https://wikipediaquality.com/api.php?action=feedcontributions&user=Abigail&feedformat=atom
Wikipedia Quality - User contributions [en]
2024-03-29T12:29:36Z
User contributions
MediaWiki 1.30.0
https://wikipediaquality.com/index.php?title=A_Wikipedia-Based_Corpus_Reference_Tool&diff=25740
A Wikipedia-Based Corpus Reference Tool
2020-10-29T07:16:59Z
<p>Abigail: + wikilinks</p>
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<div>'''A Wikipedia-Based Corpus Reference Tool''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Jason Ginsburg]].<br />
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== Overview ==<br />
This paper describes a dictionary-like reference tool that is designed to help users find information that is similar to what one would find in a dictionary when looking up a word, except that this information is extracted automatically from large corpora. For a particular vocabulary item, a user can view frequency information, part-of-speech distribution, word-forms, definitions, example paragraphs and collocations. All of this information is extracted automatically from corpora and most of this information is extracted from [[Wikipedia]]. Since Wikipedia is a massive corpus covering a diverse range of general topics, this information is probably very representative of how target words are used in general. This project has applications for English language teachers and learners, as well as for language researchers.</div>
Abigail
https://wikipediaquality.com/index.php?title=Robust_Clustering_of_Languages_Across_Wikipedia_Growth&diff=24267
Robust Clustering of Languages Across Wikipedia Growth
2020-05-20T05:43:22Z
<p>Abigail: Wikilinks</p>
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<div>'''Robust Clustering of Languages Across Wikipedia Growth''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Kristina Ban]], [[Matjaz Perc]] and [[Zoran Levnajic]].<br />
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== Overview ==<br />
Wikipedia is the largest existing knowledge repository that is growing on a genuine crowdsourcing support. While the [[English Wikipedia]] is the most extensive and the most researched one with over 5 million articles, comparatively little is known about the behaviour and growth of the remaining 283 smaller [[Wikipedia]]s, the smallest of which, Afar, has only one article. Here, authors use a subset of these data, consisting of 14 962 different articles, each of which exists in 26 [[different language]]s, from Arabic to Ukrainian. Authors study the growth of Wikipedias in these languages over a time span of 15 years. Authors show that, while an average article follows a random path from one language to another, there exist six well-defined clusters of Wikipedias that share common growth patterns. The make-up of these clusters is remarkably robust against the method used for their determination, as authors verify via four different clustering methods. Interestingly, the identified Wikipedia clusters have little correlation with language families and groups. Rather, the growth of Wikipedia across different languages is governed by different factors, ranging from similarities in culture to information literacy.</div>
Abigail
https://wikipediaquality.com/index.php?title=Automatic_Extraction_of_Semantic_Relations_from_Wikipedia&diff=24266
Automatic Extraction of Semantic Relations from Wikipedia
2020-05-20T05:41:17Z
<p>Abigail: + embed code</p>
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<div>{{Infobox work<br />
| title = Automatic Extraction of Semantic Relations from Wikipedia<br />
| date = 2015<br />
| authors = [[Patrick Arnold]]<br />[[Erhard Rahm]]<br />
| doi = 10.1142/S0218213015400102<br />
| link = http://www.worldscientific.com/doi/abs/10.1142/S0218213015400102<br />
}}<br />
'''Automatic Extraction of Semantic Relations from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Patrick Arnold]] and [[Erhard Rahm]].<br />
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== Overview ==<br />
Authors introduce a novel approach to extract semantic relations (e.g., is-a and part-of relations) from [[Wikipedia]] articles. These relations are used to build up a large and up-to-date thesaurus providing background knowledge for tasks such as determining semantic [[ontology]] mappings. Authors automatic approach uses a comprehensive set of semantic patterns, finite state machines and NLP techniques to extract millions of relations between concepts. An evaluation for different domains shows the high quality and effectiveness of the proposed approach. Authors also illustrate the value of the newly found relations for improving existing ontology mappings.<br />
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Arnold, Patrick; Rahm, Erhard. (2015). "[[Automatic Extraction of Semantic Relations from Wikipedia]]". World Scientific Publishing Company. DOI: 10.1142/S0218213015400102. <br />
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{{cite journal |last1=Arnold |first1=Patrick |last2=Rahm |first2=Erhard |title=Automatic Extraction of Semantic Relations from Wikipedia |date=2015 |doi=10.1142/S0218213015400102 |url=https://wikipediaquality.com/wiki/Automatic_Extraction_of_Semantic_Relations_from_Wikipedia |journal=World Scientific Publishing Company}}<br />
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Arnold, Patrick; Rahm, Erhard. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Automatic_Extraction_of_Semantic_Relations_from_Wikipedia">Automatic Extraction of Semantic Relations from Wikipedia</a>&amp;quot;. World Scientific Publishing Company. DOI: 10.1142/S0218213015400102. <br />
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Abigail
https://wikipediaquality.com/index.php?title=Analyzing_User_Click_Paths_in_a_Wikipedia_Navigation_Game&diff=24265
Analyzing User Click Paths in a Wikipedia Navigation Game
2020-05-20T05:39:12Z
<p>Abigail: Categories</p>
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<div>{{Infobox work<br />
| title = Analyzing User Click Paths in a Wikipedia Navigation Game<br />
| date = 2012<br />
| authors = [[Denis Helic]]<br />
| link = http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6240673<br />
}}<br />
'''Analyzing User Click Paths in a Wikipedia Navigation Game''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Denis Helic]].<br />
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== Overview ==<br />
Due to the enormous success of Web search technology navigation became only a second-class information seeking strategy on the Web. However, numerous studies highlight the importance of navigation as an alternative [[information retrieval]] technique to search. These studies provide evidences that the most efficient information finding occurs in the settings where search and navigation seamlessly integrate and complement each other. Recently, the research community has also recognized the importance of understanding the human navigation behavior since the knowledge on how users navigate helps in designing optimal navigation structures. In this paper authors try to gain more insight in how users navigate towards a known target page in [[Wikipedia]]. To that end, authors conduct an initial analysis of user click paths from a Wikipedia navigation game. In addition, authors compare the structure of Wikipedia navigational paths with the structure of search paths in [[social network]]s and routing paths in general complex networks.<br />
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Helic, Denis. (2012). "[[Analyzing User Click Paths in a Wikipedia Navigation Game]]".<br />
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{{cite journal |last1=Helic |first1=Denis |title=Analyzing User Click Paths in a Wikipedia Navigation Game |date=2012 |url=https://wikipediaquality.com/wiki/Analyzing_User_Click_Paths_in_a_Wikipedia_Navigation_Game}}<br />
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Helic, Denis. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Analyzing_User_Click_Paths_in_a_Wikipedia_Navigation_Game">Analyzing User Click Paths in a Wikipedia Navigation Game</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>
Abigail
https://wikipediaquality.com/index.php?title=Entity-Relationship_Queries_over_Wikipedia&diff=24264
Entity-Relationship Queries over Wikipedia
2020-05-20T05:36:36Z
<p>Abigail: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Entity-Relationship Queries over Wikipedia<br />
| date = 2010<br />
| authors = [[Xiaonan Li]]<br />[[Chengkai Li]]<br />[[Cong Yu]]<br />
| doi = 10.1145/1871985.1871991<br />
| link = https://dl.acm.org/citation.cfm?doid=1871985.1871991<br />
}}<br />
'''Entity-Relationship Queries over Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Xiaonan Li]], [[Chengkai Li]] and [[Cong Yu]].<br />
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== Overview ==<br />
Wikipedia is the largest user-generated knowledge base. Authors propose a structured query mechanism, entity-relationship query , for searching entities in [[Wikipedia]] corpus by their properties and inter-relationships. An entity-relationship query consists of arbitrary number of predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text rather than pre-extracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It avoids information loss that happens during extraction. Authors present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model for accurate ranking of query answers. Experiments on INEX benchmark queries and own crafted queries show the effectiveness and accuracy of ranking method.<br />
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Li, Xiaonan; Li, Chengkai; Yu, Cong. (2010). "[[Entity-Relationship Queries over Wikipedia]]".DOI: 10.1145/1871985.1871991. <br />
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{{cite journal |last1=Li |first1=Xiaonan |last2=Li |first2=Chengkai |last3=Yu |first3=Cong |title=Entity-Relationship Queries over Wikipedia |date=2010 |doi=10.1145/1871985.1871991 |url=https://wikipediaquality.com/wiki/Entity-Relationship_Queries_over_Wikipedia}}<br />
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Li, Xiaonan; Li, Chengkai; Yu, Cong. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Entity-Relationship_Queries_over_Wikipedia">Entity-Relationship Queries over Wikipedia</a>&amp;quot;.DOI: 10.1145/1871985.1871991. <br />
