| date = 2013<br />
| authors = [[Alessio Palmero Aprosio]]<br />[[Claudio Giuliano]]<br />[[Alberto Lavelli]]<br />
| link = https://dl.acm.org/citation.cfm?id=2874482<br />
}}<br />
'''Extending the Coverage of Dbpedia Properties Using Distant Supervision over Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Alessio Palmero Aprosio]], [[Claudio Giuliano]] and [[Alberto Lavelli]].<br />
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== Overview ==<br />
DBpedia is a Semantic Web project aiming to extract structured data from [[Wikipedia]] articles. Due to the increasing number of resources linked to it, [[DBpedia]] plays a central role in the Linked Open Data community. Currently, the information contained in DBpedia is mainly collected from Wikipedia [[infoboxes]], a set of subject-attribute-value triples that represents a summary of the Wikipedia page. These infoboxes are manually compiled by the Wikipedia contributors, and in more than 50% of the Wikipedia articles the infobox is missing. In this article, authors use the distant supervision paradigm to extract the missing information directly from the Wikipedia article, using a Relation Extraction tool trained on the information already present in DBpedia. Authors evaluate system on a data set consisting of seven DBpedia properties, demonstrating the suitability of the approach in extending the DBpedia coverage.<br />
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Aprosio, Alessio Palmero; Giuliano, Claudio; Lavelli, Alberto. (2013). "[[Extending the Coverage of Dbpedia Properties Using Distant Supervision over Wikipedia]]". CEUR-WS.org. <br />
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{{cite journal |last1=Aprosio |first1=Alessio Palmero |last2=Giuliano |first2=Claudio |last3=Lavelli |first3=Alberto |title=Extending the Coverage of Dbpedia Properties Using Distant Supervision over Wikipedia |date=2013 |url=https://wikipediaquality.com/wiki/Extending_the_Coverage_of_Dbpedia_Properties_Using_Distant_Supervision_over_Wikipedia |journal=CEUR-WS.org}}<br />
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Aprosio, Alessio Palmero; Giuliano, Claudio; Lavelli, Alberto. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extending_the_Coverage_of_Dbpedia_Properties_Using_Distant_Supervision_over_Wikipedia">Extending the Coverage of Dbpedia Properties Using Distant Supervision over Wikipedia</a>&amp;quot;. CEUR-WS.org. <br />
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[[Category:Scientific works]]</div>Audreyhttps://wikipediaquality.com/index.php?title=Circadian_Patterns_on_Wikipedia_Edits&diff=20954Circadian Patterns on Wikipedia Edits2019-10-05T19:22:10Z<p>Audrey: Cats.</p>
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<div>{{Infobox work<br />
| title = Circadian Patterns on Wikipedia Edits<br />
| date = 2016<br />
| authors = [[]]<br />
| doi = 10.1007/978-3-319-30569-1_22<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-30569-1_22.pdf<br />
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'''Circadian Patterns on Wikipedia Edits''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[]].<br />
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== Overview ==<br />
Cyclic behaviour and circadian patterns emerging from the editing activity of [[Wikipedia]] are hereby considered. Such patterns affect many human activities, mobility routes, energy storage and synchronization, among others. Because the editing of Wikipedia is the result of a voluntary process made by many independent human beings, the question about the signature of such circadian patterns on such data is not straightforward. Authors however show in this work that Wikipedia editing presents well defined periodic patterns with respect to daily, weekly and monthly activity. In addition, authors also show the periodic nature of the number of inter-event in time. The results of work shed some light on the activity scheduling present in society, contributing to the circadian patterns understanding.<br />
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. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Circadian_Patterns_on_Wikipedia_Edits">Circadian Patterns on Wikipedia Edits</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-30569-1_22. <br />
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[[Category:Scientific works]]</div>Audreyhttps://wikipediaquality.com/index.php?title=Wikipedia,_Collective_Authorship,_and_the_Politics_of_Knowledge&diff=20953Wikipedia, Collective Authorship, and the Politics of Knowledge2019-10-05T19:20:39Z<p>Audrey: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Wikipedia, Collective Authorship, and the Politics of Knowledge<br />
