https://wikipediaquality.com/api.php?action=feedcontributions&user=Natalia&feedformat=atomWikipedia Quality - User contributions [en]2024-03-29T14:51:41ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Connecting_Every_Bit_of_Knowledge:_the_Structure_of_Wikipedia%E2%80%99s_First_Link_Network&diff=25610Connecting Every Bit of Knowledge: the Structure of Wikipedia’s First Link Network2020-10-18T08:58:43Z<p>Natalia: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Connecting Every Bit of Knowledge: the Structure of Wikipedia’s First Link Network<br />
| date = 2017<br />
| authors = [[Mark Ibrahim]]<br />[[Christopher M. Danforth]]<br />[[Peter Sheridan Dodds]]<br />
| doi = 10.1016/j.jocs.2016.12.001<br />
| link = http://www.sciencedirect.com/science/article/pii/S1877750316304471<br />
| plink = https://www.arxiv.org/pdf/1605.00309v2<br />
}}<br />
'''Connecting Every Bit of Knowledge: the Structure of Wikipedia’s First Link Network''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Mark Ibrahim]], [[Christopher M. Danforth]] and [[Peter Sheridan Dodds]].<br />
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== Overview ==<br />
Abstract Apples, porcupines, and the most obscure Bob Dylan song—is every topic a few clicks from Philosophy? Within [[Wikipedia]], the surprising answer is yes: nearly all paths lead to Philosophy. Wikipedia is the largest, most meticulously indexed collection of human knowledge ever amassed. More than information about a topic, Wikipedia is a web of naturally emerging relationships. By following the first link in each article, authors algorithmically construct a directed network of all 4.7 million articles: Wikipedia's First Link Network. Here, authors study the English edition of Wikipedia's First Link Network for insight into how the many articles on inventions, places, people, objects, and events are related and organized. By traversing every path, authors measure the accumulation of first links, path lengths, groups of path-connected articles, and cycles. Authors also develop a new method, traversal funnels, to measure the influence each article exerts in shaping the network. Traversal funnels provide a new measure of influence for directed networks without spill-over into cycles, in contrast to traditional network centrality [[measures]]. Within Wikipedia's First Link Network, authors find scale-free distributions describe path length, accumulation, and influence. Far from dispersed, first links disproportionately accumulate at a few articles—flowing from specific to general and culminating around fundamental notions such as Community, State, and Science. Philosophy directs more paths than any other article by two orders of magnitude. Authors also observe a gravitation toward topical articles such as Health Care and Fossil Fuel. These findings enrich view of the connections and structure of Wikipedia's ever growing store of knowledge.</div>Nataliahttps://wikipediaquality.com/index.php?title=Ontologies_Derived_from_Wikipedia_:_a_Framework_for_Comparison&diff=25609Ontologies Derived from Wikipedia : a Framework for Comparison2020-10-18T08:57:05Z<p>Natalia: Categories</p>
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<div>{{Infobox work<br />
| title = Ontologies Derived from Wikipedia : a Framework for Comparison<br />
| date = 2010<br />
| authors = [[Alejandro Metke-Jimenez]]<br />[[Kerry Raymond]]<br />[[Ian MacColl]]<br />
| link = http://eprints.qut.edu.au/38432/<br />
}}<br />
'''Ontologies Derived from Wikipedia : a Framework for Comparison''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Alejandro Metke-Jimenez]], [[Kerry Raymond]] and [[Ian MacColl]].<br />
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== Overview ==<br />
Since its debut in 2001 [[Wikipedia]] has attracted the attention of many researchers in different fields. In recent years researchers in the area of [[ontology]] learning have realised the huge potential of Wikipedia as a source of semi-structured knowledge and several systems have used it as their main source of knowledge. However, the techniques used to extract [[semantic information]] vary greatly, as do the resulting ontologies. This paper introduces a framework to compare ontology learning systems that use Wikipedia as their main source of knowledge. Six prominent systems are compared and contrasted using the framework.<br />
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Metke-Jimenez, Alejandro; Raymond, Kerry; MacColl, Ian. (2010). "[[Ontologies Derived from Wikipedia : a Framework for Comparison]]". SciTePress. <br />
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{{cite journal |last1=Metke-Jimenez |first1=Alejandro |last2=Raymond |first2=Kerry |last3=MacColl |first3=Ian |title=Ontologies Derived from Wikipedia : a Framework for Comparison |date=2010 |url=https://wikipediaquality.com/wiki/Ontologies_Derived_from_Wikipedia_:_a_Framework_for_Comparison |journal=SciTePress}}<br />
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Metke-Jimenez, Alejandro; Raymond, Kerry; MacColl, Ian. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Ontologies_Derived_from_Wikipedia_:_a_Framework_for_Comparison">Ontologies Derived from Wikipedia : a Framework for Comparison</a>&amp;quot;. SciTePress. <br />
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[[Category:Scientific works]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Local_and_Global_Algorithms_for_Disambiguation_to_Wikipedia&diff=25608Local and Global Algorithms for Disambiguation to Wikipedia2020-10-18T08:54:44Z<p>Natalia: cat.</p>
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<div>{{Infobox work<br />
| title = Local and Global Algorithms for Disambiguation to Wikipedia<br />
| date = 2011<br />
| authors = [[Lev-Arie Ratinov]]<br />[[Dan Roth]]<br />[[Doug Downey]]<br />[[Michael R. Anderson]]<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=2002642&amp;type=pdf<br />
}}<br />
'''Local and Global Algorithms for Disambiguation to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Lev-Arie Ratinov]], [[Dan Roth]], [[Doug Downey]] and [[Michael R. Anderson]].<br />
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== Overview ==<br />
Disambiguating concepts and entities in a context sensitive way is a fundamental problem in [[natural language processing]]. The comprehensiveness of [[Wikipedia]] has made the online encyclopedia an increasingly popular target for disambiguation. Disambiguation to Wikipedia is similar to a traditional Word Sense Disambiguation task, but distinct in that the Wikipedia link structure provides additional information about which disambiguations are compatible. In this work authors analyze approaches that utilize this information to arrive at coherent sets of disambiguations for a given document (which authors call "global" approaches), and compare them to more traditional (local) approaches. Authors show that previous approaches for global disambiguation can be improved, but even then the local disambiguation provides a baseline which is very hard to beat.<br />
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Ratinov, Lev-Arie; Roth, Dan; Downey, Doug; Anderson, Michael R.. (2011). "[[Local and Global Algorithms for Disambiguation to Wikipedia]]". Association for Computational Linguistics. <br />
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{{cite journal |last1=Ratinov |first1=Lev-Arie |last2=Roth |first2=Dan |last3=Downey |first3=Doug |last4=Anderson |first4=Michael R. |title=Local and Global Algorithms for Disambiguation to Wikipedia |date=2011 |url=https://wikipediaquality.com/wiki/Local_and_Global_Algorithms_for_Disambiguation_to_Wikipedia |journal=Association for Computational Linguistics}}<br />
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Ratinov, Lev-Arie; Roth, Dan; Downey, Doug; Anderson, Michael R.. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Local_and_Global_Algorithms_for_Disambiguation_to_Wikipedia">Local and Global Algorithms for Disambiguation to Wikipedia</a>&amp;quot;. Association for Computational Linguistics. <br />
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[[Category:Scientific works]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Faculty_Perception_of_Wikipedia_in_the_California_State_University_System&diff=25607Faculty Perception of Wikipedia in the California State University System2020-10-18T08:52:12Z<p>Natalia: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Faculty Perception of Wikipedia in the California State University System<br />
| date = 2015<br />
| authors = [[Aline Soules]]<br />
| doi = 10.1108/NLW-08-2014-0096<br />
| link = http://www.emeraldinsight.com/doi/abs/10.1108/NLW-08-2014-0096<br />
}}<br />
'''Faculty Perception of Wikipedia in the California State University System''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Aline Soules]].<br />
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== Overview ==<br />
Purpose – The purpose of this paper is to explore whether faculty perceptions of [[Wikipedia]] have changed over a five-year period. Design/methodology/approach – A survey was conducted of four universities in the California State University System – California State University, East Bay; Humboldt State University; Cal Poly San Luis Obispo; and California State University, Fresno. Following the survey, respondents who volunteered their contact information were interviewed about their perceptions and/or their assignments/projects involving Wikipedia. Findings – The study showed that, overall, faculty perceptions of Wikipedia have shifted in Wikipedia’s favor and that some faculty members create interesting and unique assignments that involve Wikipedia or Wikipedia-like work. Research limitations/implications – This study sampled 4 of 23 campuses in the California State University System. Practical implications – The growing acceptance of Wikipedia has implications for course work with students both in terms of...<br />
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Soules, Aline. (2015). "[[Faculty Perception of Wikipedia in the California State University System]]". Emerald Group Publishing Limited. DOI: 10.1108/NLW-08-2014-0096. <br />
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{{cite journal |last1=Soules |first1=Aline |title=Faculty Perception of Wikipedia in the California State University System |date=2015 |doi=10.1108/NLW-08-2014-0096 |url=https://wikipediaquality.com/wiki/Faculty_Perception_of_Wikipedia_in_the_California_State_University_System |journal=Emerald Group Publishing Limited}}<br />
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Soules, Aline. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Faculty_Perception_of_Wikipedia_in_the_California_State_University_System">Faculty Perception of Wikipedia in the California State University System</a>&amp;quot;. Emerald Group Publishing Limited. DOI: 10.1108/NLW-08-2014-0096. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Utilization_of_Dbpedia_Mapping_in_Cross_Lingual_Wikipedia_Infobox_Completion&diff=25606Utilization of Dbpedia Mapping in Cross Lingual Wikipedia Infobox Completion2020-10-18T08:49:42Z<p>Natalia: Embed</p>
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<div>{{Infobox work<br />
| title = Utilization of Dbpedia Mapping in Cross Lingual Wikipedia Infobox Completion<br />
| date = 2016<br />
| authors = [[Megawati]]<br />[[Saemi Jang]]<br />[[Mun Yong Yi]]<br />
| doi = 10.1007/978-3-319-50127-7_25<br />
| link = https://link.springer.com/chapter/10.1007/978-3-319-50127-7_25/fulltext.html<br />
}}<br />
'''Utilization of Dbpedia Mapping in Cross Lingual Wikipedia Infobox Completion''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Megawati]], [[Saemi Jang]] and [[Mun Yong Yi]].<br />
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== Overview ==<br />
Wikipedia plays a central role in the web as one of the biggest knowledge source due to its large coverage of information that comes from various domains. However, due to the enormous number of pages and limited number of contributors to maintain all of the pages, the problem of missing information among [[Wikipedia]] articles has emerged, especially articles in multiple [[language versions]]. Several approaches have been studied to fix information gap in between cross- language Wikipedia articles. However, they can only be applied for languages that came from the same root. In this paper, authors propose an approach to generate new information for Wikipedia [[infoboxes]] written in [[different language]]s with different roots by utilizing the existing [[DBpedia]] mappings. Authors combined mapping information from DBpedia with an instance-based method to align the existing Korean-English infobox attribute-value pairs as well as to generate new pairs from the Korean version to fill missing information in the English version. The results showed that authors could expand up to 38% of the existing [[English Wikipedia]] attribute-value pairs from datasets with 61% of accuracy.<br />
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Megawati, ; Jang, Saemi; Yi, Mun Yong. (2016). "[[Utilization of Dbpedia Mapping in Cross Lingual Wikipedia Infobox Completion]]". Springer, Cham. DOI: 10.1007/978-3-319-50127-7_25. <br />
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{{cite journal |last1=Megawati |first1= |last2=Jang |first2=Saemi |last3=Yi |first3=Mun Yong |title=Utilization of Dbpedia Mapping in Cross Lingual Wikipedia Infobox Completion |date=2016 |doi=10.1007/978-3-319-50127-7_25 |url=https://wikipediaquality.com/wiki/Utilization_of_Dbpedia_Mapping_in_Cross_Lingual_Wikipedia_Infobox_Completion |journal=Springer, Cham}}<br />
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Megawati, ; Jang, Saemi; Yi, Mun Yong. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Utilization_of_Dbpedia_Mapping_in_Cross_Lingual_Wikipedia_Infobox_Completion">Utilization of Dbpedia Mapping in Cross Lingual Wikipedia Infobox Completion</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-50127-7_25. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Understanding_User%27s_Query_Intent_with_Wikipedia&diff=25605Understanding User's Query Intent with Wikipedia2020-10-18T08:47:20Z<p>Natalia: + links</p>