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Abigail
https://wikipediaquality.com/index.php?title=An_Encyclopedia,_Not_an_Experiment_in_Democracy:_Wikipedia_Biographies,_Authorship,_and_the_Wikipedia_Subject&diff=24263
An Encyclopedia, Not an Experiment in Democracy: Wikipedia Biographies, Authorship, and the Wikipedia Subject
2020-05-20T05:34:20Z
<p>Abigail: Adding embed</p>
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<div>{{Infobox work<br />
| title = An Encyclopedia, Not an Experiment in Democracy: Wikipedia Biographies, Authorship, and the Wikipedia Subject<br />
| date = 2015<br />
| authors = [[Pamela Graham]]<br />
| doi = 10.1353/bio.2015.0023<br />
| link = https://muse.jhu.edu/article/589985<br />
}}<br />
'''An Encyclopedia, Not an Experiment in Democracy: Wikipedia Biographies, Authorship, and the Wikipedia Subject''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Pamela Graham]].<br />
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== Overview ==<br />
Wikipedia biography is a culturally significant, yet overlooked form of digital life narrative. Through an examination of [[Wikipedia]]’s policies and discussion forums, and a number of its most popular and controversial biographies, this essay explores the politics of biographical practice and representation on the site.<br />
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Graham, Pamela. (2015). "[[An Encyclopedia, Not an Experiment in Democracy: Wikipedia Biographies, Authorship, and the Wikipedia Subject]]". University of Hawai'i Press. DOI: 10.1353/bio.2015.0023. <br />
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{{cite journal |last1=Graham |first1=Pamela |title=An Encyclopedia, Not an Experiment in Democracy: Wikipedia Biographies, Authorship, and the Wikipedia Subject |date=2015 |doi=10.1353/bio.2015.0023 |url=https://wikipediaquality.com/wiki/An_Encyclopedia,_Not_an_Experiment_in_Democracy:_Wikipedia_Biographies,_Authorship,_and_the_Wikipedia_Subject |journal=University of Hawai'i Press}}<br />
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Graham, Pamela. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/An_Encyclopedia,_Not_an_Experiment_in_Democracy:_Wikipedia_Biographies,_Authorship,_and_the_Wikipedia_Subject">An Encyclopedia, Not an Experiment in Democracy: Wikipedia Biographies, Authorship, and the Wikipedia Subject</a>&amp;quot;. University of Hawai'i Press. DOI: 10.1353/bio.2015.0023. <br />
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Abigail
https://wikipediaquality.com/index.php?title=Work-To-Rule:_the_Emergence_of_Algorithmic_Governance_in_Wikipedia&diff=24262
Work-To-Rule: the Emergence of Algorithmic Governance in Wikipedia
2020-05-20T05:33:15Z
<p>Abigail: Int.links</p>
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<div>'''Work-To-Rule: the Emergence of Algorithmic Governance in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Claudia Müller-Birn]], [[Leonhard Dobusch]] and [[James D. Herbsleb]].<br />
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== Overview ==<br />
Research has shown the importance of a functioning governance system for the success of peer production communities. It particularly highlights the role of human coordination and communication within the governance regime. In this article, authors extend this line of research by differentiating two [[categories]] of governance mechanisms. The first category is based primarily on communication, in which social norms emerge that are often formalized by written rules and guidelines. The second category refers to the technical infrastructure that enables users to access artifacts, and that allows the community to communicate and coordinate their collective actions to create those artifacts. Authors collected qualitative and quantitative data from [[Wikipedia]] in order to show how a community's consensus gradually converts social mechanisms into algorithmic mechanisms. In detail, authors analyze algorithmic governance mechanisms in two embedded cases: the software extension "flagged revisions" and the bot "xqbot". Authors insights point towards a growing relevance of algorithmic governance in the realm of governing large-scale peer production communities. This extends previous research, in which algorithmic governance is almost absent. Further research is needed to unfold, understand, and also modify existing interdependencies between social and algorithmic governance mechanisms.</div>
Abigail
https://wikipediaquality.com/index.php?title=Arabic_Named_Entity_Recognition_Process_Using_Transducer_Cascade_and_Arabic_Wikipedia&diff=24261
Arabic Named Entity Recognition Process Using Transducer Cascade and Arabic Wikipedia
2020-05-20T05:31:09Z
<p>Abigail: infobox</p>
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<div>{{Infobox work<br />
| title = Arabic Named Entity Recognition Process Using Transducer Cascade and Arabic Wikipedia<br />
| date = 2015<br />
| authors = [[Fatma Ben Mesmia]]<br />[[Kais Haddar]]<br />[[Denis Maurel]]<br />[[Nathalie Friburger]]<br />
| link = http://aclweb.org/anthology/R/R15/R15-1007.pdf<br />
}}<br />
'''Arabic Named Entity Recognition Process Using Transducer Cascade and Arabic Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Fatma Ben Mesmia]], [[Kais Haddar]], [[Denis Maurel]] and [[Nathalie Friburger]].<br />
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== Overview ==<br />
Transducers namely transducer cascades are used in several NLP-applications such as Arabic [[named entity recognition]] (ANER). To experiment and evaluate an ANER process, a weight coverage corpus is necessary. In this paper, authors propose an ANER method based on transducer cascade. The proposed transducer cascade is generated with the CasSys tool integrated in Unitex linguistic platform. The experimentation of method is done on a [[Wikipedia]] corpus. The Wikipedia text format is obtained with Kiwix tool. The experiment results are satisfactory based on calculated [[measures]].</div>
Abigail
https://wikipediaquality.com/index.php?title=A_Vision_for_Performing_Social_and_Economic_Data_Analysis_Using_Wikipedia%27s_Edit_History&diff=24260
A Vision for Performing Social and Economic Data Analysis Using Wikipedia's Edit History
2020-05-20T05:29:58Z
<p>Abigail: + wikilinks</p>
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<div>'''A Vision for Performing Social and Economic Data Analysis Using Wikipedia's Edit History''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Erik Dahm]], [[Moritz Schubotz]], [[Norman Meuschke]] and [[Bela Gipp]].<br />
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== Overview ==<br />
In this vision paper, authors suggest combining two lines of research to study the collective behavior of [[Wikipedia]] contributors. The first line of research analyzes Wikipedia's edit history to quantify the quality of individual contributions and the resulting [[reputation]] of the contributor. The second line of research surveys Wikipedia contributors to gain insights, e.g., on their personal and professional background, socioeconomic status, or motives to contribute toWikipedia. While both lines of research are valuable on their own, authors argue that the combination of both approaches could yield insights that exceed the sum of the individual parts. Linking survey data to contributor reputation and content-based quality metrics could provide a large-scale, public domain data set to perform user modeling, i.e. deducing interest profiles of user groups. User profiles can, among other applications, help to improve recommender systems. The resulting dataset can also enable a better understanding and improved prediction of high quality Wikipedia content and successfulWikipedia contributors. Furthermore, the dataset can enable novel research approaches to investigate team composition and collective behavior as well as help to identify domain experts and young talents. Authors report on the status of implementing large-scale, content-based analysis of the Wikipedia edit history using the big data processing framework Apache Flink. Additionally, authors describe plans to conduct a survey among Wikipedia contributors to enhance the content-based quality metrics.</div>
Abigail
https://wikipediaquality.com/index.php?title=Common_Translation_Errors_in_Wikipedia_Articles:_a_Corpus-Based_Study&diff=24259
Common Translation Errors in Wikipedia Articles: a Corpus-Based Study
2020-05-20T05:27:23Z
<p>Abigail: + category</p>
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<div>{{Infobox work<br />
| title = Common Translation Errors in Wikipedia Articles: a Corpus-Based Study<br />
| date = 2017<br />
| authors = [[Adéla Štromajerová]]<br />
| link = https://theses.cz/id/dtgdfv/<br />
}}<br />
'''Common Translation Errors in Wikipedia Articles: a Corpus-Based Study''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Adéla Štromajerová]].<br />
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== Overview ==<br />
Tato prace se zabýva běžnými chybami v překladech clanků na Wikipedii. Pro analýzu bylo využito paralelniho korpusu English/Czech [[Wikipedia]] Parallel Corpus. Na zakladě několika zdrojů byly identifikovany chyby, ktere by se potencialně mohly v korpusu objevit, a ty byly pote rozděleny do 6 kategorii – chyby lexikalni, gramaticke, syntakticke, ortograficke, lokalizacni a stylisticke. Chyby byly zanalyzovany v korpusu a nasledně porovnany s chybami vyskytujicimi se v korpusu BSC. K nejcastějsim chybam patřily chyby v kolokacich (chyby lexikalni), ve velkých pismenech a interpunkci (chyby ortograficke) a ve formatu cisel, dat a měn (chyby lokalizacni). Ukazalo se, že analyzovane chyby, ktere bylo možne porovnat, se ve studovanem korpusu vyskytovaly castěji než v korpusu BSC, nebo byla frekvence jejich výskytu stejna. Toto může být způsobeno tim, že korpus BSC obsahuje texty překladane univerzitnimi studenty anglickeho jazyka, kteři již maji jiste jazykove znalosti, a jejich překlady jsou tak na vyssi urovni.<br />
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Štromajerová, Adéla. (2017). "[[Common Translation Errors in Wikipedia Articles: a Corpus-Based Study]]". Masarykova univerzita. <br />
