https://wikipediaquality.com/api.php?action=feedcontributions&user=Sadie&feedformat=atomWikipedia Quality - User contributions [en]2024-03-28T13:58:13ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=An_Examination_of_the_Culture_of_Impartiality_in_Wikipedia,_a_Case_Study_of_the_Islamic_World_Representation_in_the_English_and_Persian_Versions_of_the_Wikipedia&diff=22238An Examination of the Culture of Impartiality in Wikipedia, a Case Study of the Islamic World Representation in the English and Persian Versions of the Wikipedia2019-11-20T07:59:42Z<p>Sadie: Adding infobox</p>
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<div>{{Infobox work<br />
| title = An Examination of the Culture of Impartiality in Wikipedia, a Case Study of the Islamic World Representation in the English and Persian Versions of the Wikipedia<br />
| date = 2015<br />
| authors = [[Somayeh Bahrami]]<br />[[Mojtaba Touiserkani]]<br />[[Majid Reza Momeni]]<br />
| doi = 10.1109/Culture.and.Computing.2015.17<br />
| link = <br />
}}<br />
'''An Examination of the Culture of Impartiality in Wikipedia, a Case Study of the Islamic World Representation in the English and Persian Versions of the Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Somayeh Bahrami]], [[Mojtaba Touiserkani]] and [[Majid Reza Momeni]].<br />
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== Overview ==<br />
It is necessary for the editors to observe the culture of impartiality while editing the texts in order to preserve the value and life of this critical encyclopedia. Since everybody is allowed to edit [[Wikipedia]] entries, observing impartiality is practically and mostly dependent on the good will of the [[Wikipedians]] causing some Muslims accuse Wikipedia of defying from impartiality to the Islamic world. Therefore the question to be addressed is "how is the culture of impartiality among the [[Wikipedia editors]] and users in the process of representing the Islamic World?" The research is carried out through the qualitative content analysis method and the deductive category application during a coded instruction including fourteen [[indicators]] (five dichotomous [[categories]] and nine references) which has surprisingly demonstrated high level of impartiality culture among the users of both Persian and English versions of the Wikipedia in the editing process of texts on the Muslim World. More surprisingly, it was shown that the proportion of positive to negative views on Islamic World is 0.89 in the Persian Wikipedia, while being 1.07 in the [[English Wikipedia]].</div>Sadiehttps://wikipediaquality.com/index.php?title=Automatic_Detection_of_Point_of_View_Differences_in_Wikipedia&diff=22237Automatic Detection of Point of View Differences in Wikipedia2019-11-20T07:56:52Z<p>Sadie: + infobox</p>
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<div>{{Infobox work<br />
| title = Automatic Detection of Point of View Differences in Wikipedia<br />
| date = 2012<br />
| authors = [[Khalid Al Khatib]]<br />[[Hinrich Sch"utze]]<br />[[Cathleen Kantner]]<br />
| link = http://www.aclweb.org/anthology/C/C12/C12-1003.pdf<br />
}}<br />
'''Automatic Detection of Point of View Differences in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Khalid Al Khatib]], [[Hinrich Sch"utze]] and [[Cathleen Kantner]].<br />
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== Overview ==<br />
Authors investigate differences in point of view (POV) between two objective documents, where one is describing the subject matter in a more positive/negative way than the other, and present an automatic method for detecting such POV differences. Authors use [[Amazon]] Mechanical Turk (AMT) to annotate sentences as positive, negative or neutral based on their POV towards a given target. A statistical classifier is trained to predict the POV score of a document, which reflects how positive/negative the document’s POV towards its target is. The results of experiments on a set of articles in the Arabic and [[English Wikipedia]]s from the people category show that method successfully detects POV differences.</div>Sadiehttps://wikipediaquality.com/index.php?title=-_Legal_Nerc_with_Ontologies,Wikipedia_and_Curriculum_Learning&diff=22236- Legal Nerc with Ontologies,Wikipedia and Curriculum Learning2019-11-20T07:55:19Z<p>Sadie: + Infobox work</p>
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<div>{{Infobox work<br />
| title = - Legal Nerc with Ontologies,Wikipedia and Curriculum Learning<br />
| date = 2017<br />
| authors = [[Cristian Cardellino]]<br />[[Milagro Teruel]]<br />[[Laura Alonso Alemany]]<br />[[Serena Villata]]<br />
| doi = 10.18653/v1/E17-2041<br />
| link = http://aclweb.org/anthology/E17-2041<br />
}}<br />
'''- Legal Nerc with Ontologies,Wikipedia and Curriculum Learning''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Cristian Cardellino]], [[Milagro Teruel]], [[Laura Alonso Alemany]] and [[Serena Villata]].<br />
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== Overview ==<br />
In this paper, authors present a [[Wikipedia]]-based approach to develop resources for the legal domain. Authors establish a mapping between a legal domain [[ontology]], LKIF (Hoekstra et al., 2007), and a Wikipedia-based ontology, YAGO (Suchanek et al., 2007), and through that authors populate LKIF. Moreover, authors use the mentions of those entities in Wikipedia text to train a specific Named Entity Recognizer and Classifier. Authors find that this classifier works well in the Wikipedia, but, as could be expected, performance decreases in a corpus of judgments of the European Court of Human Rights. However, this tool will be used as a preprocess for human annotation. Authors resort to a technique called curriculum learning aimed to overcome problems of overfitting by learning increasingly more complex concepts. However, authors find that in this particular setting, the method works best by learning from most specific to most general concepts, not the other way round.</div>Sadiehttps://wikipediaquality.com/index.php?title=Mining_Tibetan-Chinese_Bilingual_Entities_from_Wikipedia&diff=22235Mining Tibetan-Chinese Bilingual Entities from Wikipedia2019-11-20T07:52:31Z<p>Sadie: Infobox</p>
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<div>{{Infobox work<br />
| title = Mining Tibetan-Chinese Bilingual Entities from Wikipedia<br />
| date = 2017<br />
| authors = [[Tao Jiang]]<br />[[Hongzhi Yu]]<br />[[Xiangzhen He]]<br />[[Xianghe Meng]]<br />
| doi = 10.1109/ialp.2017.8300534<br />
| link = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8300534<br />
}}<br />
'''Mining Tibetan-Chinese Bilingual Entities from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Tao Jiang]], [[Hongzhi Yu]], [[Xiangzhen He]] and [[Xianghe Meng]].<br />
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== Overview ==<br />
Entity translation pairs play an important role in NLP applications, such as cross language [[information retrieval]] and [[machine translation]]. The [[named entity]] and domain entity are key factors that affect the performance of the system. However, the entity translations can hardly be found in the present bilingual dictionary or parallel corpus. There are lots of Tibetan new neologisms and [[named entities]] in Tibetan [[Wikipedia]], and this paper proposes a new method to automatically mining method of Tibetan and Chinese bilingual entity translation from Wikipedia based on the language interlink and page feature. Authors construct an extract pattern of Tibetan and Chinese entity translation pairs gained from the previous work, and adopt multi-feature candidate translation pairs to distinguish the selection model. The results verify that the entity translation mining method can achieve high accuracy.</div>Sadiehttps://wikipediaquality.com/index.php?title=Wikirevolutions:_Wikipedia_as_a_Lens_for_Studying_the_Real-Time_Formation_of_Collective_Memories_of_Revolutions&diff=22234Wikirevolutions: Wikipedia as a Lens for Studying the Real-Time Formation of Collective Memories of Revolutions2019-11-20T07:50:37Z<p>Sadie: + Embed</p>
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<div>{{Infobox work<br />
| title = Wikirevolutions: Wikipedia as a Lens for Studying the Real-Time Formation of Collective Memories of Revolutions<br />
| date = 2011<br />
| authors = [[Michela Ferron]]<br />[[Paolo Massa]]<br />
| link = http://ijoc.org/index.php/ijoc/article/viewFile/1238/608<br />
}}<br />
'''Wikirevolutions: Wikipedia as a Lens for Studying the Real-Time Formation of Collective Memories of Revolutions''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Michela Ferron]] and [[Paolo Massa]].<br />
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== Overview ==<br />
In this article, authors propose to interpret the online encyclopedia [[Wikipedia]] as an online setting in which collective memories about controversial and traumatic events are built in a collaborative way. Authors present the richness of data available on the phenomenon, providing examples of users’ participation in the creation of articles related to the 2011 Egyptian revolution. Finally, authors propose possible research directions for the empirical study of collective memory formation of traumatic and controversial events in large populations as they unfold over time.<br />
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Ferron, Michela; Massa, Paolo. (2011). "[[Wikirevolutions: Wikipedia as a Lens for Studying the Real-Time Formation of Collective Memories of Revolutions]]".<br />
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{{cite journal |last1=Ferron |first1=Michela |last2=Massa |first2=Paolo |title=Wikirevolutions: Wikipedia as a Lens for Studying the Real-Time Formation of Collective Memories of Revolutions |date=2011 |url=https://wikipediaquality.com/wiki/Wikirevolutions:_Wikipedia_as_a_Lens_for_Studying_the_Real-Time_Formation_of_Collective_Memories_of_Revolutions}}<br />
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Ferron, Michela; Massa, Paolo. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikirevolutions:_Wikipedia_as_a_Lens_for_Studying_the_Real-Time_Formation_of_Collective_Memories_of_Revolutions">Wikirevolutions: Wikipedia as a Lens for Studying the Real-Time Formation of Collective Memories of Revolutions</a>&amp;quot;.<br />
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</code></div>Sadiehttps://wikipediaquality.com/index.php?title=Gpx@Inex2007:_Ad-Hoc_Queries_and_Automated_Link_Discovery_in_the_Wikipedia&diff=22233Gpx@Inex2007: Ad-Hoc Queries and Automated Link Discovery in the Wikipedia2019-11-20T07:48:25Z<p>Sadie: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Gpx@Inex2007: Ad-Hoc Queries and Automated Link Discovery in the Wikipedia<br />
| date = 2007<br />
| authors = [[Shlomo Geva]]<br />
| link = http://eprints.qut.edu.au/18173/1/c18173.pdf<br />
}}<br />
'''Gpx@Inex2007: Ad-Hoc Queries and Automated Link Discovery in the Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Shlomo Geva]].<br />
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== Overview ==<br />
The INEX 2007 evaluation was based on the [[Wikipedia]] collection in XML format. In this paper authors describe some modifications to the GPX search engine and the approach taken in the Ad-hoc and the Link-the-Wiki tracks. The GPX retrieval strategy is based on the construction of a collection sub-tree, consisting of all nodes that contain one or more of the search terms. Nodes containing search terms are assigned a score using the GPX ranking scheme which incorporates an extended TF-IDF variant. In earlier version of GPX scores were recursively propagated from text containing nodes, through ancestors, all the way to the document root of the XML tree. In this paper authors describe a simplification whereby the score of each node is computed directly, doing away with the score propagation mechanism. Preliminary results indicate improved performance. The GPX search engine was used in the Link-the-Wiki track to identify prospective incoming links to new Wikipedia pages. Authors also describe a simple and efficient approach to the identification of prospective outgoing links in new Wikipedia pages. Authors present preliminary evaluation results. 1. The GPX Search Engine For the sake of [[completeness]] authors provide a very brief description of GPX. The reader is referred to earlier papers on GPX in INEX previous proceedings for a more complete description. The search engine is based on XPath inverted lists. For each term in the collection authors maintain an inverted list of XPath specifications. This includes the file name, the absolute XPath identifying a specific XML element, and the term position within the element. The actual data structure is designed for efficient storage and retrieval of the inverted list which are considerably less concise by comparison with basic text retrieval inverted lists. Authors briefly describe the data structure, then authors describe the node scoring calculation, and finally authors present the results.<br />