| date = 2009<br />
| authors = [[Matthew Rimmer]]<br />
| doi = 10.4337/9781848449039.00016<br />
| link = https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1353606<br />
}}<br />
'''Wikipedia, Collective Authorship, and the Politics of Knowledge''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Matthew Rimmer]].<br />
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== Overview ==<br />
This chapter considers the legal ramifications of [[Wikipedia]], and other online media, such as the Encyclopedia of Life. Nathaniel Tkacz (2007) has observed: 'Wikipedia is an ideal entry-point from which to approach the shifting character of knowledge in contemporary society.' He observes: 'Scholarship on Wikipedia from computer science, history, philosophy, pedagogy and media studies has moved beyond speculation regarding its considerable potential, to the task of interpreting - and potentially intervening in - the significance of Wikipedia's impact' (Tkacz 2007). After an introduction, Part II considers the evolution and development of Wikipedia, and the legal troubles that have attended it. It also considers the establishment of rival online encyclopedia - such as Citizendium set up by Larry Sanger, the co-founder of Wikipedia; and Knol, the mysterious new project of [[Google]]. Part III explores the use of mass, collaborative authorship in the field of science. In particular, it looks at the development of the Encyclopedia of Life, which seeks to document the world's biodiversity.<br />
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Rimmer, Matthew. (2009). "[[Wikipedia, Collective Authorship, and the Politics of Knowledge]]". Edward Elgar Publishing. DOI: 10.4337/9781848449039.00016. <br />
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{{cite journal |last1=Rimmer |first1=Matthew |title=Wikipedia, Collective Authorship, and the Politics of Knowledge |date=2009 |doi=10.4337/9781848449039.00016 |url=https://wikipediaquality.com/wiki/Wikipedia,_Collective_Authorship,_and_the_Politics_of_Knowledge |journal=Edward Elgar Publishing}}<br />
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Rimmer, Matthew. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia,_Collective_Authorship,_and_the_Politics_of_Knowledge">Wikipedia, Collective Authorship, and the Politics of Knowledge</a>&amp;quot;. Edward Elgar Publishing. DOI: 10.4337/9781848449039.00016. <br />
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</code></div>Audreyhttps://wikipediaquality.com/index.php?title=Approach_on_Visualization_of_Categories_and_Articles_in_Wikipedia&diff=20952Approach on Visualization of Categories and Articles in Wikipedia2019-10-05T19:18:21Z<p>Audrey: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Approach on Visualization of Categories and Articles in Wikipedia<br />
| date = 2009<br />
| authors = [[Chen Xiaowu]]<br />
| link = http://en.cnki.com.cn/Article_en/CJFDTotal-XTFZ2009S1042.htm<br />
}}<br />
'''Approach on Visualization of Categories and Articles in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Chen Xiaowu]].<br />
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== Overview ==<br />
Wikipedia is a free and open online encyclopedia, which allows users from all over the world to edit its articles and [[categories]], and it has been an important online knowledge source. However, the number of categories and articles relationship is huge, and the depth of categories is too deep, those together form a complicated and huge knowledge network. For [[Wikipedia]]’s huge size and complication, users cannot locate their interested information and learn knowledge from Wikipedia. To solve the problem, the approach of this paper analyses the feature of categories and articles in Wikipedia, extracts core relationship among categories based on statistic method, and the core relationship allows user find existing important relationships from all relationships of an article. In further, this approach designs the visualization of categories and article, and is able to display multi-type relations and detail whole information of Wikipedia.</div>Audreyhttps://wikipediaquality.com/index.php?title=Negotiating_Cultural_Values_in_Social_Media:_a_Case_Study_from_Wikipedia&diff=20951Negotiating Cultural Values in Social Media: a Case Study from Wikipedia2019-10-05T19:15:25Z<p>Audrey: Category</p>
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<div>{{Infobox work<br />
| title = Negotiating Cultural Values in Social Media: a Case Study from Wikipedia<br />