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{{cite journal |last1=Štromajerová |first1=Adéla |title=Common Translation Errors in Wikipedia Articles: a Corpus-Based Study |date=2017 |url=https://wikipediaquality.com/wiki/Common_Translation_Errors_in_Wikipedia_Articles:_a_Corpus-Based_Study |journal=Masarykova univerzita}}<br />
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Štromajerová, Adéla. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Common_Translation_Errors_in_Wikipedia_Articles:_a_Corpus-Based_Study">Common Translation Errors in Wikipedia Articles: a Corpus-Based Study</a>&amp;quot;. Masarykova univerzita. <br />
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Abigail
https://wikipediaquality.com/index.php?title=Using_Wikipedia_to_Improve_Web_Service_Discovery&diff=24258
Using Wikipedia to Improve Web Service Discovery
2020-05-20T05:25:10Z
<p>Abigail: Categories</p>
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<div>{{Infobox work<br />
| title = Using Wikipedia to Improve Web Service Discovery<br />
| date = 2012<br />
| authors = [[Alejandro Metke Jimenez]]<br />
| link = https://eprints.qut.edu.au/59632/<br />
}}<br />
'''Using Wikipedia to Improve Web Service Discovery''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Alejandro Metke Jimenez]].<br />
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== Overview ==<br />
Building and maintaining software are not easy tasks. However, thanks to advances in web technologies, a new paradigm is emerging in software development. The Service Oriented Architecture (SOA) is a relatively new approach that helps bridge the gap between business and IT and also helps systems remain<br />
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Jimenez, Alejandro Metke. (2012). "[[Using Wikipedia to Improve Web Service Discovery]]". Queensland University of Technology. <br />
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{{cite journal |last1=Jimenez |first1=Alejandro Metke |title=Using Wikipedia to Improve Web Service Discovery |date=2012 |url=https://wikipediaquality.com/wiki/Using_Wikipedia_to_Improve_Web_Service_Discovery |journal=Queensland University of Technology}}<br />
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Jimenez, Alejandro Metke. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Using_Wikipedia_to_Improve_Web_Service_Discovery">Using Wikipedia to Improve Web Service Discovery</a>&amp;quot;. Queensland University of Technology. <br />
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[[Category:Scientific works]]</div>
Abigail
https://wikipediaquality.com/index.php?title=Effective_Ontology_Learning_:_Concepts%27_Hierarchy_Building_Using_Plain_Text_Wikipedia&diff=24257
Effective Ontology Learning : Concepts' Hierarchy Building Using Plain Text Wikipedia
2020-05-20T05:22:40Z
<p>Abigail: Infobox</p>
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<div>{{Infobox work<br />
| title = Effective Ontology Learning : Concepts' Hierarchy Building Using Plain Text Wikipedia<br />
| date = 2012<br />
| authors = [[Khalida Bensidi Ahmed]]<br />[[Adil Toumouh]]<br />
| link = http://ceur-ws.org/Vol-867/Paper18.pdf<br />
}}<br />
'''Effective Ontology Learning : Concepts' Hierarchy Building Using Plain Text Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Khalida Bensidi Ahmed]] and [[Adil Toumouh]].<br />
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== Overview ==<br />
Ontologies stand in the heart of the Semantic Web. Nevertheless, heavyweight or formal ontologies’ engineering is being commonly judged to be a tough exercise which requires time and heavy costs. Ontology Learning is thus a solution for this exigency and an approach for the ‘knowledge acquisition bottleneck’. Since texts are massively available everywhere, making up of experts’ knowledge and their know-how, it is of great value to capture the knowledge existing within such texts. Authors approach is thus an interesting research work which tries to answer the challenge of creating concepts’ hierarchies from textual data. The significance of such a solution stems from the idea by which authors take advantage of the [[Wikipedia]] encyclopedia to achieve some good quality results.</div>
Abigail
https://wikipediaquality.com/index.php?title=Extending_the_Wikipedia_Recommender_System_Assessing_Expertise_of_Recommenders&diff=24256
Extending the Wikipedia Recommender System Assessing Expertise of Recommenders
2020-05-20T05:19:44Z
<p>Abigail: Categories</p>
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<div>{{Infobox work<br />
| title = Extending the Wikipedia Recommender System Assessing Expertise of Recommenders<br />
| date = 2009<br />
| authors = [[Thomas Lefevre]]<br />
| link = http://etd.dtu.dk/thesis/254285/Lefevre.pdf<br />
}}<br />
'''Extending the Wikipedia Recommender System Assessing Expertise of Recommenders''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Thomas Lefevre]].<br />
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== Overview ==<br />
The [[Wikipedia]] is a web-based encyclopedia, written and edited collaboratively<br />
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Lefevre, Thomas. (2009). "[[Extending the Wikipedia Recommender System Assessing Expertise of Recommenders]]". Technical University of Denmark (DTU) : Kgs. Lyngby, Denmark. <br />
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{{cite journal |last1=Lefevre |first1=Thomas |title=Extending the Wikipedia Recommender System Assessing Expertise of Recommenders |date=2009 |url=https://wikipediaquality.com/wiki/Extending_the_Wikipedia_Recommender_System_Assessing_Expertise_of_Recommenders |journal=Technical University of Denmark (DTU) : Kgs. Lyngby, Denmark}}<br />
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Lefevre, Thomas. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extending_the_Wikipedia_Recommender_System_Assessing_Expertise_of_Recommenders">Extending the Wikipedia Recommender System Assessing Expertise of Recommenders</a>&amp;quot;. Technical University of Denmark (DTU) : Kgs. Lyngby, Denmark. <br />
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[[Category:Scientific works]]</div>
Abigail
https://wikipediaquality.com/index.php?title=Finding_Social_Roles_in_Wikipedia&diff=24255
Finding Social Roles in Wikipedia
2020-05-20T05:17:50Z
<p>Abigail: infobox</p>
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<div>{{Infobox work<br />
| title = Finding Social Roles in Wikipedia<br />
| date = 2011<br />
| authors = [[Howard T. Welser]]<br />[[Dan Cosley]]<br />[[Gueorgi Kossinets]]<br />[[Austin Lin]]<br />[[Fedor Dokshin]]<br />[[Marc A. Smith]]<br />
| doi = 10.1145/1940761.1940778<br />
| link = http://dl.acm.org/citation.cfm?id=1940778<br />
}}<br />
'''Finding Social Roles in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Howard T. Welser]], [[Dan Cosley]], [[Gueorgi Kossinets]], [[Austin Lin]], [[Fedor Dokshin]] and [[Marc A. Smith]].<br />
<br />
== Overview ==<br />
This paper investigates some of the social roles people play in the online community of [[Wikipedia]]. Authors start from qualitative comments posted on community oriented pages, wiki project memberships, and user [[talk pages]] in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and [[social network]]ers. Patterns in edit histories and egocentric network visualizations suggest potential "structural signatures" that could be used as quantitative [[indicators]] of role adoption. Using simple metrics based on edit histories authors compare two samples of [[Wikipedians]]: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, authors find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month's cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and authors describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles.</div>
Abigail
https://wikipediaquality.com/index.php?title=Relation_Extraction_from_Wikipedia_Articles_by_Entities_Clustering&diff=24217
Relation Extraction from Wikipedia Articles by Entities Clustering
2020-05-16T04:26:30Z
<p>Abigail: Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Relation Extraction from Wikipedia Articles by Entities Clustering<br />
| date = 2012<br />
| authors = [[Song Liu]]<br />[[Fuji Ren]]<br />
| doi = 10.1109/CCIS.2012.6664633<br />
| link = <br />
}}<br />
'''Relation Extraction from Wikipedia Articles by Entities Clustering''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Song Liu]] and [[Fuji Ren]].<br />
<br />
== Overview ==<br />
Wikipedia is an encyclopedia based on wiki technology. It is [[multilingual]] high quality knowledge base. In this work a episode based extraction method are proposed to extract relations from [[Wikipedia]] articles. The entities are clustered and labeled. The relation extraction is benefited by the information redundancy provided by the clusters. A strict Wikipedia entities clustering algorithm based on the category system and first sentence of the article is approached. This work required less manual assist. And the relations are abundant. The results are comparable with other works [1, 2].<br />
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== Embed ==<br />
=== Wikipedia Quality ===<br />
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Liu, Song; Ren, Fuji. (2012). "[[Relation Extraction from Wikipedia Articles by Entities Clustering]]".DOI: 10.1109/CCIS.2012.6664633. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Liu |first1=Song |last2=Ren |first2=Fuji |title=Relation Extraction from Wikipedia Articles by Entities Clustering |date=2012 |doi=10.1109/CCIS.2012.6664633 |url=https://wikipediaquality.com/wiki/Relation_Extraction_from_Wikipedia_Articles_by_Entities_Clustering}}<br />
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=== HTML ===<br />
<code><br />
<nowiki><br />
Liu, Song; Ren, Fuji. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Relation_Extraction_from_Wikipedia_Articles_by_Entities_Clustering">Relation Extraction from Wikipedia Articles by Entities Clustering</a>&amp;quot;.DOI: 10.1109/CCIS.2012.6664633. <br />
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Abigail
https://wikipediaquality.com/index.php?title=Notability_Determination_for_Wikipedia&diff=24216