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Geva, Shlomo. (2007). "[[Gpx@Inex2007: Ad-Hoc Queries and Automated Link Discovery in the Wikipedia]]".<br />
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{{cite journal |last1=Geva |first1=Shlomo |title=Gpx@Inex2007: Ad-Hoc Queries and Automated Link Discovery in the Wikipedia |date=2007 |url=https://wikipediaquality.com/wiki/Gpx@Inex2007:_Ad-Hoc_Queries_and_Automated_Link_Discovery_in_the_Wikipedia}}<br />
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Geva, Shlomo. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Gpx@Inex2007:_Ad-Hoc_Queries_and_Automated_Link_Discovery_in_the_Wikipedia">Gpx@Inex2007: Ad-Hoc Queries and Automated Link Discovery in the Wikipedia</a>&amp;quot;.<br />
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</code></div>Sadiehttps://wikipediaquality.com/index.php?title=What_Do_Wikidata_and_Wikipedia_Have_in_Common%3F:_an_Analysis_of_Their_Use_of_External_References&diff=22232What Do Wikidata and Wikipedia Have in Common?: an Analysis of Their Use of External References2019-11-20T07:45:28Z<p>Sadie: Infobox</p>
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<div>{{Infobox work<br />
| title = What Do Wikidata and Wikipedia Have in Common?: an Analysis of Their Use of External References<br />
| date = 2017<br />
| authors = [[Alessandro Piscopo]]<br />[[Pavlos Vougiouklis]]<br />[[Lucie-Aimée Kaffee]]<br />[[Christopher Phethean]]<br />[[Jonathon S. Hare]]<br />[[Elena Simperl]]<br />
| doi = 10.1145/3125433.3125445<br />
| link = http://dl.acm.org/citation.cfm?id=3125445<br />
}}<br />
'''What Do Wikidata and Wikipedia Have in Common?: an Analysis of Their Use of External References''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Alessandro Piscopo]], [[Pavlos Vougiouklis]], [[Lucie-Aimée Kaffee]], [[Christopher Phethean]], [[Jonathon S. Hare]] and [[Elena Simperl]].<br />
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== Overview ==<br />
Wikidata is a community-driven knowledge graph, strongly linked to [[Wikipedia]]. However, the connection between the two projects has been sporadically explored. Authors investigated the relationship between the two projects in terms of the information they contain by looking at their external references. Authors findings show that while only a small number of sources is directly reused across [[Wikidata]] and Wikipedia, references often point to the same domain. Furthermore, Wikidata appears to use less Anglo-American-centred sources. These results deserve further in-depth investigation.</div>Sadiehttps://wikipediaquality.com/index.php?title=Automatically_Refining_the_Wikipedia_Infobox_Ontology&diff=22231Automatically Refining the Wikipedia Infobox Ontology2019-11-20T07:43:02Z<p>Sadie: Adding categories</p>
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<div>{{Infobox work<br />
| title = Automatically Refining the Wikipedia Infobox Ontology<br />
| date = 2008<br />
| authors = [[Fei Wu]]<br />[[Daniel S. Weld]]<br />
| doi = 10.1145/1367497.1367583<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=1367583&amp;type=pdf<br />
| plink = https://www.semanticscholar.org/paper/Automatically-refining-the-wikipedia-infobox-Wu-Weld/0c236e611a90018e84d9de23d1cff241354079be<br />
}}<br />
'''Automatically Refining the Wikipedia Infobox Ontology''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Fei Wu]] and [[Daniel S. Weld]].<br />
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== Overview ==<br />
The combined efforts of human volunteers have recently extracted numerous facts from [[Wikipedia]], storing them as machine-harvestable object-attribute-value triples in Wikipedia [[infoboxes]]. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more [[semantic knowledge]] from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured [[ontology]]. This paper introduces KOG, an autonomous system for refining Wikipedia's infobox-class ontology towards this end. Authors cast the problem of ontology refinement as a machine learning problem and solve it using both SVMs and a more powerful joint-inference approach expressed in Markov Logic Networks. Authors present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of system. Using these techniques, authors build a rich ontology, integrating Wikipedia's infobox-class schemata with [[WordNet]]. Authors demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other [[features]].<br />
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{{cite journal |last1=Wu |first1=Fei |last2=Weld |first2=Daniel S. |title=Automatically Refining the Wikipedia Infobox Ontology |date=2008 |doi=10.1145/1367497.1367583 |url=https://wikipediaquality.com/wiki/Automatically_Refining_the_Wikipedia_Infobox_Ontology}}<br />
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Wu, Fei; Weld, Daniel S.. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/Automatically_Refining_the_Wikipedia_Infobox_Ontology">Automatically Refining the Wikipedia Infobox Ontology</a>&amp;quot;.DOI: 10.1145/1367497.1367583. <br />
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[[Category:Scientific works]]</div>Sadiehttps://wikipediaquality.com/index.php?title=Wikipedia-Based_Hybrid_Document_Representation_for_Textual_News_Classification&diff=22230Wikipedia-Based Hybrid Document Representation for Textual News Classification2019-11-20T07:40:22Z<p>Sadie: Links</p>
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<div>'''Wikipedia-Based Hybrid Document Representation for Textual News Classification''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Marcos Mouriño García]], [[Roberto Pérez Rodríguez]], [[Manuel Vilares Ferro]] and [[Luis Anido Rifón]].<br />
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== Overview ==<br />
Automatic classification of news articles is a relevant problem due to the large amount of news generated every day, so it is crucial that these news are classified to allow for users to access to information of interest quickly and effectively. On the one hand, traditional classification systems represent documents as bag-of-words (BoW), which are oblivious to two problems of language: synonymy and polysemy. On the other hand, several authors propose the use of a bag-of-concepts (BoC) representation of documents, which tackles synonymy and polysemy. This paper shows the benefits of using a hybrid representation of documents to the classification of textual news, leveraging the advantages of both approaches—the traditional BoW representation and a BoC approach based on [[Wikipedia]] knowledge. To evaluate the proposal, authors used three of the most relevant algorithms in the state-of-the art—SVM, [[Random Forest]] and Naive Bayes—and two corpora: the Reuters-21578 corpus and a purpose-built corpus, Reuters-27000. Results obtained show that the performance of the classification algorithm depends on the dataset used, and also demonstrate that the enrichment of the BoW representation with the concepts extracted from documents through the semantic annotator adds useful information to the classifier and improves their performance. Experiments conducted show performance increases up to 4.12% when classifying the Reuters-21578 corpus with the SVM algorithm and up to 49.35% when classifying the corpus Reuters-27000 with the [[Random Forest]] algorithm.</div>Sadiehttps://wikipediaquality.com/index.php?title=Extracting_Structured_Information_from_Chinese_Wikipedia_and_Measuring_Relatedness_Between_Words&diff=22229Extracting Structured Information from Chinese Wikipedia and Measuring Relatedness Between Words2019-11-20T07:38:31Z<p>Sadie: Adding embed</p>
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<div>{{Infobox work<br />
| title = Extracting Structured Information from Chinese Wikipedia and Measuring Relatedness Between Words<br />
| date = 2012<br />
| authors = [[He Tingting]]<br />
| link = http://en.cnki.com.cn/Article_en/CJFDTOTAL-MESS201203020.htm<br />
}}<br />
'''Extracting Structured Information from Chinese Wikipedia and Measuring Relatedness Between Words''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[He Tingting]].<br />
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== Overview ==<br />
The [[Wikipedia]] is the biggest web-based encyclopedia,which is written collaboratively by volunteers around the world.It has many advantages,such as wide knowledge coverage,highly structured and rapid information update.However,the Wikipedia official website just provides some original data files,and much structured [[semantic knowledge]] can't be used directly.Therefore,in this paper,we firstly extract the [[structured information]] from these data files;then,we design the object model for the information in Wikipedia,and provide an open API for Wikipedia information;finally,we propose a novel method to compute [[relatedness]] between words.<br />
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{{cite journal |last1=Tingting |first1=He |title=Extracting Structured Information from Chinese Wikipedia and Measuring Relatedness Between Words |date=2012 |url=https://wikipediaquality.com/wiki/Extracting_Structured_Information_from_Chinese_Wikipedia_and_Measuring_Relatedness_Between_Words}}<br />
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Tingting, He. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extracting_Structured_Information_from_Chinese_Wikipedia_and_Measuring_Relatedness_Between_Words">Extracting Structured Information from Chinese Wikipedia and Measuring Relatedness Between Words</a>&amp;quot;.<br />
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<div>{{Infobox work<br />
| title = Wikipedia is Significantly Amplifying the Impact of Open Access Publications<br />
| date = 2015<br />
| authors = [[Eamon Duede]]<br />
| link = http://eprints.lse.ac.uk/70844/<br />
}}<br />
'''Wikipedia is Significantly Amplifying the Impact of Open Access Publications''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Eamon Duede]].<br />
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== Overview ==<br />
When you edit [[Wikipedia]] to include a claim, you are required to substantiate that edit by referencing a reliable source. According to a recent study, the single biggest predictor of a journal’s appearance in Wikipedia is its impact factor. One of the exciting findings, writes Eamon Duede, is that it appears [[Wikipedia editors]] are putting a premium on open access content. When given a choice between journals of similar impact factors, editors are significantly more likely to select the “open access” option.</div>Sadiehttps://wikipediaquality.com/index.php?title=Fibs_in_the_Wikipedia&diff=22227Fibs in the Wikipedia2019-11-20T07:34:43Z<p>Sadie: Infobox work</p>
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<div>{{Infobox work<br />
| title = Fibs in the Wikipedia<br />
| date = 2008<br />
| authors = [[P. D. Magnus]]<br />
| link = https://dspace.sunyconnect.suny.edu/bitstream/1951/43003/1/fibdata.pdf<br />
}}<br />
'''Fibs in the Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[P. D. Magnus]].<br />
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== Overview ==<br />
These are details of research conducted in November and December 2007. The file is meant as a supplement to publication, and Author have not attempted here to provide any analysis of the results.</div>Sadiehttps://wikipediaquality.com/index.php?title=Wikipedia_Ipod_Usability_Data&diff=22226Wikipedia Ipod Usability Data2019-11-20T07:32:45Z<p>Sadie: + infobox</p>
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<div>{{Infobox work<br />
| title = Wikipedia Ipod Usability Data<br />
| date = 2008<br />
| authors = [[James F. Hahn]]<br />