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<div>'''Understanding User's Query Intent with Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Jian Hu]], [[Gang Wang]], [[Frederick H. Lochovsky]], [[Jian-Tao Sun]] and [[Zheng Chen]].<br />
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== Overview ==<br />
Understanding the intent behind a user's query can help search engine to automatically route the query to some corresponding vertical search engines to obtain particularly relevant contents, thus, greatly improving user satisfaction. There are three major challenges to the query intent classification problem: (1) Intent representation; (2) Domain coverage and (3) Semantic interpretation. Current approaches to predict the user's intent mainly utilize machine learning techniques. However, it is difficult and often requires many human efforts to meet all these challenges by the statistical machine learning approaches. In this paper, authors propose a general methodology to the problem of query intent classification. With very little human effort, method can discover large quantities of intent concepts by leveraging [[Wikipedia]], one of the best human knowledge base. The Wikipedia concepts are used as the intent representation space, thus, each intent domain is represented as a set of Wikipedia articles and [[categories]]. The intent of any input query is identified through mapping the query into the Wikipedia representation space. Compared with previous approaches, proposed method can achieve much better coverage to classify queries in an intent domain even through the number of seed intent examples is very small. Moreover, the method is very general and can be easily applied to various intent domains. Authors demonstrate the effectiveness of this method in three different applications, i.e., travel, job, and person name. In each of the three cases, only a couple of seed intent queries are provided. Authors perform the quantitative evaluations in comparison with two baseline methods, and the experimental results shows that method significantly outperforms other methods in each intent domain.</div>Nataliahttps://wikipediaquality.com/index.php?title=Cirgirgdisco_at_Replab2014_Reputation_Dimension_Task:_Using_Wikipedia_Graph_Structure_for_Classifying_the_Reputation_Dimension_of_a_Tweet&diff=25604Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet2020-10-18T08:44:53Z<p>Natalia: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet<br />
| date = 2014<br />
| authors = [[Muhammad Atif Qureshi]]<br />[[Arjumand Younus]]<br />[[Colm O'Riordan]]<br />[[Gabriella Pasi]]<br />
| link = http://ceur-ws.org/Vol-1180/CLEF2014wn-Rep-QureshiEt2014.pdf<br />
}}<br />
'''Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Muhammad Atif Qureshi]], [[Arjumand Younus]], [[Colm O'Riordan]] and [[Gabriella Pasi]].<br />
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== Overview ==<br />
Social media repositories serve as a significant source of ev- idence when extracting information related to the [[reputation]] of a par- ticular entity (e.g., a particular politician, singer or company). Reputa- tion management experts manually mine the social media repositories (in particular [[Twitter]]) for monitoring the reputation of a particular en- tity. Recently, the online reputation management evaluation campaign known as RepLab at CLEF has turned attention to devising computa- tional methods for facilitating reputation management experts. A quite significant research challenge related to the above issue is to classify the reputation dimension of tweets with respect to entity names. More specifically, finding various aspects of a brand's reputation is an impor- tant task which can help companies in monitoring areas of their strengths and weaknesses in an effective manner. To address this issue in this paper authors use dominant [[Wikipedia categories]] related to a reputation dimension in a random forest classifier. Additionally authors also use tweet-specific fea- tures, language-specific [[features]] and similarity-based features. The ex- perimental evaluations show a significant improvement over the baseline accuracy.</div>Nataliahttps://wikipediaquality.com/index.php?title=Completing_Wikipedia%27s_Hyperlink_Structure_Through_Dimensionality_Reduction&diff=25603Completing Wikipedia's Hyperlink Structure Through Dimensionality Reduction2020-10-18T08:43:23Z<p>Natalia: Infobox work</p>
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<div>{{Infobox work<br />
| title = Completing Wikipedia's Hyperlink Structure Through Dimensionality Reduction<br />
| date = 2009<br />
| authors = [[Robert West]]<br />[[Doina Precup]]<br />[[Joelle Pineau]]<br />
| doi = 10.1145/1645953.1646093<br />
| link = http://dl.acm.org/citation.cfm?id=1646093<br />
| plink = https://www.researchgate.net/profile/Doina_Precup/publication/221613523_Completing_wikipedia%27s_hyperlink_structure_through_dimensionality_reduction/links/0912f506316e240416000000.pdf<br />
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'''Completing Wikipedia's Hyperlink Structure Through Dimensionality Reduction''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Robert West]], [[Doina Precup]] and [[Joelle Pineau]].<br />
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== Overview ==<br />
Wikipedia is the largest monolithic repository of human knowledge. In addition to its sheer size, it represents a new encyclopedic paradigm by interconnecting articles through hyperlinks. However, since these links are created by human authors, links one would expect to see are often missing. The goal of this work is to detect such gaps automatically. In this paper, authors propose a novel method for augmenting the structure of hyperlinked document collections such as [[Wikipedia]]. It does not require the extraction of any manually defined [[features]] from the article to be augmented. Instead, it is based on principal component analysis, a well-founded mathematical generalization technique, and predicts new links purely based on the statistical structure of the graph formed by the existing links. Authors method does not rely on the textual content of articles; authors are exploiting only hyperlinks. A user evaluation of technique shows that it improves the quality of top link suggestions over the state of the art and that the best predicted links are significantly more valuable than the 'average' link already present in Wikipedia. Beyond link prediction, algorithm can potentially be used to point out topics an article misses to cover and to cluster articles semantically.</div>Nataliahttps://wikipediaquality.com/index.php?title=Agent_Simulation_of_Collaborative_Knowledge_Processing_in_Wikipedia&diff=25602Agent Simulation of Collaborative Knowledge Processing in Wikipedia2020-10-18T08:41:07Z<p>Natalia: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Agent Simulation of Collaborative Knowledge Processing in Wikipedia<br />
| date = 2008<br />
| authors = [[Jinsheng Xu]]<br />[[Levent Yilmaz]]<br />[[Jinghua Zhang]]<br />
| link = https://dl.acm.org/citation.cfm?id=1400553<br />
}}<br />
'''Agent Simulation of Collaborative Knowledge Processing in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Jinsheng Xu]], [[Levent Yilmaz]] and [[Jinghua Zhang]].<br />
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== Overview ==<br />
Wikipedia, a User Innovation Community (UIC), is becoming increasingly influential source of knowledge. The knowledge in [[Wikipedia]] is produced and processed collaboratively by UIC. The results of this collaboration process present various seemingly complex patterns demonstrated by update history of different articles in Wikipedia. Agent simulation is a powerful method that is used to study the behaviors of complex systems of interacting and autonomous agents. In this paper, authors study the collaborative knowledge processing in Wikipedia using a simple agent-based model. The proposed model considers factors including knowledge distribution among agents, number of agents, behavior of agents and vandalism. Authors use this model to explain content growth rate, number and frequency of updates, edit war and vandalism in Wikipedia articles. The results demonstrate that the model captures the important empirical aspects in collaborative knowledge processing in Wikipedia.<br />
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Xu, Jinsheng; Yilmaz, Levent; Zhang, Jinghua. (2008). "[[Agent Simulation of Collaborative Knowledge Processing in Wikipedia]]". Society for Computer Simulation International. <br />
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{{cite journal |last1=Xu |first1=Jinsheng |last2=Yilmaz |first2=Levent |last3=Zhang |first3=Jinghua |title=Agent Simulation of Collaborative Knowledge Processing in Wikipedia |date=2008 |url=https://wikipediaquality.com/wiki/Agent_Simulation_of_Collaborative_Knowledge_Processing_in_Wikipedia |journal=Society for Computer Simulation International}}<br />
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Xu, Jinsheng; Yilmaz, Levent; Zhang, Jinghua. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/Agent_Simulation_of_Collaborative_Knowledge_Processing_in_Wikipedia">Agent Simulation of Collaborative Knowledge Processing in Wikipedia</a>&amp;quot;. Society for Computer Simulation International. <br />
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<div>'''Using Wikipedia Technology for Topic Maps Design''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Junghoon Yang]], [[Jangwhan Han]], [[Inseok Oh]] and [[Mingyung Kwak]].<br />
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== Overview ==<br />
In this paper authors present a method for automatically generating collection of topics from [[Wikipedia]]/Wikibooks based on user input. The constructed collection is intended to be displayed through an intuitive interface as assistance to the user creating Topic Maps for a given subject. Authors discuss the motivation behind the developed tool and outline the technique used for crawling and collecting relevant concepts from Wikipedia/Wikibooks and for building the topic structure to be output to the user.</div>Nataliahttps://wikipediaquality.com/index.php?title=Coarse_to_Fine_Grained_Sense_Disambiguation_in_Wikipedia&diff=24719Coarse to Fine Grained Sense Disambiguation in Wikipedia2020-06-18T05:42:27Z<p>Natalia: cats.</p>
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<div>{{Infobox work<br />
| title = Coarse to Fine Grained Sense Disambiguation in Wikipedia<br />
| date = 2013<br />
| authors = [[Hui Shen]]<br />[[Razvan C. Bunescu]]<br />[[Rada Mihalcea]]<br />
| link = http://aclweb.org/anthology/S/S13/S13-1003.pdf<br />
}}<br />
'''Coarse to Fine Grained Sense Disambiguation in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Hui Shen]], [[Razvan C. Bunescu]] and [[Rada Mihalcea]].<br />
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== Overview ==<br />
Wikipedia articles are annotated by volunteer contributors with numerous links that connect words and phrases to relevant titles. Links to general senses of a word are used concurrently with links to more specific senses, without being distinguished explicitly. Authors present an approach to training coarse to fine grained sense disambiguation systems in the presence of such annotation inconsistencies. Experimental results show that accounting for annotation ambiguity in [[Wikipedia]] links leads to significant improvements in disambiguation.<br />
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{{cite journal |last1=Shen |first1=Hui |last2=Bunescu |first2=Razvan C. |last3=Mihalcea |first3=Rada |title=Coarse to Fine Grained Sense Disambiguation in Wikipedia |date=2013 |url=https://wikipediaquality.com/wiki/Coarse_to_Fine_Grained_Sense_Disambiguation_in_Wikipedia}}<br />
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Shen, Hui; Bunescu, Razvan C.; Mihalcea, Rada. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Coarse_to_Fine_Grained_Sense_Disambiguation_in_Wikipedia">Coarse to Fine Grained Sense Disambiguation in Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Hypernyms_Through_Intra-Article_Organization_in_Wikipedia&diff=24718Hypernyms Through Intra-Article Organization in Wikipedia2020-06-18T05:41:17Z<p>Natalia: Adding embed</p>
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<div>{{Infobox work<br />
| title = Hypernyms Through Intra-Article Organization in Wikipedia<br />
| date = 2018<br />
| authors = [[Disha Shrivastava]]<br />[[Sreyash Kenkre]]<br />[[Santosh Penubothula]]<br />
| link = https://econpapers.repec.org/paper/nadwpaper/20180012.htm<br />
| plink = http://arxiv.org/pdf/1809.00414.pdf<br />
}}<br />
'''Hypernyms Through Intra-Article Organization in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Disha Shrivastava]], [[Sreyash Kenkre]] and [[Santosh Penubothula]].<br />
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== Overview ==<br />
Authors introduce a new measure for unsupervised hypernym detection and directionality. The motivation is to keep the measure computationally light and portatable across languages. Authors show that the relative physical location of words in explanatory articles captures the directionality property. Further, the phrases in section titles of articles about the word, capture the [[semantic similarity]] needed for hypernym detection task. Authors experimentally show that the combination of [[features]] coming from these two simple [[measures]] suffices to produce results comparable with the best unsupervised measures in terms of the average precision.<br />
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Shrivastava, Disha; Kenkre, Sreyash; Penubothula, Santosh. (2018). "[[Hypernyms Through Intra-Article Organization in Wikipedia]]".<br />
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{{cite journal |last1=Shrivastava |first1=Disha |last2=Kenkre |first2=Sreyash |last3=Penubothula |first3=Santosh |title=Hypernyms Through Intra-Article Organization in Wikipedia |date=2018 |url=https://wikipediaquality.com/wiki/Hypernyms_Through_Intra-Article_Organization_in_Wikipedia}}<br />