Notability Determination for Wikipedia
2020-05-16T04:23:52Z
<p>Abigail: Embed for English Wikipedia, HTML</p>
<hr />
<div>{{Infobox work<br />
| title = Notability Determination for Wikipedia<br />
| date = 2017<br />
| authors = [[Yashaswi Pochampally]]<br />[[Kamalakar Karlapalem]]<br />
| doi = 10.1145/3041021.3053361<br />
| link = http://dl.acm.org/citation.cfm?id=3053361<br />
}}<br />
'''Notability Determination for Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Yashaswi Pochampally]] and [[Kamalakar Karlapalem]].<br />
<br />
== Overview ==<br />
Being the ever-growing online encyclopedia, [[Wikipedia]] requires a keen investigation about which articles are to be included for it to maintain its indispensability. To prevent unnecessary articles from being included, official guidelines of Wikipedia demand these [[named entities]] to meet "notability" standards for their article inclusion. In this paper, authors evaluate named entities for their notability by using [[reliability]] and entity salience [[features]]. Evaluations of system provide evidence for the viability of solution as an alternative to the manual decisions made by the reviewers for inclusion of an article using the notability rules.<br />
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== Embed ==<br />
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Pochampally, Yashaswi; Karlapalem, Kamalakar. (2017). "[[Notability Determination for Wikipedia]]". International World Wide Web Conferences Steering Committee. DOI: 10.1145/3041021.3053361. <br />
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{{cite journal |last1=Pochampally |first1=Yashaswi |last2=Karlapalem |first2=Kamalakar |title=Notability Determination for Wikipedia |date=2017 |doi=10.1145/3041021.3053361 |url=https://wikipediaquality.com/wiki/Notability_Determination_for_Wikipedia |journal=International World Wide Web Conferences Steering Committee}}<br />
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Pochampally, Yashaswi; Karlapalem, Kamalakar. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Notability_Determination_for_Wikipedia">Notability Determination for Wikipedia</a>&amp;quot;. International World Wide Web Conferences Steering Committee. DOI: 10.1145/3041021.3053361. <br />
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Abigail
https://wikipediaquality.com/index.php?title=Learning_from_History:_Predicting_Reverted_Work_at_the_Word_Level_in_Wikipedia&diff=24215
Learning from History: Predicting Reverted Work at the Word Level in Wikipedia
2020-05-16T04:21:52Z
<p>Abigail: Learning from History: Predicting Reverted Work at the Word Level in Wikipedia - creating a new article</p>
<hr />
<div>'''Learning from History: Predicting Reverted Work at the Word Level in Wikipedia''' - scientific work related to Wikipedia quality published in 2012, written by Jeffrey M. Rzeszotarski and Aniket Kittur.<br />
<br />
== Overview ==<br />
Wikipedia's remarkable success in aggregating millions of contributions can pose a challenge for current editors, whose hard work may be reverted unless they understand and follow established norms, policies, and decisions and avoid contentious or proscribed terms. Authors present a machine learning model for predicting whether a contribution will be reverted based on word level features. Unlike previous models relying on editor-level characteristics, model can make accurate predictions based only on the words a contribution changes. A key advantage of the model is that it can provide feedback on not only whether a contribution is likely to be rejected, but also the particular words that are likely to be controversial, enabling new forms of intelligent interfaces and visualizations. Authors examine the performance of the model across a variety of Wikipedia articles.</div>
Abigail
https://wikipediaquality.com/index.php?title=Copyright_or_Copyleft%3F_Wikipedia_as_a_Turning_Point_for_Authorship&diff=24214
Copyright or Copyleft? Wikipedia as a Turning Point for Authorship
2020-05-16T04:19:50Z
<p>Abigail: Embed for English Wikipedia, HTML</p>
<hr />
<div>{{Infobox work<br />
| title = Copyright or Copyleft? Wikipedia as a Turning Point for Authorship<br />
| date = 2014<br />
| authors = [[Daniela Simone]]<br />
| doi = 10.2139/ssrn.2330766<br />
| link = https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2330766_code1154248.pdf?abstractid=2330766&amp;mirid=1<br />
}}<br />
'''Copyright or Copyleft? Wikipedia as a Turning Point for Authorship''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Daniela Simone]].<br />
<br />
== Overview ==<br />
The Copyright Designs and Patents Act 1988 provides that copyright subsists in original literary works and that the author of such a work is the owner of the copyright subsisting in it. This article considers the unique challenge that [[Wikipedia]] poses for copyright’s rules of subsistence. The article is structured in three parts. The first part outlines the model of collective authorship which thrives on Wikipedia – describing how Wikipedia works and suggests some reasons why it works. The second part offers a critical analysis of the relationship between collective authorship on Wikipedia and the concept of a work of joint authorship in copyright law. In particular, this part considers whether: (i) Wikipedia as a whole, or individual Wikipedia pages are original literary works in which copyright subsists; and (ii) whether Wikipedia contributors are joint authors and thus copyright owners. The third part examines the role of copyleft licences in sustaining collective authorship on Wikipedia and considers the ongoing relevance of copyright law standards in this context. The article concludes that the example of Wikipedia reveals some unanswered questions that lie at the heart of copyright’s notion of authorship, providing an opportunity to reconsider the way in which this notion has been understood to date and how it might adapt to creativity in the modern digital environment.<br />
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== Embed ==<br />
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Simone, Daniela. (2014). "[[Copyright or Copyleft? Wikipedia as a Turning Point for Authorship]]". Hart Publishing. DOI: 10.2139/ssrn.2330766. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Simone |first1=Daniela |title=Copyright or Copyleft? Wikipedia as a Turning Point for Authorship |date=2014 |doi=10.2139/ssrn.2330766 |url=https://wikipediaquality.com/wiki/Copyright_or_Copyleft?_Wikipedia_as_a_Turning_Point_for_Authorship |journal=Hart Publishing}}<br />
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=== HTML ===<br />
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Simone, Daniela. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Copyright_or_Copyleft?_Wikipedia_as_a_Turning_Point_for_Authorship">Copyright or Copyleft? Wikipedia as a Turning Point for Authorship</a>&amp;quot;. Hart Publishing. DOI: 10.2139/ssrn.2330766. <br />
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Abigail
https://wikipediaquality.com/index.php?title=Serendipitous_Browsing:_Stumbling_Through_Wikipedia&diff=24213
Serendipitous Browsing: Stumbling Through Wikipedia
2020-05-16T04:18:35Z
<p>Abigail: + embed code</p>
<hr />
<div>{{Infobox work<br />
| title = Serendipitous Browsing: Stumbling Through Wikipedia<br />
| date = 2012<br />
| authors = [[Claudia Hauff]]<br />[[Geert-Jan Houben]]<br />
| link = http://ceur-ws.org/Vol-836/paper7.pdf<br />
}}<br />
'''Serendipitous Browsing: Stumbling Through Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Claudia Hauff]] and [[Geert-Jan Houben]].<br />
<br />
== Overview ==<br />
While in the early years of the Web, searching for information and keeping in touch used to be the two main reasons for ’going online’, today authors turn to the Web in many dierent situations, including when authors look for entertainment to pass the time or relax. A popular tool to facilitate the users’ desire for entertainment is StumbleUpon, which allows users to stumble" through the Web one (semi-random) page at a time. Interestingly to us, many StumbleUpon users appreciate being served [[Wikipedia]] articles, which are informative pieces of text that educate the reader about a particular concept. The leisure activity of stumbling can thus also incorporate a learning experience. Since life-long learning is an important characteristic of knowledge economies, it is crucial to understand the interplay between these two - at rst sight - opposing forces. Authors hypothesize that a greater understanding of what makes certain Wikipedia articles more attractive to the serendipitously browsing user than others, will enable us to develop adaptations that expose a greater amount of Wikipedia articles to the leisure seeking user.<br />
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== Embed ==<br />
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Hauff, Claudia; Houben, Geert-Jan. (2012). "[[Serendipitous Browsing: Stumbling Through Wikipedia]]".<br />
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{{cite journal |last1=Hauff |first1=Claudia |last2=Houben |first2=Geert-Jan |title=Serendipitous Browsing: Stumbling Through Wikipedia |date=2012 |url=https://wikipediaquality.com/wiki/Serendipitous_Browsing:_Stumbling_Through_Wikipedia}}<br />
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=== HTML ===<br />
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Hauff, Claudia; Houben, Geert-Jan. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Serendipitous_Browsing:_Stumbling_Through_Wikipedia">Serendipitous Browsing: Stumbling Through Wikipedia</a>&amp;quot;.<br />
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Abigail
https://wikipediaquality.com/index.php?title=Wikitrip:_Animated_Visualization_over_Time_of_Geo-Location_and_Gender_of_Wikipedians_Who_Edited_a_Page&diff=24212
Wikitrip: Animated Visualization over Time of Geo-Location and Gender of Wikipedians Who Edited a Page
2020-05-16T04:17:26Z
<p>Abigail: + Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Wikitrip: Animated Visualization over Time of Geo-Location and Gender of Wikipedians Who Edited a Page<br />
| date = 2012<br />
| authors = [[Paolo Massa]]<br />[[Maurizio Napolitano]]<br />[[Federico Scrinzi]]<br />[[Michela Ferron]]<br />