| link = https://www.ideals.illinois.edu/handle/2142/9574<br />
}}<br />
'''Wikipedia Ipod Usability Data''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[James F. Hahn]].<br />
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== Overview ==<br />
The author wishes to acknowledge the Research and Publication Committee of the University of Illinois at Urbana-Champaign Library, which provided support for the completion of this research.</div>Sadiehttps://wikipediaquality.com/index.php?title=Wikipedia_%E2%80%93_Amat%C3%B8renes_Inntog&diff=22225Wikipedia – Amatørenes Inntog2019-11-20T07:31:30Z<p>Sadie: Infobox</p>
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<div>{{Infobox work<br />
| title = Wikipedia – Amatørenes Inntog<br />
| date = 2007<br />
| authors = [[Chris Nyborg]]<br />
| link = https://tidsskrift.dk/index.php/lexn/article/view/18537<br />
}}<br />
'''Wikipedia – Amatørenes Inntog''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Chris Nyborg]].<br />
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== Overview ==<br />
Wikipedia is the entry of amateurs into the world of encyclopaedias. Volunteers with no background in lexicography are building a massive project from scratch. The article, based on a presentation given in January 2007, outlines the mechanisms and processes involved in [[Wikipedia]]’s explosive growth. By trial and error, the contributors work out the project’s rules as they go along. Many [[Wikipedians]] become interested in exploring the theory and practice of creating an encyclopaedia, combining traditional lexicographical concepts with Wikipedia’s focus on freedom of information and the ability of anyone to contribute.</div>Sadiehttps://wikipediaquality.com/index.php?title=View_of_the_World_According_to_Wikipedia:_are_We_All_Little_Steinbergs%3F&diff=22224View of the World According to Wikipedia: are We All Little Steinbergs?2019-11-20T07:30:12Z<p>Sadie: New work - View of the World According to Wikipedia: are We All Little Steinbergs?</p>
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<div>'''View of the World According to Wikipedia: are We All Little Steinbergs?''' - scientific work related to Wikipedia quality published in 2011, written by Simon E. Overell and Stefan M. Rüger.<br />
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== Overview ==<br />
Abstract Saul Steinberg's most famous cartoon “View of the world from 9th Avenue” depicts the world as seen by self-absorbed New Yorkers. By analysing wikipediae of a range of different languages, authors find that this particular fish-eye world view is ubiquitous and inherently part of human nature. By measuring the skew in the distribution of locations in different languages authors can confirm the validity of plausible quantitative models. These models demonstrate convincingly that people all have similar world views: “Authors are all little Steinbergs.” Authors Steinberg hypothesis allows the world view of specific people to be more accurately modelled; this will allow greater understanding of a person's discourse, either by someone else or automatically by a computer.</div>Sadiehttps://wikipediaquality.com/index.php?title=Improving_the_Extraction_of_Bilingual_Terminology_from_Wikipedia&diff=22223Improving the Extraction of Bilingual Terminology from Wikipedia2019-11-20T07:27:45Z<p>Sadie: Embed</p>
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<div>{{Infobox work<br />
| title = Improving the Extraction of Bilingual Terminology from Wikipedia<br />
| date = 2009<br />
| authors = [[Maike Erdmann]]<br />[[Kotaro Nakayama]]<br />[[Takahiro Hara]]<br />[[Shojiro Nishio]]<br />
| doi = 10.1145/1596990.1596995<br />
| link = http://dl.acm.org/citation.cfm?id=1596990.1596995<br />
}}<br />
'''Improving the Extraction of Bilingual Terminology from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Maike Erdmann]], [[Kotaro Nakayama]], [[Takahiro Hara]] and [[Shojiro Nishio]].<br />
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== Overview ==<br />
Research on the automatic construction of bilingual dictionaries has achieved impressive results. Bilingual dictionaries are usually constructed from parallel corpora, but since these corpora are available only for selected text domains and language pairs, the potential of other resources is being explored as well. In this article, authors want to further pursue the idea of using [[Wikipedia]] as a corpus for bilingual terminology extraction. Authors propose a method that extracts term-translation pairs from different types of Wikipedia link information. After that, an SVM classifier trained on the [[features]] of manually labeled training data determines the correctness of unseen term-translation pairs.<br />
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Erdmann, Maike; Nakayama, Kotaro; Hara, Takahiro; Nishio, Shojiro. (2009). "[[Improving the Extraction of Bilingual Terminology from Wikipedia]]".DOI: 10.1145/1596990.1596995. <br />
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{{cite journal |last1=Erdmann |first1=Maike |last2=Nakayama |first2=Kotaro |last3=Hara |first3=Takahiro |last4=Nishio |first4=Shojiro |title=Improving the Extraction of Bilingual Terminology from Wikipedia |date=2009 |doi=10.1145/1596990.1596995 |url=https://wikipediaquality.com/wiki/Improving_the_Extraction_of_Bilingual_Terminology_from_Wikipedia}}<br />
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Erdmann, Maike; Nakayama, Kotaro; Hara, Takahiro; Nishio, Shojiro. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Improving_the_Extraction_of_Bilingual_Terminology_from_Wikipedia">Improving the Extraction of Bilingual Terminology from Wikipedia</a>&amp;quot;.DOI: 10.1145/1596990.1596995. <br />
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</code></div>Sadiehttps://wikipediaquality.com/index.php?title=Connecting_Wikipedia_and_the_Archive&diff=21449Connecting Wikipedia and the Archive2019-10-19T16:38:55Z<p>Sadie: Infobox</p>
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<div>{{Infobox work<br />
| title = Connecting Wikipedia and the Archive<br />
| date = 2017<br />
| authors = [[Ann Matsuuchi]]<br />
| link = http://wikistudies.org/index.php?journal=wikistudies&amp;page=article&amp;op=view&amp;path%5B%5D=2<br />
}}<br />
'''Connecting Wikipedia and the Archive''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Ann Matsuuchi]].<br />
<br />
== Overview ==<br />
In this essay, Author illustrate a particular instance of how the construction of knowledge can be democratized in a way that simultaneously preserves shared ideas of trust and [[reliability]], via effective collaborations of students, scholars and archivists. The described project that was started in 2015, was collaboratively designed by archivists and historians with the La Guardia & Wagner Archives (“the Archives”) and LaGuardia Community College’s faculty and librarians, and involves beginning college students in the production of a needed public history of the outbreak and impact of HIV/AIDS in New York City. This works demonstrates how community college students can become junior scholars working with primary source archival collections in a manner similar to researchers working on projects as part of institutional fellowships. Utilization of a [[Wikipedia]] as a non-commercial, public, open access information source also succeeds in raising web traffic, visibility and accessibility for unique and valuable archival collections. Collaborations across disciplines and departments and partnerships between people can allow for libraries and archives to take on new roles as new conductors of the inclusive creation of public histories.</div>Sadiehttps://wikipediaquality.com/index.php?title=Dark_Matter_at_5800_an_Investigation_of_the_Quality_of_User-Contributed_Entries_on_the_Topic_of_Dark_Matter_in_Wikipedia_and_Other_Types_of_Texts&diff=21448Dark Matter at 5800 an Investigation of the Quality of User-Contributed Entries on the Topic of Dark Matter in Wikipedia and Other Types of Texts2019-10-19T16:36:24Z<p>Sadie: Overview - Dark Matter at 5800 an Investigation of the Quality of User-Contributed Entries on the Topic of Dark Matter in Wikipedia and Other Types of Texts</p>
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<div>'''Dark Matter at 5800 an Investigation of the Quality of User-Contributed Entries on the Topic of Dark Matter in Wikipedia and Other Types of Texts''' - scientific work related to Wikipedia quality published in 2018, written by Lovisa Aijmer.<br />
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== Overview ==<br />
Statistics have shown that Wikipedia is very frequently used by the general public and that its articles rank high in online search engines. However, the accuracy and general quality of Wikipedia have been debated over the years. This study aims to investigate the quality of Wikipedia by expert reviewers pertaining to the accuracy, currency, breadth, readability, im-ages, structure, neutrality and relevance of a Wikipedia entry on dark matter. The entry has over 5800 edits. A comparison to two other centrally controlled sources, edited by acclaimed experts was also made. Data was collected by asking a number of qualified experts to review and rate three different texts, one published by NASA, one by Encyclopaedia Britannica and one from the English language version of Wikipedia. An interview with one of the experts was also carried out. The results showed that Wikipedia scored better than the other texts in all examined variables except for readability. Wikipedia was the preferred source by all but one panel members and its credibility was considered high. This review indicates that both NASA’s and Encyclopaedia Britannica’s articles on dark matter had a lower degree of quality than expected considering their brands’ high level of credibility. This report encourages the use of Wikipedia both for reference and as a platform to communicate, revise and correct re-search.</div>Sadiehttps://wikipediaquality.com/index.php?title=Tiwiki:_Searching_Wikipedia_with_Temporal_Constraints&diff=21447Tiwiki: Searching Wikipedia with Temporal Constraints2019-10-19T16:34:57Z<p>Sadie: + infobox</p>
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<div>{{Infobox work<br />
| title = Tiwiki: Searching Wikipedia with Temporal Constraints<br />
| date = 2017<br />
| authors = [[Prabal Agarwal]]<br />[[Jannik Strötgen]]<br />
| doi = 10.1145/3041021.3051112<br />
| link = https://dl.acm.org/citation.cfm?id=3051112<br />
}}<br />
'''Tiwiki: Searching Wikipedia with Temporal Constraints''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Prabal Agarwal]] and [[Jannik Strötgen]].<br />
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== Overview ==<br />
Temporal [[information retrieval]] received a lot of attention during the last years and it is, in the meantime, widely accepted in the IR community that temporal information needs are important to tackle. A particular type of temporal queries are those with explicit temporal constraints, which make almost 15% of today's Web search queries. Although several approaches to allow textual search combined with temporal constraints regarding the content of the documents have been suggested, there are no publicly available search engines allowing for a time-centric search experience.</div>Sadiehttps://wikipediaquality.com/index.php?title=Wikipedia_Pages_as_Entry_Points_for_Book_Search&diff=21446Wikipedia Pages as Entry Points for Book Search2019-10-19T16:32:48Z<p>Sadie: + infobox</p>
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<div>{{Infobox work<br />
| title = Wikipedia Pages as Entry Points for Book Search<br />
| date = 2009<br />
| authors = [[Marijn Koolen]]<br />[[Gabriella Kazai]]<br />[[Nick Craswell]]<br />
| doi = 10.1145/1498759.1498807<br />
| link = https://dl.acm.org/citation.cfm?id=1498807<br />
| plink = https://www.researchgate.net/profile/Marijn_Koolen/publication/221520222_Wikipedia_pages_as_entry_points_for_book_search/links/0912f507dab3c289d6000000.pdf<br />
}}<br />
'''Wikipedia Pages as Entry Points for Book Search''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Marijn Koolen]], [[Gabriella Kazai]] and [[Nick Craswell]].<br />
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== Overview ==<br />