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Shrivastava, Disha; Kenkre, Sreyash; Penubothula, Santosh. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Hypernyms_Through_Intra-Article_Organization_in_Wikipedia">Hypernyms Through Intra-Article Organization in Wikipedia</a>&amp;quot;.<br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Semantic_Stability_in_Wikipedia&diff=24717Semantic Stability in Wikipedia2020-06-18T05:39:36Z<p>Natalia: Links</p>
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<div>'''Semantic Stability in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Darko Stanisavljevic]], [[Ilire Hasani-Mavriqi]], [[Elisabeth Lex]], [[Markus Strohmaier]] and [[Denis Helic]].<br />
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== Overview ==<br />
In this paper authors assess the semantic stability of [[Wikipedia]] by investigating the dynamics of Wikipedia articles’ revisions over time. In a semantically stable system, articles are infrequently edited, whereas in unstable systems, article content changes more frequently. In other words, in a stable system, the [[Wikipedia community]] has reached consensus on the majority of articles. In work, authors measure semantic stability using the Rank Biased Overlap method. To that end, authors preprocess Wikipedia dumps to obtain a sequence of plain-text article revisions, whereas each revision is represented as a TF-IDF vector. To measure the similarity between consequent article revisions, authors calculate Rank Biased Overlap on subsequent term vectors. Authors evaluate approach on 10 Wikipedia language editions including the five largest language editions as well as five randomly selected small language editions. Authors experimental results reveal that even in policy driven collaboration networks such as Wikipedia, semantic stability can be achieved. However, there are differences on the velocity of the semantic stability process between small and large Wikipedia editions. Small editions exhibit faster and higher semantic stability than large ones. In particular, in large Wikipedia editions, a higher number of successive revisions is needed in order to reach a certain semantic stability level, whereas, in small Wikipedia editions, the number of needed successive revisions is much lower for the same level of semantic stability.</div>Nataliahttps://wikipediaquality.com/index.php?title=Learning_to_Tag_and_Tagging_to_Learn:_a_Case_Study_on_Wikipedia&diff=24716Learning to Tag and Tagging to Learn: a Case Study on Wikipedia2020-06-18T05:37:52Z<p>Natalia: + wikilinks</p>
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<div>'''Learning to Tag and Tagging to Learn: a Case Study on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Peter Mika]], [[Massimiliano Ciaramita]], [[Hugo Zaragoza]] and [[Jordi Atserias]].<br />
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== Overview ==<br />
The problem of semantically annotating [[Wikipedia]] inspires a novel method for dealing with domain and task adaptation of semantic taggers in cases where parallel text and metadata are available.</div>Nataliahttps://wikipediaquality.com/index.php?title=Detecting_Vandalism_on_Wikipedia_Across_Multiple_Languages&diff=24715Detecting Vandalism on Wikipedia Across Multiple Languages2020-06-18T05:35:58Z<p>Natalia: Cats.</p>
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<div>{{Infobox work<br />
| title = Detecting Vandalism on Wikipedia Across Multiple Languages<br />
| date = 2015<br />
| authors = [[Khoi-Nguyen Dao Tran]]<br />
| link = https://openresearch-repository.anu.edu.au/bitstream/1885/14453/5/Tran%20Thesis%202015.pdf<br />
}}<br />
'''Detecting Vandalism on Wikipedia Across Multiple Languages''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Khoi-Nguyen Dao Tran]].<br />
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== Overview ==<br />
Vandalism, the malicious modification or editing of articles, is a serious problem for free and open access online encyclopedias such as [[Wikipedia]]. Over the 13 year lifetime of Wikipedia, editors have identified and repaired vandalism in 1.6% of more than 500 million revisions of over 9 million English articles, but smaller manually inspected sets of revisions for research show vandalism may appear in 7% to 11% of all revisions of [[English Wikipedia]] articles. The persistent threat of vandalism has led to the development of automated programs (bots) and editing assistance programs to help editors detect and repair vandalism. Research into improving vandalism detection through application of machine learning techniques have shown significant improvements to detection rates of a wider variety of vandalism. However, the focus of research is often only on the English Wikipedia, which has led us to develop a novel research area of cross-language vandalism detection (CLVD). CLVD provides a solution to detecting vandalism across several languages through the development of language-independent machine learning models. These models can identify undetected vandalism cases across languages that may have insufficient identified cases to build learning models. The two main challenges of CLVD are (1) identifying language-independent [[features]] of vandalism that are common to [[multiple languages]], and (2) extensibility of vandalism detection models trained in one language to other languages without significant loss in detection rate. In addition, other important challenges of vandalism detection are (3) high detection rate of a variety of known vandalism types, (4) scalability to the size of Wikipedia in the number of revisions, and (5) ability to incorporate and generate multiple types of data that characterise vandalism. In this thesis, authors present research into CLVD on Wikipedia, where authors identify gaps and problems in existing vandalism detection techniques. To begin thesis, authors introduce the problem of vandalism on Wikipedia with motivating examples, and then present a review of the literature. From this review, authors identify and address the following research gaps. First, authors propose techniques for summarising the user activity of articles and comparing the knowledge coverage of articles across languages. Second, authors investigate CLVD using the metadata of article revisions together with article views to learn vandalism models and classify incoming revisions. Third, authors propose new text features that are more suitable for CLVD than text features from the literature. Fourth, authors propose a novel context-aware vandalism detection technique for sneaky types of vandalism that may not be detectable through constructing features. Finally, to show that techniques of detecting malicious activities are not limited to Wikipedia, authors apply feature sets to detecting malicious attachments and URLs in spam emails. Overall, ultimate aim is to build the next generation of vandalism detection bots that can learn and detect vandalism from multiple languages and extend their usefulness to other language editions of Wikipedia.<br />
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Tran, Khoi-Nguyen Dao. (2015). "[[Detecting Vandalism on Wikipedia Across Multiple Languages]]". The Australian National University. <br />
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{{cite journal |last1=Tran |first1=Khoi-Nguyen Dao |title=Detecting Vandalism on Wikipedia Across Multiple Languages |date=2015 |url=https://wikipediaquality.com/wiki/Detecting_Vandalism_on_Wikipedia_Across_Multiple_Languages |journal=The Australian National University}}<br />
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Tran, Khoi-Nguyen Dao. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Detecting_Vandalism_on_Wikipedia_Across_Multiple_Languages">Detecting Vandalism on Wikipedia Across Multiple Languages</a>&amp;quot;. The Australian National University. <br />
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[[Category:Scientific works]]<br />
[[Category:English Wikipedia]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Wikipedia_Can_Help_Give_News_Junkies_Their_Fix&diff=24714Wikipedia Can Help Give News Junkies Their Fix2020-06-18T05:33:05Z<p>Natalia: Links</p>
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<div>'''Wikipedia Can Help Give News Junkies Their Fix''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Paul Marks]].<br />
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== Overview ==<br />
Software can tell you when big news breaks by looking at what the online encyclopedia's legions of editors are up to</div>Nataliahttps://wikipediaquality.com/index.php?title=Extracting_Imperatives_from_Wikipedia_Article_for_Deletion_Discussions&diff=24713Extracting Imperatives from Wikipedia Article for Deletion Discussions2020-06-18T05:31:43Z<p>Natalia: + embed code</p>
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<div>{{Infobox work<br />
| title = Extracting Imperatives from Wikipedia Article for Deletion Discussions<br />
| date = 2014<br />
| authors = [[Fiona Mao]]<br />[[Robert E. Mercer]]<br />[[Lu Xiao]]<br />
| doi = 10.3115/v1/W14-2117<br />
| link = http://aclweb.org/anthology/W/W14/W14-2117.pdf<br />
}}<br />
'''Extracting Imperatives from Wikipedia Article for Deletion Discussions''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Fiona Mao]], [[Robert E. Mercer]] and [[Lu Xiao]].<br />
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== Overview ==<br />
Wikipedia contains millions of articles, collaboratively produced. If an article is controversial, an online “Article for Deletion” (AfD) discussion is held to determine whether the article should be deleted. It is open to any user to participate and make a comment or argue an opinion. Some of these comments and arguments can be counter-arguments, attacks in Dung’s (1995) argumentation terminology. Here, authors consider the extraction of one type of attack, the directive speech act formed as an imperative.<br />
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Mao, Fiona; Mercer, Robert E.; Xiao, Lu. (2014). "[[Extracting Imperatives from Wikipedia Article for Deletion Discussions]]".DOI: 10.3115/v1/W14-2117. <br />
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{{cite journal |last1=Mao |first1=Fiona |last2=Mercer |first2=Robert E. |last3=Xiao |first3=Lu |title=Extracting Imperatives from Wikipedia Article for Deletion Discussions |date=2014 |doi=10.3115/v1/W14-2117 |url=https://wikipediaquality.com/wiki/Extracting_Imperatives_from_Wikipedia_Article_for_Deletion_Discussions}}<br />
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Mao, Fiona; Mercer, Robert E.; Xiao, Lu. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extracting_Imperatives_from_Wikipedia_Article_for_Deletion_Discussions">Extracting Imperatives from Wikipedia Article for Deletion Discussions</a>&amp;quot;.DOI: 10.3115/v1/W14-2117. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Linking_Entities_in_Tweets_to_Wikipedia_Knowledge_Base&diff=24712Linking Entities in Tweets to Wikipedia Knowledge Base2020-06-18T05:29:34Z<p>Natalia: + cat.</p>
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<div>{{Infobox work<br />
| title = Linking Entities in Tweets to Wikipedia Knowledge Base<br />
| date = 2014<br />
| authors = [[Xianqi Zou]]<br />[[Chengjie Sun]]<br />[[Yaming Sun]]<br />[[Bingquan Liu]]<br />[[Lei Lin]]<br />
| doi = 10.1007/978-3-662-45924-9_33<br />
| link = https://link.springer.com/chapter/10.1007%2F978-3-662-45924-9_33<br />
}}<br />
'''Linking Entities in Tweets to Wikipedia Knowledge Base''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Xianqi Zou]], [[Chengjie Sun]], [[Yaming Sun]], [[Bingquan Liu]] and [[Lei Lin]].<br />
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== Overview ==<br />
Entity linking has received much more attention. The purpose of entity linking is to link the mentions in the text to the corresponding entities in the knowledge base. Most work of entity linking is aiming at long texts, such as BBS or blog. Microblog as a new kind of social platform, entity linking in which will face many problems. In this paper, authors divide the entity linking task into two parts. The first part is entity candidates' generation and feature extrac- tion. Authors use [[Wikipedia]] articles information to generate enough entity candi- dates, and as far as possible eliminate ambiguity candidates to get higher coverage and less quantity. In terms of feature, authors adopt belief propagation, which is based on the topic distribution, to get global feature. The experiment results show that method achieves better performance than that based on common links. When combining global [[features]] with local features, the perfor- mance will be obviously improved. The second part is entity candidates ranking. Traditional learning to rank methods have been widely used in entity linking task. However, entity linking does not consider the ranking order of non-target entities. Thus, authors utilize a boosting algorithm of non-ranking method to predict the target entity, which leads to 77.48% accuracy.<br />
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Zou, Xianqi; Sun, Chengjie; Sun, Yaming; Liu, Bingquan; Lin, Lei. (2014). "[[Linking Entities in Tweets to Wikipedia Knowledge Base]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-662-45924-9_33. <br />
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{{cite journal |last1=Zou |first1=Xianqi |last2=Sun |first2=Chengjie |last3=Sun |first3=Yaming |last4=Liu |first4=Bingquan |last5=Lin |first5=Lei |title=Linking Entities in Tweets to Wikipedia Knowledge Base |date=2014 |doi=10.1007/978-3-662-45924-9_33 |url=https://wikipediaquality.com/wiki/Linking_Entities_in_Tweets_to_Wikipedia_Knowledge_Base |journal=Springer, Berlin, Heidelberg}}<br />
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Zou, Xianqi; Sun, Chengjie; Sun, Yaming; Liu, Bingquan; Lin, Lei. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Linking_Entities_in_Tweets_to_Wikipedia_Knowledge_Base">Linking Entities in Tweets to Wikipedia Knowledge Base</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-662-45924-9_33. <br />
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[[Category:Scientific works]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Intellectual_Interchanges_in_the_History_of_the_Massive_Online_Open-Editing_Encyclopedia,_Wikipedia&diff=24711Intellectual Interchanges in the History of the Massive Online Open-Editing Encyclopedia, Wikipedia2020-06-18T05:28:01Z<p>Natalia: Adding categories</p>