| doi = 10.1145/2462932.2462980<br />
| link = http://dl.acm.org/citation.cfm?id=2462932.2462980<br />
}}<br />
'''Wikitrip: Animated Visualization over Time of Geo-Location and Gender of Wikipedians Who Edited a Page''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Paolo Massa]], [[Maurizio Napolitano]], [[Federico Scrinzi]] and [[Michela Ferron]].<br />
<br />
== Overview ==<br />
In this short paper, authors present WikiTrip, a web tool authors created and released as [[open source]] which provides a visualization over time of two kinds of information about the [[Wikipedia]]ns who edited a selected page: their location in the world and their gender. Authors also describe evidence that pages on a language edition of Wikipedia which receive most attention in terms of edits from countries where the language is not primarily spoken are about TV shows and stars, football teams or specific geographic locations.<br />
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== Embed ==<br />
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Massa, Paolo; Napolitano, Maurizio; Scrinzi, Federico; Ferron, Michela. (2012). "[[Wikitrip: Animated Visualization over Time of Geo-Location and Gender of Wikipedians Who Edited a Page]]".DOI: 10.1145/2462932.2462980. <br />
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{{cite journal |last1=Massa |first1=Paolo |last2=Napolitano |first2=Maurizio |last3=Scrinzi |first3=Federico |last4=Ferron |first4=Michela |title=Wikitrip: Animated Visualization over Time of Geo-Location and Gender of Wikipedians Who Edited a Page |date=2012 |doi=10.1145/2462932.2462980 |url=https://wikipediaquality.com/wiki/Wikitrip:_Animated_Visualization_over_Time_of_Geo-Location_and_Gender_of_Wikipedians_Who_Edited_a_Page}}<br />
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=== HTML ===<br />
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<nowiki><br />
Massa, Paolo; Napolitano, Maurizio; Scrinzi, Federico; Ferron, Michela. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikitrip:_Animated_Visualization_over_Time_of_Geo-Location_and_Gender_of_Wikipedians_Who_Edited_a_Page">Wikitrip: Animated Visualization over Time of Geo-Location and Gender of Wikipedians Who Edited a Page</a>&amp;quot;.DOI: 10.1145/2462932.2462980. <br />
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Abigail
https://wikipediaquality.com/index.php?title=Is_Wikipedia_Inefficient%3F_Modelling_Effort_and_Participation_in_Wikipedia&diff=24211
Is Wikipedia Inefficient? Modelling Effort and Participation in Wikipedia
2020-05-16T04:16:07Z
<p>Abigail: cats.</p>
<hr />
<div>{{Infobox work<br />
| title = Is Wikipedia Inefficient? Modelling Effort and Participation in Wikipedia<br />
| date = 2013<br />
| authors = [[Kevin Crowston]]<br />[[Nicolas Jullien]]<br />[[Felipe Ortega]]<br />
| link = https://ideas.repec.org/p/hal/journl/hal-00947731.html<br />
}}<br />
'''Is Wikipedia Inefficient? Modelling Effort and Participation in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Kevin Crowston]], [[Nicolas Jullien]] and [[Felipe Ortega]].<br />
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== Overview ==<br />
Concerns have been raisedabout the decreased ability of [[Wikipedia]] to recruit editors and in to harness the effort of contributors to create new articles and improve existing articles. But, as Marwell & Oliver explained,in collective projects, in the initial stage of the project, people are few and efforts costly; in the diffusion phase, the number of participants grows as their efforts are rewarding; and in the mature phase, some inefficiency may appear as the number of contributors is more than the work requires. In this paper, thanks to original data authors extract from 36 of the main language projects, authors compare the efficiency of Wikipedia projects in [[different language]]s and at different states of development to examine this effect.<br />
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== Embed ==<br />
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Crowston, Kevin; Jullien, Nicolas; Ortega, Felipe. (2013). "[[Is Wikipedia Inefficient? Modelling Effort and Participation in Wikipedia]]".<br />
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{{cite journal |last1=Crowston |first1=Kevin |last2=Jullien |first2=Nicolas |last3=Ortega |first3=Felipe |title=Is Wikipedia Inefficient? Modelling Effort and Participation in Wikipedia |date=2013 |url=https://wikipediaquality.com/wiki/Is_Wikipedia_Inefficient?_Modelling_Effort_and_Participation_in_Wikipedia}}<br />
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=== HTML ===<br />
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Crowston, Kevin; Jullien, Nicolas; Ortega, Felipe. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Is_Wikipedia_Inefficient?_Modelling_Effort_and_Participation_in_Wikipedia">Is Wikipedia Inefficient? Modelling Effort and Participation in Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>
Abigail
https://wikipediaquality.com/index.php?title=Intrinsic_Motivation_of_Open_Content_Contributions:_the_Case_of_Wikipedia&diff=24210
Intrinsic Motivation of Open Content Contributions: the Case of Wikipedia
2020-05-16T04:13:41Z
<p>Abigail: cats.</p>
<hr />
<div>{{Infobox work<br />
| title = Intrinsic Motivation of Open Content Contributions: the Case of Wikipedia<br />
| date = 2006<br />
| authors = [[Xiaoquan Zhang]]<br />[[Feng Zhu]]<br />
| link = http://ebusiness.mit.edu/wise2006/papers/3A-1_wise2006.pdf<br />
}}<br />
'''Intrinsic Motivation of Open Content Contributions: the Case of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Xiaoquan Zhang]] and [[Feng Zhu]].<br />
<br />
== Overview ==<br />
Modern information technology has enabled many community-based innovations, in which looselyconnected contributors, dispersed across organizational and geographical boundaries, collaborate via the Internet. This community-based model of knowledge creation differs considerably from traditional rm-based production process and has stimulated curiosity of scholars from a variety of elds. Two well-known examples are [[open source]] software (OSS) projects (e.g., the Apache project) and open content production (e.g., [[Wikipedia]]). In OSS projects, hundreds and thousands of programmers collaborate on some software code base. In open content production, contributors codify some knowledge base with wiki, a piece of server software that allows them to freely create and edit Web page content.<br />
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== Embed ==<br />
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Zhang, Xiaoquan; Zhu, Feng. (2006). "[[Intrinsic Motivation of Open Content Contributions: the Case of Wikipedia]]".<br />
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{{cite journal |last1=Zhang |first1=Xiaoquan |last2=Zhu |first2=Feng |title=Intrinsic Motivation of Open Content Contributions: the Case of Wikipedia |date=2006 |url=https://wikipediaquality.com/wiki/Intrinsic_Motivation_of_Open_Content_Contributions:_the_Case_of_Wikipedia}}<br />
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=== HTML ===<br />
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Zhang, Xiaoquan; Zhu, Feng. (2006). &amp;quot;<a href="https://wikipediaquality.com/wiki/Intrinsic_Motivation_of_Open_Content_Contributions:_the_Case_of_Wikipedia">Intrinsic Motivation of Open Content Contributions: the Case of Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>
Abigail
https://wikipediaquality.com/index.php?title=Learning_About_Team_Collaboration_from_Wikipedia_Edit_History&diff=24209
Learning About Team Collaboration from Wikipedia Edit History
2020-05-16T04:12:10Z
<p>Abigail: Category</p>
<hr />
<div>{{Infobox work<br />
| title = Learning About Team Collaboration from Wikipedia Edit History<br />
| date = 2010<br />
| authors = [[Adam Wierzbicki]]<br />[[Piotr Turek]]<br />[[Radoslaw Nielek]]<br />
| doi = 10.1145/1832772.1832806<br />
| link = https://dl.acm.org/citation.cfm?doid=1832772.1832806<br />
}}<br />
'''Learning About Team Collaboration from Wikipedia Edit History''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Adam Wierzbicki]], [[Piotr Turek]] and [[Radoslaw Nielek]].<br />
<br />
== Overview ==<br />
This work presents an evalation method of teams of authors in [[Wikipedia]] based on [[social network]] analysis. Authors have created an implicit social network based on the edit history of articles. This network consists of four dimensions: trust, distrust, acquaintance and knowledge. Trust and distrust are based on content modifications (copying and deleting respectively); acquaintance is based on the amount of discussion on articles' [[talk pages]] between a given pair of authors and knowledge is based on the [[categories]] in which an author typically contributes. As authors edit the Wikipedia, the social network grows and changes to take into account their collaboration patterns, creating a succinct summary of entire edit history.<br />
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== Embed ==<br />
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Wierzbicki, Adam; Turek, Piotr; Nielek, Radoslaw. (2010). "[[Learning About Team Collaboration from Wikipedia Edit History]]".DOI: 10.1145/1832772.1832806. <br />
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{{cite journal |last1=Wierzbicki |first1=Adam |last2=Turek |first2=Piotr |last3=Nielek |first3=Radoslaw |title=Learning About Team Collaboration from Wikipedia Edit History |date=2010 |doi=10.1145/1832772.1832806 |url=https://wikipediaquality.com/wiki/Learning_About_Team_Collaboration_from_Wikipedia_Edit_History}}<br />
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=== HTML ===<br />
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Wierzbicki, Adam; Turek, Piotr; Nielek, Radoslaw. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Learning_About_Team_Collaboration_from_Wikipedia_Edit_History">Learning About Team Collaboration from Wikipedia Edit History</a>&amp;quot;.DOI: 10.1145/1832772.1832806. <br />
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[[Category:Scientific works]]</div>
Abigail