A lot of the world's knowledge is stored in books, which, as a result of recent mass-digitisation efforts, are increasingly available online. Search engines, such as [[Google]] Books, provide mechanisms for searchers to enter this vast knowledge space using queries as entry points. In this paper, authors view [[Wikipedia]] as a summary of this world knowledge and aim to use this resource to guide users to relevant books. Thus, authors investigate possible ways of using Wikipedia as an intermediary between the user's query and a collection of books being searched. Authors experiment with traditional query expansion techniques, exploiting Wikipedia articles as rich sources of information that can augment the user's query. Authors then propose a novel approach based on link distance in an extended Wikipedia graph: authors associate books with Wikipedia pages that cite these books and use the link distance between these nodes and the pages that match the user query as an estimation of a book's relevance to the query. Authors results show that a) classical query expansion using terms extracted from query pages leads to increased precision, and b) link distance between query and book pages in Wikipedia provides a good indicator of relevance that can boost the retrieval score of relevant books in the result ranking of a book search engine.</div>Sadiehttps://wikipediaquality.com/index.php?title=Creating,_Destroying,_and_Restoring_Value_in_Wikipedia&diff=21445Creating, Destroying, and Restoring Value in Wikipedia2019-10-19T16:29:46Z<p>Sadie: + Embed</p>
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<div>{{Infobox work<br />
| title = Creating, Destroying, and Restoring Value in Wikipedia<br />
| date = 2007<br />
| authors = [[Reid Priedhorsky]]<br />[[Jilin Chen]]<br />[[Shyong K. Lam]]<br />[[Katherine A. Panciera]]<br />[[Loren G. Terveen]]<br />[[John Riedl]]<br />
| doi = 10.1145/1316624.1316663<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=1316663&amp;type=pdf<br />
}}<br />
'''Creating, Destroying, and Restoring Value in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Reid Priedhorsky]], [[Jilin Chen]], [[Shyong K. Lam]], [[Katherine A. Panciera]], [[Loren G. Terveen]] and [[John Riedl]].<br />
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== Overview ==<br />
Wikipedia's brilliance and curse is that any user can edit any of the encyclopedia entries. Authors introduce the notion of the impact of an edit, measured by the number of times the edited version is viewed. Using several datasets, including recent logs of all article views, authors show that an overwhelming majority of the viewed words were written by frequent editors and that this majority is increasing. Similarly, using the same impact measure, authors show that the probability of a typical article view being damaged is small but increasing, and authors present empirically grounded classes of damage. Finally, authors make policy recommendations for [[Wikipedia]] and other wikis in light of these findings.<br />
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Priedhorsky, Reid; Chen, Jilin; Lam, Shyong K.; Panciera, Katherine A.; Terveen, Loren G.; Riedl, John. (2007). "[[Creating, Destroying, and Restoring Value in Wikipedia]]".DOI: 10.1145/1316624.1316663. <br />
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{{cite journal |last1=Priedhorsky |first1=Reid |last2=Chen |first2=Jilin |last3=Lam |first3=Shyong K. |last4=Panciera |first4=Katherine A. |last5=Terveen |first5=Loren G. |last6=Riedl |first6=John |title=Creating, Destroying, and Restoring Value in Wikipedia |date=2007 |doi=10.1145/1316624.1316663 |url=https://wikipediaquality.com/wiki/Creating,_Destroying,_and_Restoring_Value_in_Wikipedia}}<br />
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Priedhorsky, Reid; Chen, Jilin; Lam, Shyong K.; Panciera, Katherine A.; Terveen, Loren G.; Riedl, John. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Creating,_Destroying,_and_Restoring_Value_in_Wikipedia">Creating, Destroying, and Restoring Value in Wikipedia</a>&amp;quot;.DOI: 10.1145/1316624.1316663. <br />
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<div>'''Enacting Argumentative Web in Semantic Wikipedia''' - scientific work related to Wikipedia quality published in 2010, written by Sergiu Indrie and Adrian Groza.<br />
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== Overview ==<br />
This research advocates the idea of combining argumentation theory with the social web technology, aiming to enact large scale or mass argumentation. The proposed framework allows mass-collaborative editing of structured arguments in the style of semantic wikipedia. The long term goal is to apply the abstract machinery of argumentation theory to more practical applications based on human generated arguments, such as deliberative democracy, business negotiation, or self-care.</div>Sadiehttps://wikipediaquality.com/index.php?title=Wikimmunity:_Fitting_the_Communications_Decency_Act_to_Wikipedia&diff=21443Wikimmunity: Fitting the Communications Decency Act to Wikipedia2019-10-19T16:26:03Z<p>Sadie: Links</p>
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<div>'''Wikimmunity: Fitting the Communications Decency Act to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Ken S. Myers]].<br />
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== Overview ==<br />
Wikipedia is an amazing resource that resembles an online encyclopedia. Critical to its success is its process: it is editable by anyone with access to its content. As the project hurtles forward towards its humbling goal of providing the world with 'free access to the sum of all human knowledge,' questions regarding its veracity and accountability become increasingly insistent and important. In the wake of the Seigenthaler biography controversy, many commentators suggested that [[Wikipedia]] should be able to escape liability for defamatory content pursuant to the immunity provided for in 47 U.S.C. Section 230(c)(1), enacted by Congress as part of the Communications Decency Act of 1996. Unfortunately, those commentators do not provide a detailed roadmap to that conclusion. Additionally, courts interpreting Section 230(c)(1) have not been self-conscious or precise with respect to their choice of several alternative approaches to the ambiguous statutory text of Section 230(c)(1). This Article is an attempt to bridge those gaps by offering a taxonomy of those available analytical approaches while exploring ambiguities relevant to that application in light of Wikipedia's unique facts. Namely, the open contribution model and the accretion-based continuous 'publication' system raise issues heretofore unexplored in Section 230(c)(1) cases: (1) What is an 'entity,' as it is used in the definition of the critical phrase, 'information content provider'? and (2) What is the appropriate level of generality to apply to the term 'information'? While this Article concludes that the Wikipedia will prevail on the basis of Section 230(c)(1) immunity, it offers a few thoughts for shaping its code in the shadow of the law. This paper will be presented in an abbreviated form at the Wikimania 2006 conference in Cambridge, Massachusetts.</div>Sadiehttps://wikipediaquality.com/index.php?title=Uaic%27s_Participation_at_Wikipedia_Retrieval_@_Imageclef_2011&diff=21442Uaic's Participation at Wikipedia Retrieval @ Imageclef 20112019-10-19T16:24:58Z<p>Sadie: Embed</p>
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<div>{{Infobox work<br />
| title = Uaic's Participation at Wikipedia Retrieval @ Imageclef 2011<br />
| date = 2011<br />
| authors = [[Emanuela Boros]]<br />[[Alexandru-Lucian Gînsca]]<br />[[Adrian Iftene]]<br />
| link = http://ceur-ws.org/Vol-1177/CLEF2011wn-ImageCLEF-BorosEt2011.pdf<br />
}}<br />
'''Uaic's Participation at Wikipedia Retrieval @ Imageclef 2011''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Emanuela Boros]], [[Alexandru-Lucian Gînsca]] and [[Adrian Iftene]].<br />
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== Overview ==<br />
This paper describes the participation of UAIC team at the ImageCLEF 2011 competition, [[Wikipedia]] Retrieval task. The aim of the task was to investigate retrieval approaches in the context of a large and heterogeneous collection of images and their noisy text annotations. Authors submitted a total of six runs, focusing effort along the textual retrieval, query expansion on English language, combined with feature extraction (Color and Edge Directionality Descriptor, CEDD). Authors intention was to build a CBIR (Content-based image retrieval) system that relies on a fast indexing and retrieval practice based not only on the textual [[multilingual]] metadata, but also on the images [[features]]. The results were satisfying in the multilingual mixed search (text and images) and query expansion approach.<br />
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{{cite journal |last1=Boros |first1=Emanuela |last2=Gînsca |first2=Alexandru-Lucian |last3=Iftene |first3=Adrian |title=Uaic's Participation at Wikipedia Retrieval @ Imageclef 2011 |date=2011 |url=https://wikipediaquality.com/wiki/Uaic's_Participation_at_Wikipedia_Retrieval_@_Imageclef_2011}}<br />
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Boros, Emanuela; Gînsca, Alexandru-Lucian; Iftene, Adrian. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Uaic's_Participation_at_Wikipedia_Retrieval_@_Imageclef_2011">Uaic's Participation at Wikipedia Retrieval @ Imageclef 2011</a>&amp;quot;.<br />
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<div>'''Framework for Evaluating Credibility of External Links in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Syed Waqar Jaffry]], [[Shahzad Sarwar]], [[Laeeq Aslam]] and [[Muhammad Murtaza Yousaf]].<br />
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== Overview ==<br />
On the advent of Web 2.0, web users have graduated from mere information-consumers and have become information-producers. [[Wikipedia]] is one of the paramount examples of this phenomenon. Open collaborative editing model of Wikipedia allows anyone to contribute information, from anywhere. Hence, general public and particularly researchers are skeptical about the information available at Wikipedia. The whole data set of Wikipedia that ranges from Wikipedia content to editors’ communication is publically available and open to use. Using this information, it seems practical to design models and frameworks to measure authenticity of Wikipedia content. In order to measure authenticity of Wikipedia information, external links play an important role. The presence of external sources or links on a web page to other sites can increase [[credibility]] of information as it allows visitors to cross-check information at external sites. However, there should be some mechanism to validate these external sources. In this work, an External Link Verification framework has been proposed and evaluated on External Links of Wikipedia articles. The proposed framework could be used to compute credibility of an external link of any web page.</div>Sadiehttps://wikipediaquality.com/index.php?title=Metadata_Synchronization_Between_Bilingual_Resources:_Case_Study_in_Wikipedia&diff=21440Metadata Synchronization Between Bilingual Resources: Case Study in Wikipedia2019-10-19T16:21:36Z<p>Sadie: cat.</p>
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<div>{{Infobox work<br />
| title = Metadata Synchronization Between Bilingual Resources: Case Study in Wikipedia<br />
| date = 2010<br />
| authors = [[Eun Kyung Kim]]<br />[[Matthias Weidl]]<br />[[Key-Sun Choi]]<br />
| link = http://ceur-ws.org/Vol-571/paper6.pdf<br />
| plink = https://www.researchgate.net/profile/Key_Sun_Choi/publication/228930822_Metadata_Synchronization_between_Bilingual_Resources_Case_Study_in_Wikipedia/links/53f1f4980cf2f2c3e7fc9e8b.pdf<br />
}}<br />
'''Metadata Synchronization Between Bilingual Resources: Case Study in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Eun Kyung Kim]], [[Matthias Weidl]] and [[Key-Sun Choi]].<br />
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== Overview ==<br />
In this paper, authors present a conceptual study aimed at understanding the impact of international resource synchronization in [[Wikipedia]] and [[DBpedia]]. In the absence of any information synchronization, each country would construct its own datasets and manage it from its users. Moreover the cooperation across the various countries is adversely aected. The solution is based on the analysis of Wikipedia infobox templates and on experimentation such as term translation.<br />