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<div>{{Infobox work<br />
| title = Intellectual Interchanges in the History of the Massive Online Open-Editing Encyclopedia, Wikipedia<br />
| date = 2016<br />
| authors = [[Jinhyuk Yun]]<br />[[Sang Hoon Lee]]<br />[[Hawoong Jeong]]<br />[[Hawoong Jeong]]<br />
| doi = 10.1103/PhysRevE.93.012307<br />
| link = http://dl.acm.org/citation.cfm?id=2833089<br />
| plink = http://export.arxiv.org/abs/1510.06092<br />
}}<br />
'''Intellectual Interchanges in the History of the Massive Online Open-Editing Encyclopedia, Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Jinhyuk Yun]], [[Sang Hoon Lee]], [[Hawoong Jeong]] and [[Hawoong Jeong]].<br />
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== Overview ==<br />
Wikipedia is a free Internet encyclopedia with an enormous amount of content. This encyclopedia is written by volunteers with various backgrounds in a collective fashion; anyone can access and edit most of the articles. This open-editing nature may give us prejudice that [[Wikipedia]] is an unstable and unreliable source; yet many studies suggest that Wikipedia is even more accurate and self-consistent than traditional encyclopedias. Scholars have attempted to understand such extraordinary [[credibility]], but usually used the number of edits as the unit of time, without consideration of real time. In this work, authors probe the formation of such collective intelligence through a systematic analysis using the entire history of 34534110 [[English Wikipedia]] articles, between 2001 and 2014. From this massive data set, authors observe the universality of both timewise and lengthwise editing scales, which suggests that it is essential to consider the real-time dynamics. By considering real time, authors find the existence of distinct growth patterns that are unobserved by utilizing the number of edits as the unit of time. To account for these results, authors present a mechanistic model that adopts the article editing dynamics based on both editor-editor andeditor-articleinteractions.ThemodelsuccessfullygeneratesthekeypropertiesofrealWikipediaarticlessuch as distinct types of articles for the editing patterns characterized by the interrelationship between the numbers of edits and editors, and the article size. In addition, the model indicates that infrequently referred articles tend to grow faster than frequently referred ones, and articles attracting a high motivation to edit counterintuitively reduce the number of participants. Authors suggest that this decay of participants eventually brings inequality among the editors, which will become more severe with time.<br />
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Yun, Jinhyuk; Lee, Sang Hoon; Jeong, Hawoong; Jeong, Hawoong. (2016). "[[Intellectual Interchanges in the History of the Massive Online Open-Editing Encyclopedia, Wikipedia]]". Phys Rev E. DOI: 10.1103/PhysRevE.93.012307. <br />
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{{cite journal |last1=Yun |first1=Jinhyuk |last2=Lee |first2=Sang Hoon |last3=Jeong |first3=Hawoong |last4=Jeong |first4=Hawoong |title=Intellectual Interchanges in the History of the Massive Online Open-Editing Encyclopedia, Wikipedia |date=2016 |doi=10.1103/PhysRevE.93.012307 |url=https://wikipediaquality.com/wiki/Intellectual_Interchanges_in_the_History_of_the_Massive_Online_Open-Editing_Encyclopedia,_Wikipedia |journal=Phys Rev E}}<br />
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Yun, Jinhyuk; Lee, Sang Hoon; Jeong, Hawoong; Jeong, Hawoong. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Intellectual_Interchanges_in_the_History_of_the_Massive_Online_Open-Editing_Encyclopedia,_Wikipedia">Intellectual Interchanges in the History of the Massive Online Open-Editing Encyclopedia, Wikipedia</a>&amp;quot;. Phys Rev E. DOI: 10.1103/PhysRevE.93.012307. <br />
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[[Category:English Wikipedia]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Categorizing_Search_Results_Using_Wordnet_and_Wikipedia&diff=24710Categorizing Search Results Using Wordnet and Wikipedia2020-06-18T05:25:00Z<p>Natalia: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Categorizing Search Results Using Wordnet and Wikipedia<br />
| date = 2012<br />
| authors = [[Reza Taghizadeh Hemayati]]<br />[[Weiyi Meng]]<br />[[Clement T. Yu]]<br />
| doi = 10.1007/978-3-642-32281-5_18<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-642-32281-5_18.pdf<br />
}}<br />
'''Categorizing Search Results Using Wordnet and Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Reza Taghizadeh Hemayati]], [[Weiyi Meng]] and [[Clement T. Yu]].<br />
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== Overview ==<br />
Terms used in search queries often have multiple meanings and usages. Consequently, search results corresponding to different meanings or usages may be retrieved, making identifying relevant results inconvenient and time-consuming. In this paper, authors study the problem of grouping the search results based on the different meanings and usages of a query. Authors build on a previous work that identifies and ranks possible [[categories]] of any user query based on the meanings and common usages of the terms and phrases within the query. Authors use these categories to group search results. In this paper, authors study different methods, including several new methods, to assign search result record (SRRs) to the categories. Authors SRR grouping framework supports a combination of categorization, clustering and query rewriting techniques. Authors experimental results show that some of grouping methods can achieve high accuracy.<br />
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Hemayati, Reza Taghizadeh; Meng, Weiyi; Yu, Clement T.. (2012). "[[Categorizing Search Results Using Wordnet and Wikipedia]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-32281-5_18. <br />
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{{cite journal |last1=Hemayati |first1=Reza Taghizadeh |last2=Meng |first2=Weiyi |last3=Yu |first3=Clement T. |title=Categorizing Search Results Using Wordnet and Wikipedia |date=2012 |doi=10.1007/978-3-642-32281-5_18 |url=https://wikipediaquality.com/wiki/Categorizing_Search_Results_Using_Wordnet_and_Wikipedia |journal=Springer, Berlin, Heidelberg}}<br />
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Hemayati, Reza Taghizadeh; Meng, Weiyi; Yu, Clement T.. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Categorizing_Search_Results_Using_Wordnet_and_Wikipedia">Categorizing Search Results Using Wordnet and Wikipedia</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-32281-5_18. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Hot_Off_the_Wiki_Structures_and_Dynamics_of_Wikipedia%E2%80%99s_Coverage_of_Breaking_News_Events&diff=24709Hot Off the Wiki Structures and Dynamics of Wikipedia’s Coverage of Breaking News Events2020-06-18T05:23:46Z<p>Natalia: Embed</p>
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<div>{{Infobox work<br />
| title = Hot Off the Wiki Structures and Dynamics of Wikipedia’s Coverage of Breaking News Events<br />
| date = 2013<br />
| authors = [[Brian Keegan]]<br />[[Darren Gergle]]<br />[[Noshir Contractor]]<br />
| doi = 10.1177/0002764212469367<br />
| link = https://www.scholars.northwestern.edu/en/publications/hot-off-the-wiki-structures-and-dynamics-of-wikipedias-coverage-o<br />
}}<br />
'''Hot Off the Wiki Structures and Dynamics of Wikipedia’s Coverage of Breaking News Events''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Brian Keegan]], [[Darren Gergle]] and [[Noshir Contractor]].<br />
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== Overview ==<br />
Wikipedia’s coverage of breaking news and current events dominates editor contributions and reader attention in any given month. Collaborators on breaking news articles rapidly synthesize content to produce timely information in spite of steep coordination demands. [[Wikipedia]]’s coverage of breaking news events thus presents a case to test theories about how open collaborations coordinate complex, time-sensitive, and knowledge-intensive work in the absence of central authority, stable membership, clear roles, or reliable information. Using the revision history from Wikipedia articles about over 3,000 breaking news events, authors investigate the structure of interactions between editors and articles. Because breaking article collaborations unfold more rapidly and involve more editors than most Wikipedia articles, they potentially regenerate prior forms of organizing. Authors analyze whether the structures of breaking and nonbreaking article networks are (a) similarly structured over time, (b) exhibit [[features]] of orga...<br />
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Keegan, Brian; Gergle, Darren; Contractor, Noshir. (2013). "[[Hot Off the Wiki Structures and Dynamics of Wikipedia’s Coverage of Breaking News Events]]". SAGE Publications. DOI: 10.1177/0002764212469367. <br />
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{{cite journal |last1=Keegan |first1=Brian |last2=Gergle |first2=Darren |last3=Contractor |first3=Noshir |title=Hot Off the Wiki Structures and Dynamics of Wikipedia’s Coverage of Breaking News Events |date=2013 |doi=10.1177/0002764212469367 |url=https://wikipediaquality.com/wiki/Hot_Off_the_Wiki_Structures_and_Dynamics_of_Wikipedia’s_Coverage_of_Breaking_News_Events |journal=SAGE Publications}}<br />
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Keegan, Brian; Gergle, Darren; Contractor, Noshir. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Hot_Off_the_Wiki_Structures_and_Dynamics_of_Wikipedia’s_Coverage_of_Breaking_News_Events">Hot Off the Wiki Structures and Dynamics of Wikipedia’s Coverage of Breaking News Events</a>&amp;quot;. SAGE Publications. DOI: 10.1177/0002764212469367. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Improving_Credibility_Evaluations_on_Wikipedia&diff=24708Improving Credibility Evaluations on Wikipedia2020-06-18T05:22:00Z<p>Natalia: Embed</p>
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<div>{{Infobox work<br />
| title = Improving Credibility Evaluations on Wikipedia<br />
| date = 2011<br />
| authors = [[T. Lucassen]]<br />[[Martin Schmettow]]<br />[[Caro H. Wiering]]<br />[[Jules M. Pieters]]<br />[[Henk Boer]]<br />
| link = https://research.utwente.nl/en/publications/improving-credibility-evaluations-on-wikipedia<br />
}}<br />
'''Improving Credibility Evaluations on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[T. Lucassen]], [[Martin Schmettow]], [[Caro H. Wiering]], [[Jules M. Pieters]] and [[Henk Boer]].<br />
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== Overview ==<br />
In this chapter, ongoing research on trust in [[Wikipedia]] is used as a case study to illustrate the design process of a support tool for Wikipedia, following the ASCE-model. This research is performed from a cognitive perspective and aims at users actively evaluating the [[credibility]] of information on Wikipedia on an article basis rather than passively relying on their trust in the source of the information (Wikipedia as a whole).<br />
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Lucassen, T.; Schmettow, Martin; Wiering, Caro H.; Pieters, Jules M.; Boer, Henk. (2011). "[[Improving Credibility Evaluations on Wikipedia]]". Universiteit Twente. <br />
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{{cite journal |last1=Lucassen |first1=T. |last2=Schmettow |first2=Martin |last3=Wiering |first3=Caro H. |last4=Pieters |first4=Jules M. |last5=Boer |first5=Henk |title=Improving Credibility Evaluations on Wikipedia |date=2011 |url=https://wikipediaquality.com/wiki/Improving_Credibility_Evaluations_on_Wikipedia |journal=Universiteit Twente}}<br />
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Lucassen, T.; Schmettow, Martin; Wiering, Caro H.; Pieters, Jules M.; Boer, Henk. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Improving_Credibility_Evaluations_on_Wikipedia">Improving Credibility Evaluations on Wikipedia</a>&amp;quot;. Universiteit Twente. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=What_Wikipedia_Deletes:_Characterizing_Dangerous_Collaborative_Content&diff=24707What Wikipedia Deletes: Characterizing Dangerous Collaborative Content2020-06-18T05:19:23Z<p>Natalia: + infobox</p>
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<div>{{Infobox work<br />
| title = What Wikipedia Deletes: Characterizing Dangerous Collaborative Content<br />
| date = 2011<br />
| authors = [[Andrew G. West]]<br />[[Insup Lee]]<br />
| doi = 10.1145/2038558.2038563<br />
| link = http://dl.acm.org/citation.cfm?id=2038563<br />
}}<br />
'''What Wikipedia Deletes: Characterizing Dangerous Collaborative Content''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Andrew G. West]] and [[Insup Lee]].<br />
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== Overview ==<br />
Collaborative environments, such as [[Wikipedia]], often have low barriers-to-entry in order to encourage participation. This accessibility is frequently abused ( e.g ., vandalism and spam). However, certain inappropriate behaviors are more threatening than others. In this work, authors study contributions which are not simply "undone" -- but deleted from revision histories and public view. Such treatment is generally reserved for edits which: (1) present a legal liability to the host ( e.g ., copyright issues, defamation), or (2) present privacy threats to individuals ( i.e ., contact information). Herein, authors analyze one year of Wikipedia's public deletion log and use brute-force strategies to learn about privately handled redactions. This permits insight about the prevalence of deletion, the reasons that induce it, and the extent of end-user exposure to dangerous content. While Wikipedia's approach is generally quite reactive, authors find that copyright issues prove most problematic of those behaviors studied.</div>Nataliahttps://wikipediaquality.com/index.php?title=A_Research_for_the_Centrality_of_Article_Edit_Collective_in_Wikipedia&diff=24706A Research for the Centrality of Article Edit Collective in Wikipedia2020-06-18T05:17:55Z<p>Natalia: cats.</p>