https://wikipediaquality.com/index.php?title=Verifying_Social_Network_Models_of_Wikipedia_Knowledge_Community&diff=24208
Verifying Social Network Models of Wikipedia Knowledge Community
2020-05-16T04:10:03Z
<p>Abigail: + categories</p>
<hr />
<div>{{Infobox work<br />
| title = Verifying Social Network Models of Wikipedia Knowledge Community<br />
| date = 2016<br />
| authors = [[Michal Jankowski-Lorek]]<br />[[Szymon Jaroszewicz]]<br />[[Łukasz Ostrowski]]<br />[[Adam Wierzbicki]]<br />
| doi = 10.1016/j.ins.2015.12.015<br />
| link = http://www.sciencedirect.com/science/article/pii/S0020025515009044<br />
}}<br />
'''Verifying Social Network Models of Wikipedia Knowledge Community''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Michal Jankowski-Lorek]], [[Szymon Jaroszewicz]], [[Łukasz Ostrowski]] and [[Adam Wierzbicki]].<br />
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== Overview ==<br />
The [[Wikipedia]] project has created one of the largest and best-known open knowledge communities. This community is a model for several similar efforts, both public and commercial, and even for the knowledge economy of the future e-society. For these reasons, issues of quality, social processes, and motivation within the Wikipedia knowledge community have attracted attention of researchers. Research has often used Social Network Analysis applied to networks created based on behavioral data available from the edit history of the Wikipedia.This paper asks the following question: are the popular assumptions about the social interpretations of networks created from the edit history valid? Authors verify commonly assumed interpretations of four types of networks created from discussions on Wikipedia [[talk pages]], co-edits and reverts in Wikipedia articles, and edits of articles in various topics, by comparing these networks with results from a survey of editors of the Polish [[Wikipedia community]]. The results indicate that while the behavioral networks are strongly related to the declarations of respondents, only in one case of the network created from talk pages and interpreted as acquaintance authors can observe a near equivalence. The article next describes improved definitions of behavioral [[indicators]] obtained through machine learning. The improved networks are much closer to their declarative counterparts.The main contribution of the article is a validated model of an acquaintance network among [[Wikipedia editors]] that can be derived from behavioral data and validly interpreted as acquaintance. Other contributions are improved versions of behavioral networks based on editing behavior and discussion history on the Wikipedia.<br />
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Jankowski-Lorek, Michal; Jaroszewicz, Szymon; Ostrowski, Łukasz; Wierzbicki, Adam. (2016). "[[Verifying Social Network Models of Wikipedia Knowledge Community]]". Elsevier Science Inc.. DOI: 10.1016/j.ins.2015.12.015. <br />
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{{cite journal |last1=Jankowski-Lorek |first1=Michal |last2=Jaroszewicz |first2=Szymon |last3=Ostrowski |first3=Łukasz |last4=Wierzbicki |first4=Adam |title=Verifying Social Network Models of Wikipedia Knowledge Community |date=2016 |doi=10.1016/j.ins.2015.12.015 |url=https://wikipediaquality.com/wiki/Verifying_Social_Network_Models_of_Wikipedia_Knowledge_Community |journal=Elsevier Science Inc.}}<br />
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Jankowski-Lorek, Michal; Jaroszewicz, Szymon; Ostrowski, Łukasz; Wierzbicki, Adam. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Verifying_Social_Network_Models_of_Wikipedia_Knowledge_Community">Verifying Social Network Models of Wikipedia Knowledge Community</a>&amp;quot;. Elsevier Science Inc.. DOI: 10.1016/j.ins.2015.12.015. <br />
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[[Category:Scientific works]]<br />
[[Category:Polish Wikipedia]]</div>
Abigail
https://wikipediaquality.com/index.php?title=Mining_Semantic_Relationships_Between_Concepts_Across_Documents_Incorporating_Wikipedia_Knowledge&diff=24207
Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge
2020-05-16T04:08:59Z
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<div>{{Infobox work<br />
| title = Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge<br />
| date = 2013<br />
| authors = [[Peng Yan]]<br />[[Wei Jin]]<br />
| doi = 10.1007/978-3-642-39736-3_6<br />
| link = https://dl.acm.org/citation.cfm?id=2529174<br />
}}<br />
'''Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Peng Yan]] and [[Wei Jin]].<br />
<br />
== Overview ==<br />
The ongoing astounding growth of text data has created an enormous need for fast and efficient text mining algorithms. Traditional approaches for document representation are mostly based on the Bag of Words (BOW) model which takes a document as an unordered collection of words. However, when applied in fine-grained information discovery tasks, such as mining semantic relationships between concepts, sorely relying on the BOW representation may not be sufficient to identify all potential relationships since the resulting associations based on the BOW approach are limited to the concepts that appear in the document collection literally. In this paper, authors attempt to complement existing information in the corpus by proposing a new hybrid approach, which mines semantic associations between concepts across multiple text units through incorporating extensive knowledge from [[Wikipedia]]. The experimental evaluation demonstrates that search performance has been significantly enhanced in terms of accuracy and coverage compared with a purely BOW-based approach and alternative solutions where only the article contents of Wikipedia or category information are considered.<br />
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Yan, Peng; Jin, Wei. (2013). "[[Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-39736-3_6. <br />
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{{cite journal |last1=Yan |first1=Peng |last2=Jin |first2=Wei |title=Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge |date=2013 |doi=10.1007/978-3-642-39736-3_6 |url=https://wikipediaquality.com/wiki/Mining_Semantic_Relationships_Between_Concepts_Across_Documents_Incorporating_Wikipedia_Knowledge |journal=Springer, Berlin, Heidelberg}}<br />
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Yan, Peng; Jin, Wei. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Mining_Semantic_Relationships_Between_Concepts_Across_Documents_Incorporating_Wikipedia_Knowledge">Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-39736-3_6. <br />
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[[Category:Scientific works]]</div>
Abigail
https://wikipediaquality.com/index.php?title=Spatiotemporal_Mapping_of_Wikipedia_Concepts&diff=24206
Spatiotemporal Mapping of Wikipedia Concepts
2020-05-16T04:06:26Z
<p>Abigail: + embed code</p>
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<div>{{Infobox work<br />
| title = Spatiotemporal Mapping of Wikipedia Concepts<br />
| date = 2010<br />
| authors = [[Adrian Popescu]]<br />[[Gregory Grefenstette]]<br />
| doi = 10.1145/1816123.1816142<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=1816142&amp;type=pdf<br />
| plink = https://www.researchgate.net/profile/Gregory_Grefenstette/publication/220924016_Spatiotemporal_mapping_of_Wikipedia_concepts/links/0fcfd505c1f995bcaa000000.pdf<br />
}}<br />
'''Spatiotemporal Mapping of Wikipedia Concepts''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Adrian Popescu]] and [[Gregory Grefenstette]].<br />
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== Overview ==<br />
Space and time are important dimensions in the representation of a large number of concepts. However there exists no available resource that provides spatiotemporal mappings of generic concepts. Here authors present a link-analysis based method for extracting the main locations and periods associated to all [[Wikipedia]] concepts. Relevant locations are selected from a set of geotagged articles, while relevant periods are discovered using a list of people with associated life periods. Authors analyze article versions over [[multiple languages]] and consider the strength of a spatial/temporal reference to be proportional to the number of languages in which it appears. To illustrate the utility of the spatiotemporal mapping of Wikipedia concepts, authors present an analysis of cultural interactions and a temporal analysis of two domains. The Wikipedia mapping can also be used to perform rich spatiotemporal document indexing by extracting implicit spatial and temporal references from texts.<br />
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Popescu, Adrian; Grefenstette, Gregory. (2010). "[[Spatiotemporal Mapping of Wikipedia Concepts]]".DOI: 10.1145/1816123.1816142. <br />
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{{cite journal |last1=Popescu |first1=Adrian |last2=Grefenstette |first2=Gregory |title=Spatiotemporal Mapping of Wikipedia Concepts |date=2010 |doi=10.1145/1816123.1816142 |url=https://wikipediaquality.com/wiki/Spatiotemporal_Mapping_of_Wikipedia_Concepts}}<br />
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Popescu, Adrian; Grefenstette, Gregory. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Spatiotemporal_Mapping_of_Wikipedia_Concepts">Spatiotemporal Mapping of Wikipedia Concepts</a>&amp;quot;.DOI: 10.1145/1816123.1816142. <br />
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Abigail
https://wikipediaquality.com/index.php?title=Wikipedia-Based_Semantic_Interpretation_for_Natural_Language_Processing&diff=24205
Wikipedia-Based Semantic Interpretation for Natural Language Processing
2020-05-16T04:05:19Z
<p>Abigail: Links</p>
<hr />
<div>'''Wikipedia-Based Semantic Interpretation for Natural Language Processing''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Evgeniy Gabrilovich]] and [[Shaul Markovitch]].<br />
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== Overview ==<br />
Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as [[WordNet]], or on huge manual efforts such as the CYC project. Here authors propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Authors method represents meaning in a high-dimensional space of concepts derived from [[Wikipedia]], the largest encyclopedia in existence. Authors explicitly represent the meaning of any text in terms of Wikipedia-based concepts. Authors evaluate the effectiveness of method on text categorization and on computing the degree of semantic [[relatedness]] between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.</div>
Abigail
https://wikipediaquality.com/index.php?title=Mining_Semantic_Relationships_Between_Concepts_Across_Documents_Incorporating_Wikipedia_Knowledge&diff=24204
Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge
2020-05-16T04:04:00Z
<p>Abigail: + embed code</p>
<hr />
<div>{{Infobox work<br />
| title = Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge<br />
| date = 2013<br />
| authors = [[Peng Yan]]<br />[[Wei Jin]]<br />
| doi = 10.1007/978-3-642-39736-3_6<br />
| link = https://dl.acm.org/citation.cfm?id=2529174<br />
}}<br />
'''Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Peng Yan]] and [[Wei Jin]].<br />
<br />
== Overview ==<br />
The ongoing astounding growth of text data has created an enormous need for fast and efficient text mining algorithms. Traditional approaches for document representation are mostly based on the Bag of Words (BOW) model which takes a document as an unordered collection of words. However, when applied in fine-grained information discovery tasks, such as mining semantic relationships between concepts, sorely relying on the BOW representation may not be sufficient to identify all potential relationships since the resulting associations based on the BOW approach are limited to the concepts that appear in the document collection literally. In this paper, authors attempt to complement existing information in the corpus by proposing a new hybrid approach, which mines semantic associations between concepts across multiple text units through incorporating extensive knowledge from [[Wikipedia]]. The experimental evaluation demonstrates that search performance has been significantly enhanced in terms of accuracy and coverage compared with a purely BOW-based approach and alternative solutions where only the article contents of Wikipedia or category information are considered.<br />
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Yan, Peng; Jin, Wei. (2013). "[[Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-39736-3_6. <br />
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{{cite journal |last1=Yan |first1=Peng |last2=Jin |first2=Wei |title=Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge |date=2013 |doi=10.1007/978-3-642-39736-3_6 |url=https://wikipediaquality.com/wiki/Mining_Semantic_Relationships_Between_Concepts_Across_Documents_Incorporating_Wikipedia_Knowledge |journal=Springer, Berlin, Heidelberg}}<br />
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Yan, Peng; Jin, Wei. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Mining_Semantic_Relationships_Between_Concepts_Across_Documents_Incorporating_Wikipedia_Knowledge">Mining Semantic Relationships Between Concepts Across Documents Incorporating Wikipedia Knowledge</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-39736-3_6. <br />
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Abigail
https://wikipediaquality.com/index.php?title=The_Evolution_of_Knowledge_Creation_Online:_Wikipedia_and_Knowledge_Processes&diff=24203
The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes
2020-05-16T04:01:12Z
<p>Abigail: Adding embed</p>
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<div>{{Infobox work<br />
| title = The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes<br />
| date = 2015<br />
| authors = [[Ruqin Ren]]<br />
| doi = 10.1145/2788993.2791320<br />
| link = https://dl.acm.org/citation.cfm?id=2791320<br />
}}<br />
'''The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Ruqin Ren]].<br />
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== Overview ==<br />
Using the evolutionary theory framework of the variation, retention, selection process, this paper explains the self-organized knowledge production behaviors online, with [[Wikipedia]] as an example. Evolution is presented as a trial-and-error process that produces a progressive accumulation of knowledge. The underlying theoretical assumption is that even though online communities feature very different characteristics than traditional organizations, the basic processes of trial-and-error learning in evolutionary theory still apply to the new forms of organizations. Based on the theory of self-organization system and evolution theory, the processes of variation and selection are explained in depth with examples observed on Wikipedia. The study presents a nested hierarchy of vicarious selectors that plays an important role in online knowledge creation.<br />
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Ren, Ruqin. (2015). "[[The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes]]".DOI: 10.1145/2788993.2791320. <br />
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{{cite journal |last1=Ren |first1=Ruqin |title=The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes |date=2015 |doi=10.1145/2788993.2791320 |url=https://wikipediaquality.com/wiki/The_Evolution_of_Knowledge_Creation_Online:_Wikipedia_and_Knowledge_Processes}}<br />
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Ren, Ruqin. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/The_Evolution_of_Knowledge_Creation_Online:_Wikipedia_and_Knowledge_Processes">The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes</a>&amp;quot;.DOI: 10.1145/2788993.2791320. <br />
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Abigail
https://wikipediaquality.com/index.php?title=Tractable_Probabilistic_Knowledge_Bases:_Wikipedia_and_Beyond&diff=24202
Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond
2020-05-16T04:00:06Z
<p>Abigail: Adding embed</p>
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<div>{{Infobox work<br />
| title = Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond<br />
| date = 2014<br />
| authors = [[Mathias Niepert]]<br />[[Pedro M. Domingos]]<br />
| link = https://dl.acm.org/citation.cfm?id=2908353<br />
}}<br />
'''Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Mathias Niepert]] and [[Pedro M. Domingos]].<br />
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== Overview ==<br />
Building large-scale knowledge bases from a variety of data sources is a longstanding goal of AI research. However, existing approaches either ignore the uncertainty inherent to knowledge extracted from text, the web, and other sources, or lack a consistent probabilistic semantics with tractable inference. To address this problem, authors present a framework for tractable probabilistic knowledge bases (TPKBs). TPKBs consist of a hierarchy of classes of objects and a hierarchy of classes of object pairs such that attributes and relations are independent conditioned on those classes. These characteristics facilitate both tractable probabilistic reasoning and tractable maximum-likelihood parameter learning. TPKBs feature a rich query language that allows one to express and infer complex relationships between classes, relations, objects, and their attributes. The queries are translated to sequences of operations in a relational database facilitating query execution times in the sub-second range. Authors demonstrate the power of TPKBs by leveraging large data sets extracted from [[Wikipedia]] to learn their structure and parameters. The resulting TPKB models a distribution over millions of objects and billions of parameters. Authors apply the TPKB to entity resolution and object linking problems and show that the TPKB can accurately align large knowledge bases and integrate triples from open IE projects.<br />
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Niepert, Mathias; Domingos, Pedro M.. (2014). "[[Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond]]". AAAI Press. <br />
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{{cite journal |last1=Niepert |first1=Mathias |last2=Domingos |first2=Pedro M. |title=Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond |date=2014 |url=https://wikipediaquality.com/wiki/Tractable_Probabilistic_Knowledge_Bases:_Wikipedia_and_Beyond |journal=AAAI Press}}<br />
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Niepert, Mathias; Domingos, Pedro M.. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Tractable_Probabilistic_Knowledge_Bases:_Wikipedia_and_Beyond">Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond</a>&amp;quot;. AAAI Press. <br />
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Abigail
https://wikipediaquality.com/index.php?title=A_Jester%27s_Promenade:_Citations_to_Wikipedia_in_Law_Reviews,_2002-2008&diff=24201
A Jester's Promenade: Citations to Wikipedia in Law Reviews, 2002-2008
2020-05-16T03:57:44Z
<p>Abigail: Infobox</p>
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<div>{{Infobox work<br />
| title = A Jester's Promenade: Citations to Wikipedia in Law Reviews, 2002-2008<br />
| date = 2012<br />
| authors = [[Daniel J. Baker]]<br />
| link = https://works.bepress.com/aallcallforpapers/68/<br />
}}<br />
'''A Jester's Promenade: Citations to Wikipedia in Law Reviews, 2002-2008''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Daniel J. Baker]].<br />
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== Overview ==<br />