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Kim, Eun Kyung; Weidl, Matthias; Choi, Key-Sun. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Metadata_Synchronization_Between_Bilingual_Resources:_Case_Study_in_Wikipedia">Metadata Synchronization Between Bilingual Resources: Case Study in Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Sadiehttps://wikipediaquality.com/index.php?title=Preparation_of_Topical_Reading_Lists_from_the_Link_Structure_of_Wikipedia&diff=21439Preparation of Topical Reading Lists from the Link Structure of Wikipedia2019-10-19T16:20:26Z<p>Sadie: Adding infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Preparation of Topical Reading Lists from the Link Structure of Wikipedia<br />
| date = 2006<br />
| authors = [[Alexander D. Wissner-Gross]]<br />
| doi = 10.1109/ICALT.2006.1652568<br />
| link = http://www.ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01652568<br />
}}<br />
'''Preparation of Topical Reading Lists from the Link Structure of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Alexander D. Wissner-Gross]].<br />
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== Overview ==<br />
Personalized reading preparation poses an important challenge for education and continuing education. Using a PageRank derivative and graph distance ordering, authors show that personalized background reading lists can be generated automatically from the link structure of [[Wikipedia]]. Authors examine the operation of new tool in professional, student, and interdisciplinary researcher learning models. Additionally, authors present desktop and mobile interfaces for the generated reading lists.</div>Sadiehttps://wikipediaquality.com/index.php?title=Simulating_Collaborative_Writing:_Software_Agents_Produce_a_Wikipedia&diff=21438Simulating Collaborative Writing: Software Agents Produce a Wikipedia2019-10-19T16:19:10Z<p>Sadie: Adding wikilinks</p>
<hr />
<div>'''Simulating Collaborative Writing: Software Agents Produce a Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Klaus G. Troitzsch]].<br />
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== Overview ==<br />
This paper originates from the FP6 project "Emergence in the Loop (EMIL)" which explores the emergence of norms in artificial societies. Part of work package 3 of this project is a simulator that allows for simulation experiments in different scenarios, one of which is collaborative writing. The agents in this still prototypical implementation are able to perform certain actions, such as writing short texts, submitting them to a central collection of texts (the "encyclopaedia") or adding their texts to texts formerly prepared by other agents. At the same time they are able to comment upon others' texts, for instance checking for correct spelling, for double entries in the encyclopaedia or for plagiarisms. Findings of this kind lead to reproaching the original authors of blamable texts. Under certain conditions blamable activities are no longer performed after some time.</div>Sadiehttps://wikipediaquality.com/index.php?title=Information_Extraction_from_Wikipedia_Using_Pattern_Learning&diff=21437Information Extraction from Wikipedia Using Pattern Learning2019-10-19T16:16:48Z<p>Sadie: Adding wikilinks</p>
<hr />
<div>'''Information Extraction from Wikipedia Using Pattern Learning''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Márton Miháltz]].<br />
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== Overview ==<br />
In this paper authors present solutions for the crucial task of extracting [[structured information]] from massive free-text resources, such as [[Wikipedia]], for the sake of semantic databases serving upcoming Semantic Web technologies. Authors demonstrate both a verb frame-based approach using deep [[natural language processing]] techniques with extraction patterns developed by human knowledge experts and machine learning methods using shallow linguistic processing. Authors also propose a method for learning verb frame-based extraction patterns automatically from labeled data. Authors show that labeled training data can be produced with only minimal human effort by utilizing existing semantic resources and the special characteristics of Wikipedia. Custom solutions for [[named entity recognition]] are also possible in this scenario. Authors present evaluation and comparison of the different approaches for several different relations.</div>Sadiehttps://wikipediaquality.com/index.php?title=Extracting_Domain-Relevant_Term_Using_Wikipedia_based_on_Random_Walk_Model&diff=21436Extracting Domain-Relevant Term Using Wikipedia based on Random Walk Model2019-10-19T16:15:03Z<p>Sadie: + category</p>
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<div>{{Infobox work<br />
| title = Extracting Domain-Relevant Term Using Wikipedia based on Random Walk Model<br />
| date = 2012<br />
| authors = [[Wenjuan Wu]]<br />[[Tao Liu]]<br />[[He Hu]]<br />[[Xiaoyong Du]]<br />
| doi = 10.1109/ChinaGrid.2012.20<br />
| link = http://doi.ieeecomputersociety.org/10.1109/ChinaGrid.2012.20<br />
}}<br />
'''Extracting Domain-Relevant Term Using Wikipedia based on Random Walk Model''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Wenjuan Wu]], [[Tao Liu]], [[He Hu]] and [[Xiaoyong Du]].<br />
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== Overview ==<br />
In this paper authors present a new approach for the automatic identification of domain-relevant concepts and entities of a given domain using the category and page structures of the [[Wikipedia]] in a language independent way. By applying Markov random walk algorithm on the weighted Wikipedia link graph, approach can identify large quantities of domain-relevant concepts and entities with very little human effort. Experimental results show that method achieves high accuracy and acceptable efficiency in domain-relevant term extraction.<br />
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Wu, Wenjuan; Liu, Tao; Hu, He; Du, Xiaoyong. (2012). "[[Extracting Domain-Relevant Term Using Wikipedia based on Random Walk Model]]".DOI: 10.1109/ChinaGrid.2012.20. <br />
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Wu, Wenjuan; Liu, Tao; Hu, He; Du, Xiaoyong. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extracting_Domain-Relevant_Term_Using_Wikipedia_based_on_Random_Walk_Model">Extracting Domain-Relevant Term Using Wikipedia based on Random Walk Model</a>&amp;quot;.DOI: 10.1109/ChinaGrid.2012.20. <br />
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[[Category:Scientific works]]</div>Sadiehttps://wikipediaquality.com/index.php?title=Modeling_and_Predicting_Page-View_Dynamics_on_Wikipedia&diff=21435Modeling and Predicting Page-View Dynamics on Wikipedia2019-10-19T16:12:47Z<p>Sadie: Adding wikilinks</p>
<hr />
<div>'''Modeling and Predicting Page-View Dynamics on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Marijn ten Thij]], [[Yana Volkovich]], [[David Laniado]] and [[Andreas Kaltenbrunner]].<br />
<br />
== Overview ==<br />
The simplicity of producing and consuming online content makes it difficult to estimate how much attention will be devoted from Internet users to any given content. This work presents a general overview of temporal patterns in the access to content on a huge collaborative platform. Authors propose a model for predicting the popularity of promoted content, inspired by the analysis of the page-view dynamics on [[Wikipedia]]. Compared to previous studies, the observed popularity patterns are more complex; however, model uses just few parameters to fully describe them. The model is validated through empirical measurements.</div>Sadiehttps://wikipediaquality.com/index.php?title=Reliability_of_Wikipedia_as_a_Medication_Information_Source_for_Pharmacy_Students&diff=21434Reliability of Wikipedia as a Medication Information Source for Pharmacy Students2019-10-19T16:11:42Z<p>Sadie: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Reliability of Wikipedia as a Medication Information Source for Pharmacy Students<br />
| date = 2011<br />
| authors = [[Stacey M. Lavsa]]<br />[[Shelby L. Corman]]<br />[[Colleen M. Culley]]<br />[[Tara L. Pummer]]<br />
| doi = 10.1016/j.cptl.2011.01.007<br />
| link = http://www.sciencedirect.com/science/article/pii/S1877129711000086<br />
}}<br />
'''Reliability of Wikipedia as a Medication Information Source for Pharmacy Students''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Stacey M. Lavsa]], [[Shelby L. Corman]], [[Colleen M. Culley]] and [[Tara L. Pummer]].<br />
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== Overview ==<br />
Abstract Objective To assess the accuracy, [[completeness]], and referencing of medication information in [[Wikipedia]] compared with information found in the manufacturer's package insert. Methods Wikipedia articles for the 20 most frequently prescribed drugs per published lists of top 200 brand and generic drugs were evaluated. Four drug information residency-trained pharmacists independently assessed the articles for specific [[categories]] of information typically found in medication package inserts. Each category was evaluated for presence in the Wikipedia article, accuracy, completeness, and referencing (fully, partially, or none). Package inserts, Micromedex Drugdex Evaluations, Clinical Pharmacology, and Lexi-Comp databases were used to verify accuracy, and completeness was evaluated by comparing article contents to package inserts alone. Results Of the 20 categories of information assessed, a mean of twelve (range, 8–16) categories were present in each of the 20 Wikipedia articles. Categories most frequently absent were drug interactions and medication use in breastfeeding. No article contained all categories of information. Information on contraindications and precautions, drug absorption, and adverse drug events was most frequently found to be inaccurate; descriptions of off-label indications, contraindications and precautions, drug interactions, adverse drug events, and dosing were most frequently incomplete. Referencing was poor across all articles, with seven of the 20 articles not supported by any references. Conclusion Wikipedia does not provide consistently accurate, complete, and referenced medication information. Pharmacy faculty should actively recommend against students' use of Wikipedia for medication information and urge them to consult more credible drug information resources.<br />
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Lavsa, Stacey M.; Corman, Shelby L.; Culley, Colleen M.; Pummer, Tara L.. (2011). "[[Reliability of Wikipedia as a Medication Information Source for Pharmacy Students]]". Elsevier. DOI: 10.1016/j.cptl.2011.01.007. <br />
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{{cite journal |last1=Lavsa |first1=Stacey M. |last2=Corman |first2=Shelby L. |last3=Culley |first3=Colleen M. |last4=Pummer |first4=Tara L. |title=Reliability of Wikipedia as a Medication Information Source for Pharmacy Students |date=2011 |doi=10.1016/j.cptl.2011.01.007 |url=https://wikipediaquality.com/wiki/Reliability_of_Wikipedia_as_a_Medication_Information_Source_for_Pharmacy_Students |journal=Elsevier}}<br />
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Lavsa, Stacey M.; Corman, Shelby L.; Culley, Colleen M.; Pummer, Tara L.. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Reliability_of_Wikipedia_as_a_Medication_Information_Source_for_Pharmacy_Students">Reliability of Wikipedia as a Medication Information Source for Pharmacy Students</a>&amp;quot;. Elsevier. DOI: 10.1016/j.cptl.2011.01.007. <br />
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</code></div>Sadiehttps://wikipediaquality.com/index.php?title=Wikipedia_Usage_Estimates_Prevalence_of_Influenza-Like_Illness_in_Near_Real-Time&diff=21433Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in Near Real-Time2019-10-19T16:09:04Z<p>Sadie: wikilinks</p>
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<div>'''Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in Near Real-Time''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[David J McIver]] and [[John S. Brownstein]].<br />
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== Overview ==<br />