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<div>{{Infobox work<br />
| title = A Research for the Centrality of Article Edit Collective in Wikipedia<br />
| date = 2011<br />
| authors = [[Dongjie Zhao]]<br />[[Haitao Yang]]<br />[[Jian Jiang]]<br />[[Deyi Li]]<br />[[Haisu Zhang]]<br />
| doi = 10.1109/ICM.2011.53<br />
| link = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6113769<br />
}}<br />
'''A Research for the Centrality of Article Edit Collective in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Dongjie Zhao]], [[Haitao Yang]], [[Jian Jiang]], [[Deyi Li]] and [[Haisu Zhang]].<br />
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== Overview ==<br />
Aiming at the problems of the centrality of article edit collective in wikipedia, under the direction of the idea of networked data mining, [[featured articles]] in wikipedia were analyzed by text processing to find the difference of sentences between adjacent versions and identify the edit interaction connection between editors, then the article edit interaction networks were constructed, where the node is editor and the link is the edit interaction connection between editors, then degree, between ness and closeness and topology potential were used to analyze empirically the local centrality of article edit interaction networks. Results show that the cumulative distributions for degree, between ness and topology potential of nodes follow shifted power law distribution, closeness follows normal distribution, and there are many nodes with small degree and between ness but big closeness, there are few nodes with big degree, between ness and closeness. There isn't an absolute center in the networks. However the edit collective have strong heterogeneity and local community structure and topology potential can synthetically characterize the centrality of nodes. The method can effectively find the central nodes in the networks and the research deepens the knowledge of the characteristic of collective edit interaction and collective intelligence.<br />
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Zhao, Dongjie; Yang, Haitao; Jiang, Jian; Li, Deyi; Zhang, Haisu. (2011). "[[A Research for the Centrality of Article Edit Collective in Wikipedia]]". IEEE Computer Society. DOI: 10.1109/ICM.2011.53. <br />
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{{cite journal |last1=Zhao |first1=Dongjie |last2=Yang |first2=Haitao |last3=Jiang |first3=Jian |last4=Li |first4=Deyi |last5=Zhang |first5=Haisu |title=A Research for the Centrality of Article Edit Collective in Wikipedia |date=2011 |doi=10.1109/ICM.2011.53 |url=https://wikipediaquality.com/wiki/A_Research_for_the_Centrality_of_Article_Edit_Collective_in_Wikipedia |journal=IEEE Computer Society}}<br />
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Zhao, Dongjie; Yang, Haitao; Jiang, Jian; Li, Deyi; Zhang, Haisu. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Research_for_the_Centrality_of_Article_Edit_Collective_in_Wikipedia">A Research for the Centrality of Article Edit Collective in Wikipedia</a>&amp;quot;. IEEE Computer Society. DOI: 10.1109/ICM.2011.53. <br />
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[[Category:Scientific works]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Editing_the_Wikipedia:_Its_Role_in_Science_Education&diff=24705Editing the Wikipedia: Its Role in Science Education2020-06-18T05:15:29Z<p>Natalia: + category</p>
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<div>{{Infobox work<br />
| title = Editing the Wikipedia: Its Role in Science Education<br />
| date = 2011<br />
| authors = [[Pilar Mareca]]<br />[[Vicente Alcober Bosch]]<br />
| link = http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?reload=true&amp;arnumber=5974194<br />
}}<br />
'''Editing the Wikipedia: Its Role in Science Education''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Pilar Mareca]] and [[Vicente Alcober Bosch]].<br />
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== Overview ==<br />
This paper describes and analyzes how the cooperation of Engineering students in a [[Wikipedia]] editing project helped to improve their learning and understanding of Physics. This project aims to incorporate to the first University Courses other forms of learning, including specifically the communication of scientific concepts to other students and general audiences. Students have been in accordance to say that with the Wikipedia project have learned to work better together and helped them gain insight into the concepts of Physics.<br />
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Mareca, Pilar; Bosch, Vicente Alcober. (2011). "[[Editing the Wikipedia: Its Role in Science Education]]".<br />
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{{cite journal |last1=Mareca |first1=Pilar |last2=Bosch |first2=Vicente Alcober |title=Editing the Wikipedia: Its Role in Science Education |date=2011 |url=https://wikipediaquality.com/wiki/Editing_the_Wikipedia:_Its_Role_in_Science_Education}}<br />
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Mareca, Pilar; Bosch, Vicente Alcober. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Editing_the_Wikipedia:_Its_Role_in_Science_Education">Editing the Wikipedia: Its Role in Science Education</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Select,_Link_and_Rank:_Diversified_Query_Expansion_and_Entity_Ranking_Using_Wikipedia&diff=24704Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia2020-06-18T05:13:39Z<p>Natalia: + category</p>
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<div>{{Infobox work<br />
| title = Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia<br />
| date = 2016<br />
| authors = [[Adit Krishnan]]<br />[[Deepak Padmanabhan]]<br />[[Sayan Ranu]]<br />[[Sameep Mehta]]<br />
| doi = 10.1007/978-3-319-48740-3_11<br />
| link = https://link.springer.com/chapter/10.1007/978-3-319-48740-3_11/fulltext.html<br />
}}<br />
'''Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Adit Krishnan]], [[Deepak Padmanabhan]], [[Sayan Ranu]] and [[Sameep Mehta]].<br />
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== Overview ==<br />
A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. Authors propose a method, Select-Link-Rank, that exploits [[semantic information]] from [[Wikipedia]] to generate diversified query expansions. SLR does collective processing of terms and Wikipedia entities in an integrated framework, simultaneously diversifying query expansions and entity recommendations. SLR starts with selecting informative terms from search results of the initial query, links them to Wikipedia entities, performs a diversity-conscious entity scoring and transfers such scoring to the term space to arrive at query expansion suggestions. Through an extensive empirical analysis and user study, authors show that method outperforms the state-of-the-art diversified query expansion and diversified entity recommendation techniques.<br />
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Krishnan, Adit; Padmanabhan, Deepak; Ranu, Sayan; Mehta, Sameep. (2016). "[[Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia]]". Springer, Cham. DOI: 10.1007/978-3-319-48740-3_11. <br />
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{{cite journal |last1=Krishnan |first1=Adit |last2=Padmanabhan |first2=Deepak |last3=Ranu |first3=Sayan |last4=Mehta |first4=Sameep |title=Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia |date=2016 |doi=10.1007/978-3-319-48740-3_11 |url=https://wikipediaquality.com/wiki/Select,_Link_and_Rank:_Diversified_Query_Expansion_and_Entity_Ranking_Using_Wikipedia |journal=Springer, Cham}}<br />
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Krishnan, Adit; Padmanabhan, Deepak; Ranu, Sayan; Mehta, Sameep. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Select,_Link_and_Rank:_Diversified_Query_Expansion_and_Entity_Ranking_Using_Wikipedia">Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-48740-3_11. <br />
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[[Category:Scientific works]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Context-Aware_Category_Ranking_for_Wikipedia_Concepts&diff=24703Context-Aware Category Ranking for Wikipedia Concepts2020-06-18T05:11:36Z<p>Natalia: Cats.</p>
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<div>{{Infobox work<br />
| title = Context-Aware Category Ranking for Wikipedia Concepts<br />
| date = 2012<br />
| authors = [[Huiman Hou]]<br />[[Lijiang Chen]]<br />[[Shimin Chen]]<br />[[Peng Jiang]]<br />
| link = https://patents.google.com/patent/WO2014019126A1/en<br />
}}<br />
'''Context-Aware Category Ranking for Wikipedia Concepts''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Huiman Hou]], [[Lijiang Chen]], [[Shimin Chen]] and [[Peng Jiang]].<br />
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== Overview ==<br />
Systems, methods, and computer-readable and executable instructions are provided for categorizing a concept. Categorizing a concept can include selecting a target concept with a number of surrounding textual contexts. Categorizing a concept can also include determining a number of candidate [[categories]] for the target concept based on the number of surrounding textual contexts. Categorizing a concept can also include selecting a predefined number of articles, each with a desired [[relatedness]] to the number of candidate categories. Furthermore, categorizing a concept can include calculating a relatedness score for each of the number of candidate categories based on a relatedness with the number of articles.<br />
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Hou, Huiman; Chen, Lijiang; Chen, Shimin; Jiang, Peng. (2012). "[[Context-Aware Category Ranking for Wikipedia Concepts]]".<br />
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{{cite journal |last1=Hou |first1=Huiman |last2=Chen |first2=Lijiang |last3=Chen |first3=Shimin |last4=Jiang |first4=Peng |title=Context-Aware Category Ranking for Wikipedia Concepts |date=2012 |url=https://wikipediaquality.com/wiki/Context-Aware_Category_Ranking_for_Wikipedia_Concepts}}<br />
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Hou, Huiman; Chen, Lijiang; Chen, Shimin; Jiang, Peng. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Context-Aware_Category_Ranking_for_Wikipedia_Concepts">Context-Aware Category Ranking for Wikipedia Concepts</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Ninth_Graders%E2%80%99_Use_of_and_Trust_in_Wikipedia,_Textbooks,_and_Digital_Resources_from_Textbook_Publishers&diff=24702Ninth Graders’ Use of and Trust in Wikipedia, Textbooks, and Digital Resources from Textbook Publishers2020-06-18T05:10:32Z<p>Natalia: + infobox</p>
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<div>{{Infobox work<br />
| title = Ninth Graders’ Use of and Trust in Wikipedia, Textbooks, and Digital Resources from Textbook Publishers<br />
| date = 2016<br />
| authors = [[Ove Edvard Hatlevik]]<br />
| doi = 10.1007/978-94-6300-648-4_12<br />
| link = https://link.springer.com/chapter/10.1007%2F978-94-6300-648-4_12<br />
}}<br />
'''Ninth Graders’ Use of and Trust in Wikipedia, Textbooks, and Digital Resources from Textbook Publishers''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Ove Edvard Hatlevik]].<br />
<br />
== Overview ==<br />
Recent research has examined the role of information and communication technology (ICT) in learning and teaching (Balanskat & Gertsch, 2010; Binkley et al., 2012; Fraillon, Ainley, Schulz, Friedman, & Gebhardt, 2014; Law, 2009). Both commercial and non-profit actors have developed specific learning resources for school, teachers, and students.</div>Nataliahttps://wikipediaquality.com/index.php?title=Reference_Works_in_the_Age_of_Wikipedia:_a_Review_of_Print_and_Electronic_Holdings_at_the_University_of_Queensland_Library&diff=24701Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library2020-06-18T05:09:11Z<p>Natalia: + category</p>
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<div>{{Infobox work<br />
| title = Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library<br />
| date = 2016<br />
| authors = [[Jeanette O'Shea]]<br />
| link = https://espace.library.uq.edu.au/view/UQ:553564<br />
}}<br />
'''Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Jeanette O'Shea]].<br />
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== Overview ==<br />
The purpose of this snapshot project is to review reference works held by the University of Queensland Library (UQ Library). This involves making recommendations regarding their ongoing management, and to provide a methodological framework for the continuous evaluation and strategic management of reference works. It was carried out over a limited 10-day working period.<br />
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O'Shea, Jeanette. (2016). "[[Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library]]". University of Queensland. Library. <br />
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{{cite journal |last1=O'Shea |first1=Jeanette |title=Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library |date=2016 |url=https://wikipediaquality.com/wiki/Reference_Works_in_the_Age_of_Wikipedia:_a_Review_of_Print_and_Electronic_Holdings_at_the_University_of_Queensland_Library |journal=University of Queensland. Library}}<br />
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O'Shea, Jeanette. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Reference_Works_in_the_Age_of_Wikipedia:_a_Review_of_Print_and_Electronic_Holdings_at_the_University_of_Queensland_Library">Reference Works in the Age of Wikipedia: a Review of Print and Electronic Holdings at the University of Queensland Library</a>&amp;quot;. University of Queensland. Library. <br />
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[[Category:Scientific works]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Query_Translation_Using_Wikipedia-Based_Resources_for_Analysis_and_Disambiguation&diff=24700Query Translation Using Wikipedia-Based Resources for Analysis and Disambiguation2020-06-18T05:07:59Z<p>Natalia: + Embed</p>