| date = 2012<br />
| authors = [[Jonathan T. Morgan]]<br />[[Robert M. Mason]]<br />[[Karine Nahon]]<br />
| doi = 10.1109/HICSS.2012.443<br />
| link = http://ieeexplore.ieee.org/document/6149246/<br />
| plink = https://pdfs.semanticscholar.org/aa6e/906a7e58b93acce26bc7864564b9fd0c134b.pdf<br />
}}<br />
'''Negotiating Cultural Values in Social Media: a Case Study from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Jonathan T. Morgan]], [[Robert M. Mason]] and [[Karine Nahon]].<br />
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== Overview ==<br />
Wikipedia arguably is one of the most visible examples of the use of social media to enlist volunteers to contribute to a social good. [[Wikipedia]] was created to provide an accessible, encyclopedic information resource for people of all nations and cultures. Previous research has shown potential for unacknowledged cultural bias in socio-technical systems. However, the extent to which the technological and social structures of the [[English Wikipedia]] are shaped by its western origin and orientation has not been examined. Authors fill this gap by studying how [[Wikipedia editors]] created the culturally controversial article Jyllands-Posten Muhammad Cartoon Controversy. Authors use Carlile's boundary-spanning model to illustrate how Wikipedia is unable to satisfactorily resolve the fundamental tension between its stated mission of global access and empowerment and the inherent (but unacknowledged) cultural bias of the technologies and processes employed by the English language [[Wikipedia community]]. This case study illustrates how knowledge management systems, even those intended to encompass multiple value systems through the use of an open social media design, have built-in (value) biases through the specific technologies and processes employed in the design.<br />
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Morgan, Jonathan T.; Mason, Robert M.; Nahon, Karine. (2012). "[[Negotiating Cultural Values in Social Media: a Case Study from Wikipedia]]".DOI: 10.1109/HICSS.2012.443. <br />
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{{cite journal |last1=Morgan |first1=Jonathan T. |last2=Mason |first2=Robert M. |last3=Nahon |first3=Karine |title=Negotiating Cultural Values in Social Media: a Case Study from Wikipedia |date=2012 |doi=10.1109/HICSS.2012.443 |url=https://wikipediaquality.com/wiki/Negotiating_Cultural_Values_in_Social_Media:_a_Case_Study_from_Wikipedia}}<br />
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Morgan, Jonathan T.; Mason, Robert M.; Nahon, Karine. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Negotiating_Cultural_Values_in_Social_Media:_a_Case_Study_from_Wikipedia">Negotiating Cultural Values in Social Media: a Case Study from Wikipedia</a>&amp;quot;.DOI: 10.1109/HICSS.2012.443. <br />
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[[Category:Scientific works]]<br />
[[Category:English Wikipedia]]</div>Audreyhttps://wikipediaquality.com/index.php?title=Extracting_Semantic_Concept_Relations_from_Wikipedia&diff=20950Extracting Semantic Concept Relations from Wikipedia2019-10-05T19:14:19Z<p>Audrey: Adding wikilinks</p>
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<div>'''Extracting Semantic Concept Relations from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Patrick Arnold]] and [[Erhard Rahm]].<br />
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== Overview ==<br />
Background knowledge as provided by repositories such as [[WordNet]] is of critical importance for linking or mapping ontologies and related tasks. Since current repositories are quite limited in their scope and currentness, authors investigate how to automatically build up improved repositories by extracting semantic relations (e.g., is-a and part-of relations) from [[Wikipedia]] articles. Authors approach uses a comprehensive set of semantic patterns, finite state machines and NLP-techniques to process Wikipedia definitions and to identify semantic relations between concepts. Authors approach is able to extract multiple relations from a single Wikipedia article. An evaluation for different domains shows the high quality and effectiveness of the proposed approach.</div>Audreyhttps://wikipediaquality.com/index.php?title=Don%E2%80%99T_Cite_It,_Write_It._Raising_Awareness_of_Acoustics_Through_Wikipedia&diff=20949Don’T Cite It, Write It. Raising Awareness of Acoustics Through Wikipedia2019-10-05T19:11:47Z<p>Audrey: Int.links</p>