Wikipedia usage data has been harnessed to estimate the prevalence of influenza-like illness (ILI) in the US population. By observing the number of times certain key [[Wikipedia]] articles are viewed each day, a model was developed that accurately estimated ILI, within 0.27% of official Centers for Disease Control and Prevention data. Additionally, this method was able to accurately determine the week in which ILI peaked 17% more often than [[Google]] Flu Trends. This work demonstrates the power of open, freely available data to aid in disease surveillance.</div>Sadiehttps://wikipediaquality.com/index.php?title=Effects_of_Document_Clustering_in_Modeling_Wikipedia-Style_Term_Descriptions&diff=21432Effects of Document Clustering in Modeling Wikipedia-Style Term Descriptions2019-10-19T16:06:16Z<p>Sadie: + links</p>
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<div>'''Effects of Document Clustering in Modeling Wikipedia-Style Term Descriptions''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Atsushi Fujii]], [[Yuya Fujii]] and [[Takenobu Tokunaga]].<br />
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== Overview ==<br />
Reflecting the rapid growth of science, technology, and culture, it has become common practice to consult tools on the World Wide Web for various terms. Existing search engines provide an enormous volume of information, but retrieved information is not organized. Hand-compiled encyclopedias provide organized information, but the quantity of information is limited. In this paper, aiming to integrate the advantages of both tools, authors propose a method to organize a search result based on multiple viewpoints as in [[Wikipedia]]. Because viewpoints required for explanation are different depending on the type of a term, such as animal and disease, authors model articles in Wikipedia to extract a viewpoint structure for each term type. To identify a set of term types, authors independently use manual annotation and automatic document clustering for Wikipedia articles. Authors also propose an effective feature for clustering of Wikipedia articles. Authors experimentally show that the document clustering reduces the cost for the manual annotation while maintaining the accuracy for modeling Wikipedia articles.</div>Sadiehttps://wikipediaquality.com/index.php?title=%E2%80%98Searching_for_a_Centre_That_Holds%E2%80%99_in_the_Network_Society:_Social_Construction_of_Knowledge_On,_and_With,_English_Wikipedia&diff=21431‘Searching for a Centre That Holds’ in the Network Society: Social Construction of Knowledge On, and With, English Wikipedia2019-10-19T16:04:51Z<p>Sadie: + links</p>
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<div>'''‘Searching for a Centre That Holds’ in the Network Society: Social Construction of Knowledge On, and With, English Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Paško Bilić]].<br />
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== Overview ==<br />
The communication model of the network society is not horizontal and flat. Different mediated constructions and centreing performances on new media platforms work towards integrating the symbolic environment, and towards representing the imagined mediated centres. [[Wikipedia]] aspires to become ‘the sum of all human knowledge’. Despite being built on anonymous contributions its underlying dynamic is a process of empirically traceable social construction of knowledge. A case study of [[English Wikipedia]]’s In the news (ITN) section will be presented. Through flexible mediated content production, based on the routinization of the process in policies and guidelines, Wikipedia constructs social centres through consensus-driven media rituals, based on the [[neutral point of view]]. Wikipedia has blurred the border between different types of knowledge in the process of ‘searching for a centre that holds’. It constantly negotiates the border between its internal collaboration and its external symbolic environment.</div>Sadiehttps://wikipediaquality.com/index.php?title=Wikimatch:_Using_Wikipedia_for_Ontology_Matching&diff=21430Wikimatch: Using Wikipedia for Ontology Matching2019-10-19T16:03:46Z<p>Sadie: + Embed</p>
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<div>{{Infobox work<br />
| title = Wikimatch: Using Wikipedia for Ontology Matching<br />
| date = 2012<br />
| authors = [[Sven Hertling]]<br />[[Heiko Paulheim]]<br />
| link = https://dl.acm.org/citation.cfm?id=2887600<br />
}}<br />
'''Wikimatch: Using Wikipedia for Ontology Matching''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Sven Hertling]] and [[Heiko Paulheim]].<br />
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== Overview ==<br />
Finding correspondences between different ontologies is a crucial task in the Semantic Web. Ontology matching tools are capable of solving that task in an automated manner, some even dealing with ontologies in different natural languages. Most state of the art matching tools use internal element and structure based techniques, while the use of large-scale external knowledge resources, especially internet resources, is still rare. In this paper, authors introduce WikiMatch, a matching tool that exploits [[Wikipedia]] as an external knowledge source. Authors show that using Wikipedia is a feasible way of performing [[ontology]] matching, especially if different natural languages are involved.<br />
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Hertling, Sven; Paulheim, Heiko. (2012). "[[Wikimatch: Using Wikipedia for Ontology Matching]]". CEUR-WS.org. <br />
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{{cite journal |last1=Hertling |first1=Sven |last2=Paulheim |first2=Heiko |title=Wikimatch: Using Wikipedia for Ontology Matching |date=2012 |url=https://wikipediaquality.com/wiki/Wikimatch:_Using_Wikipedia_for_Ontology_Matching |journal=CEUR-WS.org}}<br />
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Hertling, Sven; Paulheim, Heiko. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikimatch:_Using_Wikipedia_for_Ontology_Matching">Wikimatch: Using Wikipedia for Ontology Matching</a>&amp;quot;. CEUR-WS.org. <br />
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</code></div>Sadiehttps://wikipediaquality.com/index.php?title=Cultural_Diversity_of_Quality_of_Information_on_Wikipedias&diff=21429Cultural Diversity of Quality of Information on Wikipedias2019-10-19T16:02:21Z<p>Sadie: infobox</p>
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<div>{{Infobox work<br />
| title = Cultural Diversity of Quality of Information on Wikipedias<br />
| date = 2017<br />
| authors = [[Dariusz Jemielniak]]<br />[[Maciej Wilamowski]]<br />
| doi = 10.1002/asi.23901<br />
| link = http://onlinelibrary.wiley.com/doi/10.1002/asi.23901/abstract<br />
}}<br />
'''Cultural Diversity of Quality of Information on Wikipedias''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Dariusz Jemielniak]] and [[Maciej Wilamowski]].<br />
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== Overview ==<br />
This article explores the relationship between linguistic culture and the preferred standards of presenting information based on article representation in major [[Wikipedia]]s. Using primary research analysis of the number of images, references, internal links, external links, words, and characters, as well as their proportions in Good and Featured articles on the eight largest Wikipedias, authors discover a high diversity of approaches and format preferences, correlating with culture. Authors demonstrate that high-quality standards in information presentation are not globally shared and that in many aspects, the language culture's influence determines what is perceived to be proper, desirable, and exemplary for encyclopedic entries. As a result, authors demonstrate that standards for encyclopedic knowledge are not globally agreed-upon and “objective” but local and very subjective.</div>Sadiehttps://wikipediaquality.com/index.php?title=Evaluating_Wiki_Contributions_Using_Social_Networks_:_a_Case_Study_on_Wikipedia&diff=21428Evaluating Wiki Contributions Using Social Networks : a Case Study on Wikipedia2019-10-19T16:00:02Z<p>Sadie: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Evaluating Wiki Contributions Using Social Networks : a Case Study on Wikipedia<br />
| date = 2005<br />
| authors = [[Nikolaos Korfiatis]]<br />[[Ambjoern Naeve]]<br />
| link = http://www.diva-portal.org/smash/record.jsf?pid=diva2:478637<br />
}}<br />
'''Evaluating Wiki Contributions Using Social Networks : a Case Study on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2005, written by [[Nikolaos Korfiatis]] and [[Ambjoern Naeve]].<br />
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== Overview ==<br />
In this paper authors present an approach to the problem of evaluating contribution in shared access repositories such us wikis based on the activity of the contributors as denoted by [[social network]] mea ...</div>Sadiehttps://wikipediaquality.com/index.php?title=Complementary_and_Alternative_Medicine_on_Wikipedia:_Opportunities_for_Improvement&diff=21427Complementary and Alternative Medicine on Wikipedia: Opportunities for Improvement2019-10-19T15:57:56Z<p>Sadie: infobox</p>
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<div>{{Infobox work<br />
| title = Complementary and Alternative Medicine on Wikipedia: Opportunities for Improvement<br />
| date = 2014<br />
| authors = [[Malcolm Koo]]<br />
| doi = 10.1155/2014/105186<br />
| link = https://www.hindawi.com/journals/ecam/2014/105186/<br />
}}<br />
'''Complementary and Alternative Medicine on Wikipedia: Opportunities for Improvement''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Malcolm Koo]].<br />
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== Overview ==<br />
Wikipedia, a free and collaborative Internet encyclopedia, has become one of the most popular sources of free information on the Internet. However, there have been concerns over the quality of online health information, particularly that on complementary and alternative medicine (CAM). This exploratory study aimed to evaluate several page attributes of articles on CAM in the [[English Wikipedia]]. A total of 97 articles were analyzed and compared with eight articles of broad [[categories]] of therapies in conventional medicine using the Mann-Whitney U test. Based on the [[Wikipedia]] editorial assessment grading, 4% of the articles attained “good article” status, 34% required considerable editing, and 56% needed substantial improvements in their content. The median daily access of the articles over the previous 90 days was 372 (range: 7–4,214). The median word count was 1840 with a [[readability]] of grade 12.7 (range: 9.4–17.7). Medians of word count and citation density of the CAM articles were significantly lower than those in the articles of conventional medicine therapies. In conclusion, despite its limitations, the general public will continue to access health information on Wikipedia. There are opportunities for health professionals to contribute their knowledge and to improve the accuracy and [[completeness]] of the CAM articles on Wikipedia.</div>Sadiehttps://wikipediaquality.com/index.php?title=Creating_a_Semantic_Graph_from_Wikipedia&diff=21426Creating a Semantic Graph from Wikipedia2019-10-19T15:55:56Z<p>Sadie: cat.</p>
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<div>{{Infobox work<br />
| title = Creating a Semantic Graph from Wikipedia<br />
| date = 2012<br />
| authors = [[Ryan Tanner]]<br />
| link = http://digitalcommons.trinity.edu/cgi/viewcontent.cgi?article=1028&amp;context=compsci_honors<br />
}}<br />
'''Creating a Semantic Graph from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Ryan Tanner]].<br />
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== Overview ==<br />
With the continued need to organize and automate the use of data, solutions are needed to transform unstructred text into structred information. By treating dependency grammar functions as programming language functions, this process produces “property maps” which connect entities (people, places, events) with snippets of information. These maps are used to construct a semantic graph. By inputting [[Wikipedia]], a large graph of information is produced representing a section of history. The resulting graph allows a user to quickly browse a topic and view the interconnections between entities across history.<br />