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<div>{{Infobox work<br />
| title = Query Translation Using Wikipedia-Based Resources for Analysis and Disambiguation<br />
| date = 2010<br />
| authors = [[Benoît Gaillard]]<br />[[Orange Labs]]<br />[[Malek Boualem]]<br />[[Olivier Collin]]<br />
| link = http://www.mt-archive.info/EAMT-2010-Gaillard.pdf<br />
}}<br />
'''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.<br />
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Gaillard, Benoît; Labs, Orange; Boualem, Malek; Collin, Olivier. (2010). "[[Query Translation Using Wikipedia-Based Resources for Analysis and Disambiguation]]".<br />
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{{cite journal |last1=Gaillard |first1=Benoît |last2=Labs |first2=Orange |last3=Boualem |first3=Malek |last4=Collin |first4=Olivier |title=Query Translation Using Wikipedia-Based Resources for Analysis and Disambiguation |date=2010 |url=https://wikipediaquality.com/wiki/Query_Translation_Using_Wikipedia-Based_Resources_for_Analysis_and_Disambiguation}}<br />
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Gaillard, Benoît; Labs, Orange; Boualem, Malek; Collin, Olivier. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Query_Translation_Using_Wikipedia-Based_Resources_for_Analysis_and_Disambiguation">Query Translation Using Wikipedia-Based Resources for Analysis and Disambiguation</a>&amp;quot;.<br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Taking_Up_the_Mop:_Identifying_Future_Wikipedia_Administrators&diff=24699Taking Up the Mop: Identifying Future Wikipedia Administrators2020-06-18T05:06:23Z<p>Natalia: infobox</p>
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<div>{{Infobox work<br />
| title = Taking Up the Mop: Identifying Future Wikipedia Administrators<br />
| date = 2008<br />
| authors = [[Moira Burke]]<br />[[Robert E. Kraut]]<br />
| doi = 10.1145/1358628.1358871<br />
| link = http://dl.acm.org/citation.cfm?id=1358871<br />
}}<br />
'''Taking Up the Mop: Identifying Future Wikipedia Administrators''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Moira Burke]] and [[Robert E. Kraut]].<br />
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== Overview ==<br />
As [[Wikipedia]] grows, so do the messy byproducts of collaboration. Backlogs of administrative work are increasing, suggesting the need for more users with privileged admin status. This paper presents a model of editors who have successfully passed the peer review process to become admins. The lightweight model is based on behavioral metadata and comments, and does not require any page text. It demonstrates that the [[Wikipedia community]] has shifted in the last two years to prioritizing policymaking and organization experience over simple article-level coordination, and mere edit count does not lead to adminship. The model can be applied as an "AdminFinderBot" to automatically search all editors' histories and pick out likely future admins, as a self-evaluation tool, or as a dashboard of relevant statistics for voters evaluating admin candidates.</div>Nataliahttps://wikipediaquality.com/index.php?title=Analysing_the_Duration_of_Trending_Topics_in_Twitter_Using_Wikipedia&diff=24698Analysing the Duration of Trending Topics in Twitter Using Wikipedia2020-06-18T05:03:28Z<p>Natalia: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Analysing the Duration of Trending Topics in Twitter Using Wikipedia<br />
| date = 2014<br />
| authors = [[Tuan A. Tran]]<br />[[Mihai Georgescu]]<br />[[Xiaofei Zhu]]<br />[[Nattiya Kanhabua]]<br />
| doi = 10.1145/2615569.2615655<br />
| link = http://dl.acm.org/citation.cfm?id=2615569.2615655<br />
}}<br />
'''Analysing the Duration of Trending Topics in Twitter Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Tuan A. Tran]], [[Mihai Georgescu]], [[Xiaofei Zhu]] and [[Nattiya Kanhabua]].<br />
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== Overview ==<br />
The analysis of trending topics in [[Twitter]] is a goldmine for a variety of studies and applications. However, the contents of topics vary greatly from daily routines to major public events, enduring from a few hours to weeks or months. It is thus helpful to distinguish trending topics related to real-world events with those originated within virtual communities. In this paper, authors analyse trending topics in Twitter using [[Wikipedia]] as reference for studying the provenance of trending topics. Authors show that among different factors, the duration of a trending topic characterizes exogenous Twitter trending topics better than endogenous ones.<br />
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Tran, Tuan A.; Georgescu, Mihai; Zhu, Xiaofei; Kanhabua, Nattiya. (2014). "[[Analysing the Duration of Trending Topics in Twitter Using Wikipedia]]".DOI: 10.1145/2615569.2615655. <br />
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{{cite journal |last1=Tran |first1=Tuan A. |last2=Georgescu |first2=Mihai |last3=Zhu |first3=Xiaofei |last4=Kanhabua |first4=Nattiya |title=Analysing the Duration of Trending Topics in Twitter Using Wikipedia |date=2014 |doi=10.1145/2615569.2615655 |url=https://wikipediaquality.com/wiki/Analysing_the_Duration_of_Trending_Topics_in_Twitter_Using_Wikipedia}}<br />
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Tran, Tuan A.; Georgescu, Mihai; Zhu, Xiaofei; Kanhabua, Nattiya. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Analysing_the_Duration_of_Trending_Topics_in_Twitter_Using_Wikipedia">Analysing the Duration of Trending Topics in Twitter Using Wikipedia</a>&amp;quot;.DOI: 10.1145/2615569.2615655. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Wikipedia_Mining_of_Hidden_Links_Between_Political_Leaders&diff=24579Wikipedia Mining of Hidden Links Between Political Leaders2020-06-09T06:22:05Z<p>Natalia: + embed code</p>
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<div>{{Infobox work<br />
| title = Wikipedia Mining of Hidden Links Between Political Leaders<br />
| date = 2016<br />
| authors = [[Klaus M. Frahm]]<br />[[Katia Jaffrès-Runser]]<br />[[Dima L. Shepelyansky]]<br />
| doi = 10.1140/epjb/e2016-70526-3<br />
| link = https://link.springer.com/article/10.1140%2Fepjb%2Fe2016-70526-3<br />
| plink = https://www.arxiv.org/abs/1609.01948<br />
}}<br />
'''Wikipedia Mining of Hidden Links Between Political Leaders''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Klaus M. Frahm]], [[Katia Jaffrès-Runser]] and [[Dima L. Shepelyansky]].<br />
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== Overview ==<br />
Abstract Authors describe a new method of reduced [[Google]] matrix which allows to establish direct and hidden links between a subset of nodes of a large directed network. This approach uses parallels with quantum scattering theory, developed for processes in nuclear and mesoscopic physics and quantum chaos. The method is applied to the [[Wikipedia]] networks in [[different language]] editions analyzing several groups of political leaders of USA, UK, Germany, France, Russia and G20. Authors demonstrate that this approach allows to recover reliably direct and hidden links among political leaders. Authors argue that the reduced Google matrix method can form the mathematical basis for studies in social and political sciences analyzing Leader-Members eXchange (LMX).<br />
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Frahm, Klaus M.; Jaffrès-Runser, Katia; Shepelyansky, Dima L.. (2016). "[[Wikipedia Mining of Hidden Links Between Political Leaders]]". Springer. DOI: 10.1140/epjb/e2016-70526-3. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Frahm |first1=Klaus M. |last2=Jaffrès-Runser |first2=Katia |last3=Shepelyansky |first3=Dima L. |title=Wikipedia Mining of Hidden Links Between Political Leaders |date=2016 |doi=10.1140/epjb/e2016-70526-3 |url=https://wikipediaquality.com/wiki/Wikipedia_Mining_of_Hidden_Links_Between_Political_Leaders |journal=Springer}}<br />
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=== HTML ===<br />
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Frahm, Klaus M.; Jaffrès-Runser, Katia; Shepelyansky, Dima L.. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_Mining_of_Hidden_Links_Between_Political_Leaders">Wikipedia Mining of Hidden Links Between Political Leaders</a>&amp;quot;. Springer. DOI: 10.1140/epjb/e2016-70526-3. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=A_Method_for_Predicting_Wikipedia_Editors%27_Editing_Interest:_based_on_a_Factor_Graph_Model&diff=24578A Method for Predicting Wikipedia Editors' Editing Interest: based on a Factor Graph Model2020-06-09T06:19:28Z<p>Natalia: Adding infobox</p>
<hr />
<div>{{Infobox work<br />
| title = A Method for Predicting Wikipedia Editors' Editing Interest: based on a Factor Graph Model<br />
| date = 2016<br />
| authors = [[Haisu Zhang]]<br />[[Sheng Zhang]]<br />[[Zhaolin Wu]]<br />[[Liwei Huang]]<br />[[Yutao Ma]]<br />
| doi = 10.4018/IJWSR.2016070101<br />
| link = http://dl.acm.org/citation.cfm?id=2984654<br />
}}<br />
'''A Method for Predicting Wikipedia Editors' Editing Interest: based on a Factor Graph Model''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Haisu Zhang]], [[Sheng Zhang]], [[Zhaolin Wu]], [[Liwei Huang]] and [[Yutao Ma]].<br />
<br />
== Overview ==<br />
Recruiting or recommending appropriate potential [[Wikipedia editors]] to edit a specific [[Wikipedia]] entry or article can play an important role in improving the quality and [[credibility]] of Wikipedia. According to empirical observations based on a small-scale dataset collected from Wikipedia, this paper proposes an Interest Prediction Factor Graph IPFG model, which is characterized by editor's social properties, hyperlinks between Wikipedia entries, the [[categories]] of an entry and other important [[features]], to predict an editor's editing interest in types of Wikipedia entries. Furthermore, the paper suggests a parameter learning algorithm based on the gradient descent algorithm and the Loopy Sum-Product algorithm for factor graphs. An experiment on a Wikipedia dataset with different frequencies of data collection shows that the average prediction accuracy F1 score of the IPFG model for data collected quarterly could be up to 0.875, which is approximately 0.49 higher than that of a collaborative filtering approach. In addition, the paper analyzes how incomplete social properties and editing bursts affect the prediction accuracy of the IPFG model. The authors' results can provide insight into effective Wikipedia article tossing and can improve the quality of special entries that belong to specific categories by means of collective collaboration.</div>Nataliahttps://wikipediaquality.com/index.php?title=The_Pediaphon_-_Speech_Interface_to_the_Free_Wikipedia_Encyclopedia_for_Mobile_Phones,_Pda%27s_and_Mp3-Players&diff=24577The Pediaphon - Speech Interface to the Free Wikipedia Encyclopedia for Mobile Phones, Pda's and Mp3-Players2020-06-09T06:16:27Z<p>Natalia: Embed</p>
<hr />
<div>{{Infobox work<br />
| title = The Pediaphon - Speech Interface to the Free Wikipedia Encyclopedia for Mobile Phones, Pda's and Mp3-Players<br />
| date = 2007<br />
| authors = [[Andreas Bischoff]]<br />
| doi = 10.1109/DEXA.2007.139<br />
| link = http://www.ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04312959<br />
}}<br />
'''The Pediaphon - Speech Interface to the Free Wikipedia Encyclopedia for Mobile Phones, Pda's and Mp3-Players''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Andreas Bischoff]].<br />
<br />
== Overview ==<br />
This paper presents an approach to generate audio based learning material dynamically from [[Wikipedia]] articles for m-learning and ubiquitous access. It introduces the so called 'Pediaphon', an speech interface to the free Wikipedia online encyclopedia as an example application for 'microlearning'. The effective generation and the deployment of the audio data to the user via podcast or progressive download (pseudo streaming) are covered. A convenient cellphone interface to the Wikipedia content, which is usable with every mobile phone will be introduced.<br />
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Bischoff, Andreas. (2007). "[[The Pediaphon - Speech Interface to the Free Wikipedia Encyclopedia for Mobile Phones, Pda's and Mp3-Players]]".DOI: 10.1109/DEXA.2007.139. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Bischoff |first1=Andreas |title=The Pediaphon - Speech Interface to the Free Wikipedia Encyclopedia for Mobile Phones, Pda's and Mp3-Players |date=2007 |doi=10.1109/DEXA.2007.139 |url=https://wikipediaquality.com/wiki/The_Pediaphon_-_Speech_Interface_to_the_Free_Wikipedia_Encyclopedia_for_Mobile_Phones,_Pda's_and_Mp3-Players}}<br />
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=== HTML ===<br />
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Bischoff, Andreas. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/The_Pediaphon_-_Speech_Interface_to_the_Free_Wikipedia_Encyclopedia_for_Mobile_Phones,_Pda's_and_Mp3-Players">The Pediaphon - Speech Interface to the Free Wikipedia Encyclopedia for Mobile Phones, Pda's and Mp3-Players</a>&amp;quot;.DOI: 10.1109/DEXA.2007.139. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=The_Qualim_Question_Answering_Demo:_Supplementing_Answers_with_Paragraphs_Drawn_from_Wikipedia&diff=24576The Qualim Question Answering Demo: Supplementing Answers with Paragraphs Drawn from Wikipedia2020-06-09T06:14:30Z<p>Natalia: + Embed</p>
<hr />
<div>{{Infobox work<br />
| title = The Qualim Question Answering Demo: Supplementing Answers with Paragraphs Drawn from Wikipedia<br />
| date = 2008<br />
| authors = [[Michael Kaisser]]<br />
| doi = 10.3115/1564144.1564153<br />
| link = https://dl.acm.org/citation.cfm?doid=1564144.1564153<br />
}}<br />
'''The Qualim Question Answering Demo: Supplementing Answers with Paragraphs Drawn from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Michael Kaisser]].<br />
<br />
== Overview ==<br />
This paper describes the online demo of the QuALiM Question Answering system. While the system actually gets answers from the web by querying major search engines, during presentation answers are supplemented with relevant passages from [[Wikipedia]]. Authors believe that this additional information improves a user's search experience.<br />