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<div>'''Don’T Cite It, Write It. Raising Awareness of Acoustics Through Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Thais C. Morata]], [[Max Lum]], [[James Hare]] and [[Leonardo Fuks]].<br />
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== Overview ==<br />
Wikipedia is accessed by hundreds of millions around the world and that makes [[Wikipedia]] one of the most powerful platforms for the dissemination of science information. While Wikipedia offers high-quality content about certain topics, a large proportion of articles are insufficiently developed. The [[Wikimedia Foundation]] has engaged in partnerships with scientific and academic institutions to improve the coverage and communication of science to the public. These efforts are beneficial to professional and academic associations interested in sharing reliable, vetted information about their discipline with the world. The National Institute for Occupational Safety and Health (NIOSH) is one of the agencies engaged in this effort. NIOSH developed and manages the WikiProject Occupational Safety and Health. NIOSH also participated in a classroom program (where students write Wikipedia articles) to expand and improve Wikipedia’s content on acoustics, noise, and hearing loss prevention. Faculty and students from the ...</div>Audreyhttps://wikipediaquality.com/index.php?title=Time-Focused_Analysis_of_Connectivity_and_Popularity_of_Historical_Persons_in_Wikipedia&diff=20948Time-Focused Analysis of Connectivity and Popularity of Historical Persons in Wikipedia2019-10-05T19:10:13Z<p>Audrey: + embed code</p>
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<div>{{Infobox work<br />
| title = Time-Focused Analysis of Connectivity and Popularity of Historical Persons in Wikipedia<br />
| date = 2018<br />
| authors = [[Adam Jatowt]]<br />[[Daisuke Kawai]]<br />[[Katsumi Tanaka]]<br />
| doi = 10.1007/s00799-018-0231-4<br />
| link = https://link.springer.com/article/10.1007%2Fs00799-018-0231-4<br />
}}<br />
'''Time-Focused Analysis of Connectivity and Popularity of Historical Persons in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Adam Jatowt]], [[Daisuke Kawai]] and [[Katsumi Tanaka]].<br />
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== Overview ==<br />
Wikipedia contains large amounts of content related to history. It is being used extensively for many knowledge intensive tasks within computer science, digital humanities and related fields. In this paper, authors look into [[Wikipedia]] articles on historical people for studying link-related temporal [[features]] of articles on past people. Authors study sheds new light on the characteristics of information about historical people recorded in the [[English Wikipedia]] and quantifies user interest in such data. Authors propose a novel style of analysis in which authors use signals derived from the hyperlink structure of Wikipedia as well as from article view logs, and authors overlay them over temporal dimension to understand relations between time periods, link structure and article popularity. In the latter part of the paper, authors also demonstrate several ways for estimating person importance based on the temporal aspects of the link structure as well as a method for ranking cities using the computed importance scores of their related persons.<br />
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Jatowt, Adam; Kawai, Daisuke; Tanaka, Katsumi. (2018). "[[Time-Focused Analysis of Connectivity and Popularity of Historical Persons in Wikipedia]]". Springer Berlin Heidelberg. DOI: 10.1007/s00799-018-0231-4. <br />
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{{cite journal |last1=Jatowt |first1=Adam |last2=Kawai |first2=Daisuke |last3=Tanaka |first3=Katsumi |title=Time-Focused Analysis of Connectivity and Popularity of Historical Persons in Wikipedia |date=2018 |doi=10.1007/s00799-018-0231-4 |url=https://wikipediaquality.com/wiki/Time-Focused_Analysis_of_Connectivity_and_Popularity_of_Historical_Persons_in_Wikipedia |journal=Springer Berlin Heidelberg}}<br />
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Jatowt, Adam; Kawai, Daisuke; Tanaka, Katsumi. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Time-Focused_Analysis_of_Connectivity_and_Popularity_of_Historical_Persons_in_Wikipedia">Time-Focused Analysis of Connectivity and Popularity of Historical Persons in Wikipedia</a>&amp;quot;. Springer Berlin Heidelberg. DOI: 10.1007/s00799-018-0231-4. <br />