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Tanner, Ryan. (2012). "[[Creating a Semantic Graph from Wikipedia]]".<br />
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{{cite journal |last1=Tanner |first1=Ryan |title=Creating a Semantic Graph from Wikipedia |date=2012 |url=https://wikipediaquality.com/wiki/Creating_a_Semantic_Graph_from_Wikipedia}}<br />
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Tanner, Ryan. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Creating_a_Semantic_Graph_from_Wikipedia">Creating a Semantic Graph from Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Sadiehttps://wikipediaquality.com/index.php?title=Comparing_Semantic_Relatedness_Between_Word_Pairs_in_Portuguese_Using_Wikipedia&diff=21425Comparing Semantic Relatedness Between Word Pairs in Portuguese Using Wikipedia2019-10-19T15:53:55Z<p>Sadie: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Comparing Semantic Relatedness Between Word Pairs in Portuguese Using Wikipedia<br />
| date = 2014<br />
| authors = [[Roger Granada]]<br />[[Cássia Trojahn]]<br />[[Renata Vieira]]<br />
| doi = 10.1007/978-3-319-09761-9_17<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-09761-9_17.pdf<br />
}}<br />
'''Comparing Semantic Relatedness Between Word Pairs in Portuguese Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Roger Granada]], [[Cássia Trojahn]] and [[Renata Vieira]].<br />
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== Overview ==<br />
The growth of available data in digital format has been facilitating the development of new models to automatically infer the [[semantic similarity]] between word pairs. However, there are still many natural languages without sufficient resources to evaluate [[measures]] of semantic [[relatedness]]. In this paper authors translated word pairs from a well-known baseline for evaluating semantic relatedness measures into Portuguese and performed a manual evaluation of each pair. Authors compared the correlation with similar datasets in other languages and generated LSA models from [[Wikipedia]] articles in order to verify the pertinence of each dataset and how semantic similarity conveys across languages.</div>Sadiehttps://wikipediaquality.com/index.php?title=Using_Links_to_Classify_Wikipedia_Pages&diff=21424Using Links to Classify Wikipedia Pages2019-10-19T15:51:03Z<p>Sadie: Embed</p>
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<div>{{Infobox work<br />
| title = Using Links to Classify Wikipedia Pages<br />
| date = 2009<br />
| authors = [[Rianne Kaptein]]<br />[[Jaap Kamps]]<br />
| doi = 10.1007/978-3-642-03761-0_44<br />
| link = http://dl.acm.org/citation.cfm?id=1611964<br />
}}<br />
'''Using Links to Classify Wikipedia Pages''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Rianne Kaptein]] and [[Jaap Kamps]].<br />
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== Overview ==<br />
This paper contains a description of experiments for the 2008 INEX XML-mining track. Authors goal for the XML-mining track is to explore whether authors can use link information to improve classification accuracy. Authors approach is to propagate category probabilities over linked pages. Authors find that using link information leads to marginal improvements over a baseline that uses a Naive Bayes model. For the initially misclassified pages, link information is either not available or contains too much noise.<br />
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Kaptein, Rianne; Kamps, Jaap. (2009). "[[Using Links to Classify Wikipedia Pages]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-03761-0_44. <br />
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{{cite journal |last1=Kaptein |first1=Rianne |last2=Kamps |first2=Jaap |title=Using Links to Classify Wikipedia Pages |date=2009 |doi=10.1007/978-3-642-03761-0_44 |url=https://wikipediaquality.com/wiki/Using_Links_to_Classify_Wikipedia_Pages |journal=Springer, Berlin, Heidelberg}}<br />
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Kaptein, Rianne; Kamps, Jaap. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Using_Links_to_Classify_Wikipedia_Pages">Using Links to Classify Wikipedia Pages</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-03761-0_44. <br />
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</code></div>Sadiehttps://wikipediaquality.com/index.php?title=Enabling_Entity_Retrieval_by_Exploiting_Wikipedia_as_a_Semantic_Knowledge_Source&diff=21423Enabling Entity Retrieval by Exploiting Wikipedia as a Semantic Knowledge Source2019-10-19T15:48:48Z<p>Sadie: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Enabling Entity Retrieval by Exploiting Wikipedia as a Semantic Knowledge Source<br />
| date = 2012<br />
| authors = [[Sofia J. Athenikos]]<br />
| doi = 10.1145/2215676.2215687<br />
| link = https://dl.acm.org/citation.cfm?id=2215676.2215687<br />
}}<br />
'''Enabling Entity Retrieval by Exploiting Wikipedia as a Semantic Knowledge Source''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Sofia J. Athenikos]].<br />
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== Overview ==<br />
This dissertation research, PanAnthropon FilmWorld, aims to demonstrate direct retrieval of entities and related facts by exploiting [[Wikipedia]] as a [[semantic knowledge]] source, with the film domain as its proof-of-concept domain of application. To this end, a semantic knowledge base concerning the film domain has been constructed with the data extracted/derived from 10,640 Wikipedia pages on films and additional pages on film awards. The knowledge base currently contains 209,266 entities and 2,345,931 entity-centric facts. Both the knowledge base and the corresponding semantic search interface are based on the coherent classification of entities. Entity-centric facts are also consistently represented as tuples. The semantic search interface (http://dlib.ischool.drexel.edu:8080/sofia/PA/) supports multiple types of semantic search functions, which go beyond the traditional keyword-based search function, including the main General Entity Retrieval Query (GERQ) function, which is concerned with retrieving all entities that match the specified entity type, subtype, and semantic conditions and thus corresponds to the main research problem. Two types of evaluation have been performed in order to evaluate (1) the quality of [[information extraction]] and (2) the effectiveness of [[information retrieval]] using the semantic interface. The first type of evaluation has been performed by inspecting 11,495 film-centric facts concerning 100 films. The results have confirmed high [[data quality]] with 99.96% average precision and 99.84% average recall. The second type of evaluation has been performed by conducting an experiment with human subjects. The experiment involved having the subjects perform a retrieval task by using both the PanAnthropon interface and the Internet Movie Database (IMDb) interface and comparing their task performance between the two interfaces. The results have confirmed higher effectiveness of the PanAnthropon interface vs. the IMDb interface (83.11% vs. 40.78% average precision; 83.55% vs. 40.26% average recall). Moreover, the subjects' responses to the post-task questionnaire indicate that the subjects found the PanAnthropon interface to be highly usable and easily understandable as well as highly effective. The main contribution from this research therefore consists in achieving the set research goal, namely, demonstrating the utility and feasibility of semantics-based direct entity retrieval.<br />
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Athenikos, Sofia J.. (2012). "[[Enabling Entity Retrieval by Exploiting Wikipedia as a Semantic Knowledge Source]]".DOI: 10.1145/2215676.2215687. <br />
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{{cite journal |last1=Athenikos |first1=Sofia J. |title=Enabling Entity Retrieval by Exploiting Wikipedia as a Semantic Knowledge Source |date=2012 |doi=10.1145/2215676.2215687 |url=https://wikipediaquality.com/wiki/Enabling_Entity_Retrieval_by_Exploiting_Wikipedia_as_a_Semantic_Knowledge_Source}}<br />
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Athenikos, Sofia J.. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Enabling_Entity_Retrieval_by_Exploiting_Wikipedia_as_a_Semantic_Knowledge_Source">Enabling Entity Retrieval by Exploiting Wikipedia as a Semantic Knowledge Source</a>&amp;quot;.DOI: 10.1145/2215676.2215687. <br />
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</code></div>Sadiehttps://wikipediaquality.com/index.php?title=Biases_in_the_Production_and_Reception_of_Collective_Knowledge:_the_Case_of_Hindsight_Bias_in_Wikipedia&diff=21422Biases in the Production and Reception of Collective Knowledge: the Case of Hindsight Bias in Wikipedia2019-10-19T15:47:44Z<p>Sadie: + infobox</p>
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<div>{{Infobox work<br />
| title = Biases in the Production and Reception of Collective Knowledge: the Case of Hindsight Bias in Wikipedia<br />
| date = 2018<br />
| authors = [[Aileen Oeberst]]<br />[[Ina von der Beck]]<br />[[Mitja D. Back]]<br />[[Ulrike Cress]]<br />[[Steffen Nestler]]<br />
| doi = 10.1007/s00426-017-0865-7<br />
| link = https://link.springer.com/article/10.1007%2Fs00426-017-0865-7<br />
}}<br />
'''Biases in the Production and Reception of Collective Knowledge: the Case of Hindsight Bias in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Aileen Oeberst]], [[Ina von der Beck]], [[Mitja D. Back]], [[Ulrike Cress]] and [[Steffen Nestler]].<br />
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== Overview ==<br />
The Web 2.0 enabled collaboration at an unprecedented level. In one of the flagships of mass collaboration—[[Wikipedia]]—a large number of authors socially negotiate the world’s largest compendium of knowledge. Several guidelines in Wikipedia restrict contributions to verifiable information from reliable sources to ensure recognized knowledge. Much psychological research demonstrates, however, that individual information processing is biased. This poses the question whether individual biases translate to Wikipedia articles or whether they are prevented by its guidelines. The present research makes use of hindsight bias to examine this question. To this end, authors analyzed foresight and hindsight versions of Wikipedia articles regarding a broad variety of events (Study 1). Authors found the majority of articles not to contain traces of hindsight bias—contrary to prior individual research. However, for a particular category of events—disasters—we found robust evidence for hindsight bias. In a lab experiment (Study 2), authors then examined whether individuals’ hindsight bias is translated into articles under controlled conditions and tested whether collaborative writing—as present in Wikipedia—affects the resultant bias (vs. individual writing). Finally, authors investigated the impact of biased Wikipedia articles on readers (Study 3). As predicted, biased articles elicited a hindsight bias in readers, who had not known of the event previously. Moreover, biased articles also affected individuals who knew about the event already, and who had already developed a hindsight bias: biased articles further increased their hindsight.</div>Sadiehttps://wikipediaquality.com/index.php?title=Query_Translation_Using_Wikipedia-Based_Resources_for_Analysis_and_Disambiguation&diff=21421Query Translation Using Wikipedia-Based Resources for Analysis and Disambiguation2019-10-19T15:45:30Z<p>Sadie: Creating a page: Query Translation Using Wikipedia-Based Resources for Analysis and Disambiguation</p>
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<div>'''Query Translation Using Wikipedia-Based Resources for Analysis and Disambiguation''' - scientific work related to Wikipedia quality published in 2010, written by Benoît Gaillard, Orange Labs, Malek Boualem and Olivier Collin.<br />
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== Overview ==<br />