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Kaisser, Michael. (2008). "[[The Qualim Question Answering Demo: Supplementing Answers with Paragraphs Drawn from Wikipedia]]". Association for Computational Linguistics. DOI: 10.3115/1564144.1564153. <br />
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{{cite journal |last1=Kaisser |first1=Michael |title=The Qualim Question Answering Demo: Supplementing Answers with Paragraphs Drawn from Wikipedia |date=2008 |doi=10.3115/1564144.1564153 |url=https://wikipediaquality.com/wiki/The_Qualim_Question_Answering_Demo:_Supplementing_Answers_with_Paragraphs_Drawn_from_Wikipedia |journal=Association for Computational Linguistics}}<br />
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=== HTML ===<br />
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Kaisser, Michael. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/The_Qualim_Question_Answering_Demo:_Supplementing_Answers_with_Paragraphs_Drawn_from_Wikipedia">The Qualim Question Answering Demo: Supplementing Answers with Paragraphs Drawn from Wikipedia</a>&amp;quot;. Association for Computational Linguistics. DOI: 10.3115/1564144.1564153. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Finding_Needles_in_an_Encyclopedic_Haystack:_Detecting_Classes_Among_Wikipedia_Articles&diff=24575Finding Needles in an Encyclopedic Haystack: Detecting Classes Among Wikipedia Articles2020-06-09T06:12:14Z<p>Natalia: Infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Finding Needles in an Encyclopedic Haystack: Detecting Classes Among Wikipedia Articles<br />
| date = 2018<br />
| authors = [[Marius Pasca]]<br />
| doi = 10.1145/3178876.3186025<br />
| link = https://www2018.thewebconf.org/program/web-content-analysis/#29<br />
}}<br />
'''Finding Needles in an Encyclopedic Haystack: Detecting Classes Among Wikipedia Articles''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Marius Pasca]].<br />
<br />
== Overview ==<br />
A lightweight method distinguishes articles within [[Wikipedia]] that are classes (“Novel”, “Book”) from other articles (“Three Men in a Boat”, “Diary of a Pilgrimage”). It exploits clues available within the article text and within [[categories]] associated with articles in Wikipedia, while not requiring any linguistic preprocessing tools. Experimental results show that classes can be identified among Wikipedia articles in [[multiple languages]], at aggregate precision and recall above 0.9 and 0.6 respectively.</div>Nataliahttps://wikipediaquality.com/index.php?title=The_Hidden_Order_of_Wikipedia&diff=24574The Hidden Order of Wikipedia2020-06-09T06:10:35Z<p>Natalia: + links</p>
<hr />
<div>'''The Hidden Order of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Fernanda B. Viégas]], [[Martin Wattenberg]] and [[Matthew Mehall McKeon]].<br />
<br />
== Overview ==<br />
Authors examine the procedural side of [[Wikipedia]], the well-known internet encyclopedia. Despite the lack of structure in the underlying wiki technology, users abide by hundreds of rules and follow well-defined processes. Authors case study is the Featured Article (FA) process, one of the best established procedures on the site. Authors analyze the FA process through the theoretical framework of commons governance, and demonstrate how this process blends elements of traditional workflow with peer production. Authors conclude that rather than encouraging anarchy, many aspects of wiki technology lend themselves to the collective creation of formalized process and policy.</div>Nataliahttps://wikipediaquality.com/index.php?title=Why_Be_a_Wikipedian&diff=24573Why Be a Wikipedian2020-06-09T06:08:38Z<p>Natalia: infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Why Be a Wikipedian<br />
| date = 2009<br />
| authors = [[Hoda Baytiyeh]]<br />[[Jay Pfaffman]]<br />
| doi = 10.3115/1600053.1600117<br />
| link = http://dl.acm.org/citation.cfm?id=1600053.1600117<br />
| plink = https://www.researchgate.net/profile/Hoda_Baytiyeh/publication/221033683_Why_be_a_Wikipedian/links/02e7e53355daba10fc000000.pdf<br />
}}<br />
'''Why Be a Wikipedian''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Hoda Baytiyeh]] and [[Jay Pfaffman]].<br />
<br />
== Overview ==<br />
Wikipedia is a user-edited encyclopedia. Unpaid users contribute articles, edit them, and have heated debates about what information should be included or excluded. This study is designed to learn more about why people are willing to do this work without any fiscal compensation. [[Wikipedia]] administrators (n=115) completed an online survey with Likert-scaled items of potential types of satisfaction derived from participation as well as comments that were used to check the validity of the Likert-scaled items and allow participants to say in their own words why they were Wikipedian. Results showed that contributors in Wikipedia are driven largely by motivations to learn and create.</div>Nataliahttps://wikipediaquality.com/index.php?title=Entity_Linking_in_Media_Content_and_User_Comments:_Connecting_Data_to_Wikipedia_and_Other_Knowledge_Bases&diff=24572Entity Linking in Media Content and User Comments: Connecting Data to Wikipedia and Other Knowledge Bases2020-06-09T06:06:06Z<p>Natalia: Adding embed</p>
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<div>{{Infobox work<br />
| title = Entity Linking in Media Content and User Comments: Connecting Data to Wikipedia and Other Knowledge Bases<br />
| date = 2015<br />
| authors = [[David Tomás]]<br />[[Yoan Gutiérrez]]<br />[[Francisco Agulló]]<br />
| doi = 10.1109/eCHALLENGES.2015.7441053<br />
| link = http://ieeexplore.ieee.org/document/7441053/<br />
}}<br />
'''Entity Linking in Media Content and User Comments: Connecting Data to Wikipedia and Other Knowledge Bases''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[David Tomás]], [[Yoan Gutiérrez]] and [[Francisco Agulló]].<br />
<br />
== Overview ==<br />
This paper presents an approach to entity linking in the domain of Social TV on two different knowledge bases: [[Wikipedia]] and own [[ontology]] of media assets. Authors provide insights into the main challenges posed by this task, together with a description of different tools and related projects in the field. Since the system described is part of a platform intended for commercial exploitation, licensing issues are described in order to help small and medium-sized enterprises (SME) willing to develop systems for entity linking to take decisions on the best choice. The paper also presents information about business benefits of these technologies for both end users and companies. The approach described includes an evaluation of three different disambiguation methods and two different entity candidate selection processes.<br />
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Tomás, David; Gutiérrez, Yoan; Agulló, Francisco. (2015). "[[Entity Linking in Media Content and User Comments: Connecting Data to Wikipedia and Other Knowledge Bases]]".DOI: 10.1109/eCHALLENGES.2015.7441053. <br />
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{{cite journal |last1=Tomás |first1=David |last2=Gutiérrez |first2=Yoan |last3=Agulló |first3=Francisco |title=Entity Linking in Media Content and User Comments: Connecting Data to Wikipedia and Other Knowledge Bases |date=2015 |doi=10.1109/eCHALLENGES.2015.7441053 |url=https://wikipediaquality.com/wiki/Entity_Linking_in_Media_Content_and_User_Comments:_Connecting_Data_to_Wikipedia_and_Other_Knowledge_Bases}}<br />
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Tomás, David; Gutiérrez, Yoan; Agulló, Francisco. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Entity_Linking_in_Media_Content_and_User_Comments:_Connecting_Data_to_Wikipedia_and_Other_Knowledge_Bases">Entity Linking in Media Content and User Comments: Connecting Data to Wikipedia and Other Knowledge Bases</a>&amp;quot;.DOI: 10.1109/eCHALLENGES.2015.7441053. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Representing_African_Cities_in_Wikipedia:_the_Case_of_Lagos_and_Kinshasa&diff=24571Representing African Cities in Wikipedia: the Case of Lagos and Kinshasa2020-06-09T06:03:25Z<p>Natalia: infobox</p>
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<div>{{Infobox work<br />
| title = Representing African Cities in Wikipedia: the Case of Lagos and Kinshasa<br />
| date = 2018<br />
| authors = [[Brendan Luyt]]<br />
| doi = 10.1177/0266666918756171<br />
| link = http://journals.sagepub.com/doi/full/10.1177/0266666918756171<br />
}}<br />
'''Representing African Cities in Wikipedia: the Case of Lagos and Kinshasa''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Brendan Luyt]].<br />
<br />
== Overview ==<br />
The role played by representations in the lives of cities endows the study of their production and distribution in various media with importance. Today, the Internet, that amorphous network linking much of the world, is a powerful new media for the imagination of city spaces and hence in need of investigation. In this article, Author focus on the online encyclopaedia [[Wikipedia]], one of the most popular websites on the Internet. My aim is to explore the representations of two of the largest sub-Saharan African cities, Lagos and Kinshasa, in their respective Wikipedia articles. Wikipedia has been described as the encyclopaedia anyone can edit, suggesting that it is open to multiple perspectives on any particular topic. Given the history of how Africa in general has been either marginalized or conjured as an exotic or miserable “other” by much media work this potential for wider range of representations should not be overlooked. Does Wikipedia live up to its [[reputation]] in the case of Kinshasa and Lagos?</div>Nataliahttps://wikipediaquality.com/index.php?title=Credibility_Judgment_and_Verification_Behavior_of_College_Students_Concerning_Wikipedia&diff=24570Credibility Judgment and Verification Behavior of College Students Concerning Wikipedia2020-06-09T06:01:48Z<p>Natalia: Adding categories</p>
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<div>{{Infobox work<br />
| title = Credibility Judgment and Verification Behavior of College Students Concerning Wikipedia<br />
| date = 2011<br />
| authors = [[Sook Lim]]<br />[[Christine Simon]]<br />
| doi = 10.5210/fm.v16i4.3263<br />
| link = http://firstmonday.org/article/view/3263/2860<br />
}}<br />
'''Credibility Judgment and Verification Behavior of College Students Concerning Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Sook Lim]] and [[Christine Simon]].<br />
<br />
== Overview ==<br />
This study examines [[credibility]] judgments in relation to peripheral cues and genre of [[Wikipedia]] articles, and attempts to understand user information verification behavior based on the theory of bounded rationality. Data were collected employing both an experiment and a survey at a large public university in the midwestern United States in Spring 2010. This study shows some interesting patterns. It appears that the effect of peripheral cues on credibility judgments differed according to genre. Those who did not verify information displayed a higher level of satisficing than those who did. Students used a variety of peripheral cues of Wikipedia. The exploratory data show that peer endorsement may be more important than formal authorities for user generated information sources, such as Wikipedia, which calls for further research.<br />
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Lim, Sook; Simon, Christine. (2011). "[[Credibility Judgment and Verification Behavior of College Students Concerning Wikipedia]]".DOI: 10.5210/fm.v16i4.3263. <br />
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{{cite journal |last1=Lim |first1=Sook |last2=Simon |first2=Christine |title=Credibility Judgment and Verification Behavior of College Students Concerning Wikipedia |date=2011 |doi=10.5210/fm.v16i4.3263 |url=https://wikipediaquality.com/wiki/Credibility_Judgment_and_Verification_Behavior_of_College_Students_Concerning_Wikipedia}}<br />
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Lim, Sook; Simon, Christine. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Credibility_Judgment_and_Verification_Behavior_of_College_Students_Concerning_Wikipedia">Credibility Judgment and Verification Behavior of College Students Concerning Wikipedia</a>&amp;quot;.DOI: 10.5210/fm.v16i4.3263. <br />
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[[Category:Scientific works]]</div>Nataliahttps://wikipediaquality.com/index.php?title=Sequencing_Wikipedia_Pages:_an_On-The-Fly_Approach_to_Course_Building&diff=24569Sequencing Wikipedia Pages: an On-The-Fly Approach to Course Building2020-06-09T06:00:02Z<p>Natalia: infobox</p>
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<div>{{Infobox work<br />
| title = Sequencing Wikipedia Pages: an On-The-Fly Approach to Course Building<br />
| date = 2016<br />
| authors = [[Fabio Gasparetti]]<br />[[Carla Limongelli]]<br />[[Alessandra Milita]]<br />[[Filippo Sciarrone]]<br />[[Andrea Tarantini]]<br />
| doi = 10.5220/0005815203970404<br />
| link = https://iris.uniroma3.it/handle/11590/309589<br />
}}<br />
'''Sequencing Wikipedia Pages: an On-The-Fly Approach to Course Building''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Fabio Gasparetti]], [[Carla Limongelli]], [[Alessandra Milita]], [[Filippo Sciarrone]] and [[Andrea Tarantini]].<br />
<br />
== Overview ==<br />
With its 5,006,202 articles, 49 millions of registered people and on average 800 new articles per day,</div>Nataliahttps://wikipediaquality.com/index.php?title=Link_Analysis_of_Wikipedia_Documents_Using_Mapreduce&diff=24568Link Analysis of Wikipedia Documents Using Mapreduce2020-06-09T05:58:38Z<p>Natalia: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Link Analysis of Wikipedia Documents Using Mapreduce<br />
| date = 2015<br />
| authors = [[Vasa Hardik]]<br />[[Vasudevan Anirudh]]<br />[[Palanisamy Balaji]]<br />
| doi = 10.1109/IRI.2015.92<br />
| link = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7301030<br />
}}<br />
'''Link Analysis of Wikipedia Documents Using Mapreduce''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Vasa Hardik]], [[Vasudevan Anirudh]] and [[Palanisamy Balaji]].<br />