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</code></div>Audreyhttps://wikipediaquality.com/index.php?title=.@Senatorburr_-_Those_Who_Forget_the_Past_are_Doomed_to_Repeat_It_-_and_Put_Our_Security_at_Risk_When_They_Do.Https://En.Wikipedia.Org/Wiki/Clipper_Chip&diff=20947.@Senatorburr - Those Who Forget the Past are Doomed to Repeat It - and Put Our Security at Risk When They Do.Https://En.Wikipedia.Org/Wiki/Clipper Chip2019-10-05T19:08:08Z<p>Audrey: infobox</p>
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<div>{{Infobox work<br />
| title = .@Senatorburr - Those Who Forget the Past are Doomed to Repeat It - and Put Our Security at Risk When They Do.Https://En.Wikipedia.Org/Wiki/Clipper_Chip#Technical_Vulnerabilities<br />
| date = 2016<br />
| authors = [[Eric Woodward]]<br />
| link = https://mysticbits.com/2016/senatorburr---those-who-forget-the-past-are-doomed-to<br />
}}<br />
'''.@Senatorburr - Those Who Forget the Past are Doomed to Repeat It - and Put Our Security at Risk When They Do.Https://En.Wikipedia.Org/Wiki/Clipper_Chip#Technical_Vulnerabilities''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Eric Woodward]].<br />
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== Overview ==<br />
.@SenatorBurr - Those who forget the past are doomed to repeat it - and put security at risk when they do.</div>Audreyhttps://wikipediaquality.com/index.php?title=The_Xtrieval_Framework_at_Clef_2008:_Imageclef_Wikipedia_Mm_Task&diff=20946The Xtrieval Framework at Clef 2008: Imageclef Wikipedia Mm Task2019-10-05T19:06:08Z<p>Audrey: infobox</p>
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<div>{{Infobox work<br />
| title = The Xtrieval Framework at Clef 2008: Imageclef Wikipedia Mm Task<br />
| date = 2008<br />
| authors = [[Thomas Wilhelm]]<br />[[Jens Kürsten]]<br />[[Maximilian Eibl]]<br />
| link = http://ceur-ws.org/Vol-1174/CLEF2008wn-ImageCLEF-WilhelmEt2008b.pdf<br />
}}<br />
'''The Xtrieval Framework at Clef 2008: Imageclef Wikipedia Mm Task''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Thomas Wilhelm]], [[Jens Kürsten]] and [[Maximilian Eibl]].<br />
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== Overview ==<br />
This paper describes participation at the ImageCLEF [[Wikipedia]] MM task. Authors used Xtrieval framework for the preparation and execution of the experiments. Authors submitted 4 experiments in total. The results of these experiments were mixed. The text-only experiment scored second best with a mean average precision (MAP) of 0.2166. In combination with image based [[features]] the MAP dropped to 0.2138. With the addition of thesaurus based query expansion it scored best with a MAP of 0.2195. Without query expansion and with the inclusion of the provided concepts the lowest MAP of 0.2048 was achieved, but there were 23 more relevant documents retrieved than in all 3 other experiments. Furthermore, the retrieval speed and comparison operations for vectors could be speeded up by implementing an interface to the PostgreSQL database.</div>Audreyhttps://wikipediaquality.com/index.php?title=Wcaminer:_a_Novel_Knowledge_Discovery_System_for_Mining_Concept_Associations_Using_Wikipedia&diff=20945Wcaminer: a Novel Knowledge Discovery System for Mining Concept Associations Using Wikipedia2019-10-05T19:03:36Z<p>Audrey: Adding new article - Wcaminer: a Novel Knowledge Discovery System for Mining Concept Associations Using Wikipedia</p>
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<div>'''Wcaminer: a Novel Knowledge Discovery System for Mining Concept Associations Using Wikipedia''' - scientific work related to Wikipedia quality published in 2014, written by Peng Yan and Wei Jin.<br />
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== Overview ==<br />
This paper presents WCAMiner, a system focusing on detecting how concepts are associated by incorporating Wikipedia knowledge. Authors propose to combine content analysis and link analysis techniques over Wikipedia resources, and define various association mining models to interpret such queries. Specifically, algorithm can automatically build a Concept Association Graph (CAG) from Wikipedia for two given topics of interest, and generate a ranked list of concept chains as potential associations between the two given topics. In comparison to traditional cross-document mining models where documents are usually domain-specific, the system proposed here is capable of handling different query scenarios across domains without being limited to the given documents. Authors highlight the importance of this problem in various domains, present experiments on different datasets and compare the mining results with two competitive baseline models to demonstrate the improved performance of system.</div>Audrey