This work investigates query translation using only Wikipedia-based resources in a two step approach: analysis and disam- biguation. After arguing that data mined from Wikipedia is particularly relevant to query translation, both from a lexical and a semantic perspective, authors detail the im- plementation of the approach. In the analysis phase, lexical units are extracted from queries and associated to several possible translations using a Wikipedia- based bilingual dictionary. During the second phase, one translation is chosen amongst the many candidates, based on topic homogeneity, asserted with the help of semantic information carried by cate- gories of Wikipedia articles. Authors report promising results regarding translation accuracy.</div>Sadiehttps://wikipediaquality.com/index.php?title=Who_is_It%3F_Context_Sensitive_Named_Entity_and_Instance_Recognition_by_Means_of_Wikipedia&diff=21420Who is It? Context Sensitive Named Entity and Instance Recognition by Means of Wikipedia2019-10-19T15:42:51Z<p>Sadie: Categories</p>
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<div>{{Infobox work<br />
| title = Who is It? Context Sensitive Named Entity and Instance Recognition by Means of Wikipedia<br />
| date = 2008<br />
| authors = [[Ulli Waltinger]]<br />[[Alexander Mehler]]<br />
| doi = 10.1109/WIIAT.2008.421<br />
| link = https://dl.acm.org/citation.cfm?id=1486927.1487081<br />
}}<br />
'''Who is It? Context Sensitive Named Entity and Instance Recognition by Means of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Ulli Waltinger]] and [[Alexander Mehler]].<br />
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== Overview ==<br />
This paper presents an approach for predicting context sensitive entities exemplified in the domain of person names. Authors approach is based on building a weighted context but also a weighted people graph and predicting the context entity by extracting the best fitting sub graph using a spreading activation technique. The results of the experiments show a quite promising F-Measure of 0.99.<br />
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Waltinger, Ulli; Mehler, Alexander. (2008). "[[Who is It? Context Sensitive Named Entity and Instance Recognition by Means of Wikipedia]]".DOI: 10.1109/WIIAT.2008.421. <br />
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{{cite journal |last1=Waltinger |first1=Ulli |last2=Mehler |first2=Alexander |title=Who is It? Context Sensitive Named Entity and Instance Recognition by Means of Wikipedia |date=2008 |doi=10.1109/WIIAT.2008.421 |url=https://wikipediaquality.com/wiki/Who_is_It?_Context_Sensitive_Named_Entity_and_Instance_Recognition_by_Means_of_Wikipedia}}<br />
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Waltinger, Ulli; Mehler, Alexander. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/Who_is_It?_Context_Sensitive_Named_Entity_and_Instance_Recognition_by_Means_of_Wikipedia">Who is It? Context Sensitive Named Entity and Instance Recognition by Means of Wikipedia</a>&amp;quot;.DOI: 10.1109/WIIAT.2008.421. <br />
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[[Category:Scientific works]]</div>Sadiehttps://wikipediaquality.com/index.php?title=Changes_in_College_Students%27_Perceptions_of_Use_of_Web-Based_Resources_for_Academic_Tasks_with_Wikipedia_Projects:_a_Preliminary_Exploration&diff=20903Changes in College Students' Perceptions of Use of Web-Based Resources for Academic Tasks with Wikipedia Projects: a Preliminary Exploration2019-10-04T05:13:56Z<p>Sadie: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Changes in College Students' Perceptions of Use of Web-Based Resources for Academic Tasks with Wikipedia Projects: a Preliminary Exploration<br />
| date = 2014<br />
| authors = [[Tomoko Traphagan]]<br />[[John W. Traphagan]]<br />[[Linda Neavel Dickens]]<br />[[Paul Resta]]<br />
| doi = 10.1080/10494820.2011.641685<br />
| link = https://www.sciencedirect.com/science/article/pii/S0920548915000197<br />
| plink = https://www.researchgate.net/profile/Linda_Dickens/publication/254220521_Changes_in_college_students&#039;_perceptions_of_use_of_web-based_resources_for_academic_tasks_with_Wikipedia_projects_a_preliminary_exploration/links/55f0342d08ae199d47c116ab.pdf<br />
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'''Changes in College Students' Perceptions of Use of Web-Based Resources for Academic Tasks with Wikipedia Projects: a Preliminary Exploration''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Tomoko Traphagan]], [[John W. Traphagan]], [[Linda Neavel Dickens]] and [[Paul Resta]].<br />
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== Overview ==<br />
Motivated by the need to facilitate Net Generation students' information literacy (IL), or more specifically, to promote student understanding of legitimate, effective use of Web-based resources, this exploratory study investigated how analyzing, writing, posting, and monitoring [[Wikipedia]] entries might help students develop critical perspectives related to the legitimacy of Wikipedia and other publicly accessible Web-based resources for academic tasks. Results of survey and interview data analyses from two undergraduate courses indicated that undergraduate students typically prefer using publicly accessible Web-based resources to traditional academic resources, such as scholarly journal articles and books both in print and digital form; furthermore, they view the former as helpful academic tools with various utilities. Results also suggest that the Wikipedia activity, integrated into regular course curriculum, led students to gain knowledge about processes of Web-based information creation, become more cr...</div>Sadiehttps://wikipediaquality.com/index.php?title=Intelligence_in_Wikipedia&diff=20902Intelligence in Wikipedia2019-10-04T05:11:23Z<p>Sadie: Category</p>
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<div>{{Infobox work<br />
| title = Intelligence in Wikipedia<br />
| date = 2008<br />
| authors = [[Daniel S. Weld]]<br />[[Fei Wu]]<br />[[Eytan Adar]]<br />[[Saleema Amershi]]<br />[[James Fogarty]]<br />[[Raphael Hoffmann]]<br />[[Kayur Patel]]<br />[[Michael Skinner]]<br />
| link = http://dl.acm.org/citation.cfm?id=1620270.1620344<br />
}}<br />
'''Intelligence in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Daniel S. Weld]], [[Fei Wu]], [[Eytan Adar]], [[Saleema Amershi]], [[James Fogarty]], [[Raphael Hoffmann]], [[Kayur Patel]] and [[Michael Skinner]].<br />
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== Overview ==<br />
The Intelligence in [[Wikipedia]] project at the University of Washington is combining self-supervised [[information extraction]] (IE) techniques with a mixed initiative interface designed to encourage communal content creation (CCC). Since IE and CCC are each powerful ways to produce large amounts of [[structured information]], they have been studied extensively — but only in isolation. By combining the two methods in a virtuous feedback cycle, authors aim for substantial synergy. While previous papers have described the details of individual aspects of endeavor [25, 26, 24, 13], this report provides an overview of the project’s progress and vision.<br />
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Weld, Daniel S.; Wu, Fei; Adar, Eytan; Amershi, Saleema; Fogarty, James; Hoffmann, Raphael; Patel, Kayur; Skinner, Michael. (2008). "[[Intelligence in Wikipedia]]". AAAI Press. <br />
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{{cite journal |last1=Weld |first1=Daniel S. |last2=Wu |first2=Fei |last3=Adar |first3=Eytan |last4=Amershi |first4=Saleema |last5=Fogarty |first5=James |last6=Hoffmann |first6=Raphael |last7=Patel |first7=Kayur |last8=Skinner |first8=Michael |title=Intelligence in Wikipedia |date=2008 |url=https://wikipediaquality.com/wiki/Intelligence_in_Wikipedia |journal=AAAI Press}}<br />
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Weld, Daniel S.; Wu, Fei; Adar, Eytan; Amershi, Saleema; Fogarty, James; Hoffmann, Raphael; Patel, Kayur; Skinner, Michael. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/Intelligence_in_Wikipedia">Intelligence in Wikipedia</a>&amp;quot;. AAAI Press. <br />
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[[Category:Scientific works]]</div>Sadiehttps://wikipediaquality.com/index.php?title=A_Technique_for_Suggesting_Related_Wikipedia_Articles_Using_Link_Analysis&diff=20901A Technique for Suggesting Related Wikipedia Articles Using Link Analysis2019-10-04T05:10:11Z<p>Sadie: Adding embed</p>
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<div>{{Infobox work<br />
| title = A Technique for Suggesting Related Wikipedia Articles Using Link Analysis<br />
| date = 2012<br />
| authors = [[Christopher Markson]]<br />[[Min Song]]<br />
| doi = 10.1145/2232817.2232883<br />
| link = https://dl.acm.org/ft_gateway.cfm?id=2232883&amp;type=pdf<br />
}}<br />
'''A Technique for Suggesting Related Wikipedia Articles Using Link Analysis''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Christopher Markson]] and [[Min Song]].<br />
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== Overview ==<br />
With more than 3.7 million articles, [[Wikipedia]] has become an important social medium for sharing knowledge. However, with this enormous repository of information, it can often be difficult to locate fundamental topics that support lower-level articles. By exploiting the information stored in the links between articles, authors propose that related companion articles can be automatically generated to help further the reader's understanding of a given topic. This approach to a recommendation system uses tested link analysis techniques to present users with a clear path to related high-level articles, furthering the understanding of low-level topics.<br />
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Markson, Christopher; Song, Min. (2012). "[[A Technique for Suggesting Related Wikipedia Articles Using Link Analysis]]".DOI: 10.1145/2232817.2232883. <br />
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{{cite journal |last1=Markson |first1=Christopher |last2=Song |first2=Min |title=A Technique for Suggesting Related Wikipedia Articles Using Link Analysis |date=2012 |doi=10.1145/2232817.2232883 |url=https://wikipediaquality.com/wiki/A_Technique_for_Suggesting_Related_Wikipedia_Articles_Using_Link_Analysis}}<br />
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Markson, Christopher; Song, Min. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Technique_for_Suggesting_Related_Wikipedia_Articles_Using_Link_Analysis">A Technique for Suggesting Related Wikipedia Articles Using Link Analysis</a>&amp;quot;.DOI: 10.1145/2232817.2232883. <br />
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</code></div>Sadiehttps://wikipediaquality.com/index.php?title=Building_a_Biomedical_Semantic_Network_in_Wikipedia_with_Semantic_Wiki_Links&diff=20900Building a Biomedical Semantic Network in Wikipedia with Semantic Wiki Links2019-10-04T05:09:09Z<p>Sadie: + wikilinks</p>
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<div>'''Building a Biomedical Semantic Network in Wikipedia with Semantic Wiki Links''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Benjamin M. Good]], [[Erik L. Clarke]], [[Salvatore Loguercio]] and [[Andrew I. Su]].<br />
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== Overview ==<br />
Wikipedia is increasingly used as a platform for collaborative data curation, but its current technical implementation has significant limitations that hinder its use in biocuration applications. Specifically, while editors can easily link between two articles in [[Wikipedia]] to indicate a relationship, there is no way to indicate the nature of that relationship in a way that is computationally accessible to the system or to external developers. For example, in addition to noting a relationship between a gene and a disease, it would be useful to differentiate the cases where genetic mutation or altered expression causes the disease. Here, authors introduce a straightforward method that allows [[Wikipedia editors]] to embed computable semantic relations directly in the context of current Wikipedia articles. In addition, authors demonstrate two novel applications enabled by the presence of these new relationships. The first is a dynamically generated information box that can be rendered on all semantically enhanced Wikipedia articles. The second is a prototype gene annotation system that draws its content from the gene-centric articles on Wikipedia and exposes the new semantic relationships to enable previously impossible, user-defined queries.</div>Sadie