<br />
== Overview ==<br />
Wikipedia, a collaborative and user driven encyclopedia is considered to be the largest content thesaurus on the web, expanding into a massive database housing a huge amount of information. In this paper, authors present the design and implementation of a MapReduce-based [[Wikipedia]] link analysis system that provides a hierarchical examination of document connectivity in Wikipedia and captures the semantic relationships between the articles. Authors system consists of a Wikipedia crawler, a MapReduce-based distributed parser and the link analysis techniques. The results produced by this study are then modelled to the web Key Performance Indicators (KPIs) for link-structure interpretation. Authors find that Wikipedia has a remarkable capability as a corpus for content correlation with respect to connectivity among articles. Link Analysis and Semantic Structuration of Wikipedia not only provides an ergonomic report of tire-based link hierarchy of Wikipedia articles but also reflects the general cognition on semantic relationship between them. The results of analysis are aimed at providing valuable insights on evaluating the accuracy and the content scalability of Wikipedia through its link schematics.<br />
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Hardik, Vasa; Anirudh, Vasudevan; Balaji, Palanisamy. (2015). "[[Link Analysis of Wikipedia Documents Using Mapreduce]]".DOI: 10.1109/IRI.2015.92. <br />
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{{cite journal |last1=Hardik |first1=Vasa |last2=Anirudh |first2=Vasudevan |last3=Balaji |first3=Palanisamy |title=Link Analysis of Wikipedia Documents Using Mapreduce |date=2015 |doi=10.1109/IRI.2015.92 |url=https://wikipediaquality.com/wiki/Link_Analysis_of_Wikipedia_Documents_Using_Mapreduce}}<br />
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=== HTML ===<br />
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Hardik, Vasa; Anirudh, Vasudevan; Balaji, Palanisamy. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Link_Analysis_of_Wikipedia_Documents_Using_Mapreduce">Link Analysis of Wikipedia Documents Using Mapreduce</a>&amp;quot;.DOI: 10.1109/IRI.2015.92. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Research_Guides:_Ada_Lovelave_Day_Wikipedia_Editathon:_Home&diff=24567Research Guides: Ada Lovelave Day Wikipedia Editathon: Home2020-06-09T05:57:21Z<p>Natalia: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Research Guides: Ada Lovelave Day Wikipedia Editathon: Home<br />
| date = 2016<br />
| authors = [[Emily Gari]]<br />
| link = http://libguides.colorado.edu/adalovelaceday<br />
}}<br />
'''Research Guides: Ada Lovelave Day Wikipedia Editathon: Home''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Emily Gari]].<br />
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== Overview ==<br />
October 11th is Ada Lovelace Day, which celebrates women identified people in STEM. Help make their contributions clear by adding & editing [[Wikipedia]] entries for them!<br />
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== Embed ==<br />
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Gari, Emily. (2016). "[[Research Guides: Ada Lovelave Day Wikipedia Editathon: Home]]".<br />
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{{cite journal |last1=Gari |first1=Emily |title=Research Guides: Ada Lovelave Day Wikipedia Editathon: Home |date=2016 |url=https://wikipediaquality.com/wiki/Research_Guides:_Ada_Lovelave_Day_Wikipedia_Editathon:_Home}}<br />
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=== HTML ===<br />
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Gari, Emily. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Research_Guides:_Ada_Lovelave_Day_Wikipedia_Editathon:_Home">Research Guides: Ada Lovelave Day Wikipedia Editathon: Home</a>&amp;quot;.<br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Collective_Remembering_of_Organizations:_Co-Construction_of_Organizational_Pasts_in_Wikipedia&diff=24566Collective Remembering of Organizations: Co-Construction of Organizational Pasts in Wikipedia2020-06-09T05:55:55Z<p>Natalia: Embed for English Wikipedia, HTML</p>
<hr />
<div>{{Infobox work<br />
| title = Collective Remembering of Organizations: Co-Construction of Organizational Pasts in Wikipedia<br />
| date = 2015<br />
| authors = [[Michael Etter]]<br />[[Finn Årup Nielsen]]<br />
| doi = 10.1108/CCIJ-09-2014-0059<br />
| link = http://www.emeraldinsight.com/doi/abs/10.1108/CCIJ-09-2014-0059<br />
}}<br />
'''Collective Remembering of Organizations: Co-Construction of Organizational Pasts in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Michael Etter]] and [[Finn Årup Nielsen]].<br />
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== Overview ==<br />
Purpose – How organizations’ pasts are presented to the public is crucial, because this presentation shapes corporate [[reputation]]s. Increasingly, various actors contribute to the public remembering of organizations with new information and communication technologies (ICTs). The purpose of this paper is to investigate the online encyclopedia [[Wikipedia]] as a global memory place, where the pasts of organizations are communicatively co-constructed by actors of a loosely connected community. Design/methodology/approach – The authors analyze 1,459 edits of Wikipedia pages of ten organizations from various industries. Quantitative content analysis detects Wikipedia edits for their reputational relevance and reference to formal sources, such as corporate communication or newspapers. Furthermore, the authors investigate to which degree current corporate communication in form of 177 press releases has an influence on the remembering process in Wikipedia. Findings – The analysis shows how the continuous construction o...<br />
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Etter, Michael; Nielsen, Finn Årup. (2015). "[[Collective Remembering of Organizations: Co-Construction of Organizational Pasts in Wikipedia]]". Emerald Group Publishing Limited. DOI: 10.1108/CCIJ-09-2014-0059. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Etter |first1=Michael |last2=Nielsen |first2=Finn Årup |title=Collective Remembering of Organizations: Co-Construction of Organizational Pasts in Wikipedia |date=2015 |doi=10.1108/CCIJ-09-2014-0059 |url=https://wikipediaquality.com/wiki/Collective_Remembering_of_Organizations:_Co-Construction_of_Organizational_Pasts_in_Wikipedia |journal=Emerald Group Publishing Limited}}<br />
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Etter, Michael; Nielsen, Finn Årup. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Collective_Remembering_of_Organizations:_Co-Construction_of_Organizational_Pasts_in_Wikipedia">Collective Remembering of Organizations: Co-Construction of Organizational Pasts in Wikipedia</a>&amp;quot;. Emerald Group Publishing Limited. DOI: 10.1108/CCIJ-09-2014-0059. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Crowdsourcing_Knowledge_:_Interdiscursive_Flows_from_Wikipedia_into_Scholarly_Research&diff=24565Crowdsourcing Knowledge : Interdiscursive Flows from Wikipedia into Scholarly Research2020-06-09T05:54:53Z<p>Natalia: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Crowdsourcing Knowledge : Interdiscursive Flows from Wikipedia into Scholarly Research<br />
| date = 2014<br />
| authors = [[Simon Lindgren]]<br />
| doi = 10.3384/cu.2000.1525.146609<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=2310047&amp;type=pdf<br />
| plink = https://www.researchgate.net/profile/Simon_Lindgren/publication/269635710_Crowdsourcing_Knowledge_Interdiscursive_Flows_from_Wikipedia_into_Scholarly_Research/links/54911ef30cf2d1800d87c7e9.pdf<br />
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'''Crowdsourcing Knowledge : Interdiscursive Flows from Wikipedia into Scholarly Research''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Simon Lindgren]].<br />
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== Overview ==<br />
Information increasingly flows from smart online knowledge systems, based on ‘collective intelligence’, and to the more traditional form of knowledge production that takes place within academia. Lo ...</div>Nataliahttps://wikipediaquality.com/index.php?title=Syntax_Analyzer_a_Selectivity_Estimation_Technique_Applied_on_Wikipedia_Xml_Data_Set&diff=24564Syntax Analyzer a Selectivity Estimation Technique Applied on Wikipedia Xml Data Set2020-06-09T05:52:31Z<p>Natalia: wikilinks</p>
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<div>'''Syntax Analyzer a Selectivity Estimation Technique Applied on Wikipedia Xml Data Set''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Muath Alrammal]] and [[Gaétan Hains]].<br />
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== Overview ==<br />
Querying large volume of XML data represents a bottleneck for several computationally intensive applications. A fast and accurate selectivity estimation mechanism is of practical importance because selectivity estimation plays a fundamental role in XML query performance. Recently proposed techniques are all based on some forms of structure synopses that could be time consuming to build and not effective for summarizing complex structure relationships. Precisely, current techniques do not handle or process efficiently the large text nodes exist in some data sets as [[Wikipedia]]. To overcome this limitation, authors extend previous work [12] that is a stream-based selectivity estimation technique to process efficiently the English data set of Wikipedia. The content of XML text nodes in Wikipedia contains a massive amount of real-life information that techniques bring closer to practical and efficient everyday use. Extensive experiments on Wikipedia data sets (with different sizes) show that technique achieves a remarkable accuracy and reasonable performance.</div>Nataliahttps://wikipediaquality.com/index.php?title=The_Wikipedia_:_Experts,_Expertise_and_Ethical_Challenges&diff=24563The Wikipedia : Experts, Expertise and Ethical Challenges2020-06-09T05:51:05Z<p>Natalia: + embed code</p>
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<div>{{Infobox work<br />
| title = The Wikipedia : Experts, Expertise and Ethical Challenges<br />
| date = 2008<br />
| authors = [[Sharman Lichtenstein]]<br />
| link = http://dro.deakin.edu.au/view/DU:30018339<br />
}}<br />
'''The Wikipedia : Experts, Expertise and Ethical Challenges''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Sharman Lichtenstein]].<br />
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== Overview ==<br />
Participatory models are replacing the traditional models of experts and expertise that are based on individuals, their credentials and domain experience. The [[Wikipedia]] is a well-known and popular online encyclopedia, built, edited and administrated by lay citizens rather than traditional experts. It utilises a Web-based participatory model of experts and expertise to enable knowledge contributions and provide administration. While much has been written about the Wikipedia and its merits and pitfalls, there are important ethical challenges stemming from the underlying Wikipedia model. Ethical concerns are likely to be important to Wikipedia users, however as yet, such concerns have not been systematically explored. By reviewing and synthesising existing literature, this paper identifies six key ethical challenges for existing and potential Wikipedia users, stemming from the underlying Web-based participatory model of experts and expertise. Important implications arising from the findings are also discussed.<br />
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Lichtenstein, Sharman. (2008). "[[The Wikipedia : Experts, Expertise and Ethical Challenges]]". Deakin University. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Lichtenstein |first1=Sharman |title=The Wikipedia : Experts, Expertise and Ethical Challenges |date=2008 |url=https://wikipediaquality.com/wiki/The_Wikipedia_:_Experts,_Expertise_and_Ethical_Challenges |journal=Deakin University}}<br />
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=== HTML ===<br />
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Lichtenstein, Sharman. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/The_Wikipedia_:_Experts,_Expertise_and_Ethical_Challenges">The Wikipedia : Experts, Expertise and Ethical Challenges</a>&amp;quot;. Deakin University. <br />
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</code></div>Nataliahttps://wikipediaquality.com/index.php?title=Analysis_on_the_Applications_of_Wikipedia_in_Chinese_Information_Processing&diff=24562Analysis on the Applications of Wikipedia in Chinese Information Processing2020-06-09T05:48:11Z<p>Natalia: Infobox</p>
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<div>{{Infobox work<br />
| title = Analysis on the Applications of Wikipedia in Chinese Information Processing<br />
| date = 2011<br />
| authors = [[Rongbo Wang]]<br />[[Zhiqun Chen]]<br />[[Xiaohua Wang]]<br />[[Xiaoxi Huang]]<br />
| doi = 10.1109/ICMT.2011.6002315<br />
| link = http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6002315<br />
}}<br />
'''Analysis on the Applications of Wikipedia in Chinese Information Processing''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Rongbo Wang]], [[Zhiqun Chen]], [[Xiaohua Wang]] and [[Xiaoxi Huang]].<br />
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== Overview ==<br />
As an encyclopedia, [[Wikipedia]] has the advantages of large amount of information, wide coverage and timely updating. The Wikipedia is constructed and shared by all users. Currently, with comparison to the applications of other language resources such as [[WordNet]] and Hownet in [[natural language processing]] (NLP), Wikipedia is seldom used in NLP by researchers. In this paper, firstly, the main characteristics of Wikipedia are analyzed. Then its applications in areas of NLP are introduced, especially in Chinese information processing. Finally, after a more detailed comparison among Wikipedia, WordNet and HowNet, authors conclude that Wikipedia will become a very important language resource and be applied to Chinese information processing by more and more researchers.</div>Natalia