Due to its perceived omniscience and ease-of-use, reliance on the online encyclopedia [[Wikipedia]] as a source for information has become pervasive. As a result, scholars and commentators have begun turning their attentions toward this resource and its uses. The main focus of previous writers, however, has been on the use of Wikipedia in the judicial process, whether by litigants relying on Wikipedia in their pleadings or judges relying on it in their decisions. No one, until now, has examined the use of Wikipedia in the legal scholarship context. This article intends to shine a light on the citation aspect of the Wikipedia-as-authority phenomenon by providing detailed statistics on the scope of its use and critiquing or building on the arguments of other commentators. Part II provides an overview of the debate regarding the citation of Wikipedia, beginning with a general discussion on the purposes of citation. In this Part, this article examines why some authors choose to cite to * © Daniel J. Baker 2010. This is a revised version of the winning entry in the New Member Division of the 2010 American Association of Law Libraries/LexisNexis® Call for Papers Competition. ** Law Reference/Research Librarian, O’Quinn Law Library, University of Houston Law Center. B.A., The Ohio State University; J.D., University of Cincinnati College of Law; M.L.I.S., Wayne State University. The author would like to thank David DeKorte for his help in fine-tuning the methodology; Spencer Simons, Lauren Schroeder, and Emily Lawson for their editorial insights on earlier drafts; and Mon Yin Lung, Helen Boyce, Chris Dykes, Suzanne Gordon-Martin, and Saskia Mehlhorn for their support during the writing of this article. I/S: A JOURNAL OF LAW AND POLICY FOR THE INFORMATION SOCIETY 2 I/S: A JOURNAL OF LAW AND POLICY [Vol. 7:2 Wikipedia and explains why such citation is nonetheless problematic despite its perceived advantages. A citation analysis performed on works published by nearly 500 American law reviews between 2002 and 2008 is the focus of Part III, from a description of the methodology to an examination of the results of the analysis and any trends that may be discerned from the statistics. Finally, Part IV examines the propriety of citing to Wikipedia, culminating in a call for tighter editorial standards in law reviews. In all that Author endure, of one thing Author am sure Knowledge and Reason change like the Season A Jester's Promenade1</div>
Abigail
https://wikipediaquality.com/index.php?title=Abrir_at_Ntcir-9_Geotime_Task_Usage_of_Wikipedia_and_Geonames_for_Handling_Named_Entity_Information&diff=24200
Abrir at Ntcir-9 Geotime Task Usage of Wikipedia and Geonames for Handling Named Entity Information
2020-05-16T03:56:33Z
<p>Abigail: Adding categories</p>
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<div>{{Infobox work<br />
| title = Abrir at Ntcir-9 Geotime Task Usage of Wikipedia and Geonames for Handling Named Entity Information<br />
| date = 2011<br />
| authors = [[Masaharu Yoshioka]]<br />
| link = http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings9/NTCIR/12-NTCIR9-GEOTIME-YoshiokaM.pdf<br />
}}<br />
'''Abrir at Ntcir-9 Geotime Task Usage of Wikipedia and Geonames for Handling Named Entity Information''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Masaharu Yoshioka]].<br />
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== Overview ==<br />
In the previous NTCIR8-GeoTime task, ABRIR (Appropriate Boolean query Reformulation for Information Retrieval) proved to be one of the most effective systems for retrieving documents with Geographic and Temporal constraints. However, failure analysis showed that the identification of [[named entities]] and relationships between these entities and the query is important in improving the quality of the system. In this paper, authors propose to use [[Wikipedia]] and GeoNames as resources for extracting knowledge about named entities. Authors also modify system to use such information.<br />
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Yoshioka, Masaharu. (2011). "[[Abrir at Ntcir-9 Geotime Task Usage of Wikipedia and Geonames for Handling Named Entity Information]]".<br />
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{{cite journal |last1=Yoshioka |first1=Masaharu |title=Abrir at Ntcir-9 Geotime Task Usage of Wikipedia and Geonames for Handling Named Entity Information |date=2011 |url=https://wikipediaquality.com/wiki/Abrir_at_Ntcir-9_Geotime_Task_Usage_of_Wikipedia_and_Geonames_for_Handling_Named_Entity_Information}}<br />
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Yoshioka, Masaharu. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Abrir_at_Ntcir-9_Geotime_Task_Usage_of_Wikipedia_and_Geonames_for_Handling_Named_Entity_Information">Abrir at Ntcir-9 Geotime Task Usage of Wikipedia and Geonames for Handling Named Entity Information</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>
Abigail
https://wikipediaquality.com/index.php?title=Dynamics_of_Conflicts_in_Wikipedia&diff=24199
Dynamics of Conflicts in Wikipedia
2020-05-16T03:54:33Z
<p>Abigail: + links</p>
<hr />
<div>'''Dynamics of Conflicts in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Taha Yasseri]], [[Robert Sumi]], [[András Rung]], [[András Kornai]], [[András Kornai]] and [[János Kertész]].<br />
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== Overview ==<br />
In this work authors study the dynamical [[features]] of editorial wars in [[Wikipedia]] (WP). Based on previously established algorithm, authors build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, authors show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies. On long time scales, authors identify three distinct developmental patterns for the overall behavior of the articles. Authors are able to distinguish cases eventually leading to consensus from those cases where a compromise is far from achievable. Finally, authors analyze discussion networks and conclude that edit wars are mainly fought by few editors only.</div>
Abigail
https://wikipediaquality.com/index.php?title=Towards_Detection_of_Influential_Sentences_Affecting_Reputation_in_Wikipedia&diff=24198
Towards Detection of Influential Sentences Affecting Reputation in Wikipedia
2020-05-16T03:52:02Z
<p>Abigail: + Embed</p>
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<div>{{Infobox work<br />
| title = Towards Detection of Influential Sentences Affecting Reputation in Wikipedia<br />
| date = 2016<br />
| authors = [[Yiwei Zhou]]<br />[[Alexandra I. Cristea]]<br />
| doi = 10.1145/2908131.2908177<br />
| link = http://dl.acm.org/citation.cfm?id=2908177<br />
| plink = https://www.semanticscholar.org/paper/Towards-detection-of-influential-sentences-in-Zhou-Cristea/1e558fb6dd89aba7bda031f0daa44211eb20011e<br />
}}<br />
'''Towards Detection of Influential Sentences Affecting Reputation in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Yiwei Zhou]] and [[Alexandra I. Cristea]].<br />
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== Overview ==<br />
Wikipedia has become the most frequently viewed online encyclopaedia website. Some sentences in [[Wikipedia]] articles have direct and obvious impact on people's opinions towards the mentioned [[named entities]]. This paper defines and tackles the problem of [[reputation]]-influential sentence detection in Wikipedia articles from various domains. Authors leverage multiple lexicons, to generate domain independent [[features]] . Authors generate topical features and word embedding features from unlabelled dataset, to boost the classification performance. Authors conduct several experiments, to prove the effectiveness of these features. Authors further adapt a two-step binary classification method , to perform multi-classification. Authors evaluation results show that this method outperforms the state-of-the-art one-vs-one multi-classification method for this problem.<br />
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== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Zhou, Yiwei; Cristea, Alexandra I.. (2016). "[[Towards Detection of Influential Sentences Affecting Reputation in Wikipedia]]".DOI: 10.1145/2908131.2908177. <br />
</nowiki><br />
</code><br />
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=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Zhou |first1=Yiwei |last2=Cristea |first2=Alexandra I. |title=Towards Detection of Influential Sentences Affecting Reputation in Wikipedia |date=2016 |doi=10.1145/2908131.2908177 |url=https://wikipediaquality.com/wiki/Towards_Detection_of_Influential_Sentences_Affecting_Reputation_in_Wikipedia}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Zhou, Yiwei; Cristea, Alexandra I.. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Towards_Detection_of_Influential_Sentences_Affecting_Reputation_in_Wikipedia">Towards Detection of Influential Sentences Affecting Reputation in Wikipedia</a>&amp;quot;.DOI: 10.1145/2908131.2908177. <br />
</nowiki><br />
</code></div>
Abigail
https://wikipediaquality.com/index.php?title=Citations_to_Wikipedia_in_Canadian_Law_Journal_and_Law_Review_Articles&diff=24197
Citations to Wikipedia in Canadian Law Journal and Law Review Articles
2020-05-16T03:50:00Z
<p>Abigail: + embed code</p>
<hr />
<div>{{Infobox work<br />
| title = Citations to Wikipedia in Canadian Law Journal and Law Review Articles<br />
| date = 2015<br />
| authors = [[Rex Shoyama]]<br />
| link = https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2578678<br />
}}<br />
'''Citations to Wikipedia in Canadian Law Journal and Law Review Articles''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Rex Shoyama]].<br />
<br />
== Overview ==<br />
To better understand how and why [[Wikipedia]] is utilized in Canadian legal scholarship, the author conducted a citation analysis of Canadian law journal and law review articles. This article presents the findings of this analysis, along with a discussion of implications and possible themes for future research. Overall, most Canadian authors appear to be quite selective and conservative when it comes to citing Wikipedia. However, the nature of such citations to Wikipedia indicates that legal researchers may need to develop greater information literacy skills when it comes to supporting assertions based on non-legal information sources (particularly with respect to statistical data, historical information and technological definitions). Law librarians may have an important educational role to play in this regard.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Shoyama, Rex. (2015). "[[Citations to Wikipedia in Canadian Law Journal and Law Review Articles]]".<br />
</nowiki><br />
</code><br />
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=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Shoyama |first1=Rex |title=Citations to Wikipedia in Canadian Law Journal and Law Review Articles |date=2015 |url=https://wikipediaquality.com/wiki/Citations_to_Wikipedia_in_Canadian_Law_Journal_and_Law_Review_Articles}}<br />
</nowiki><br />
</code><br />
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=== HTML ===<br />
<code><br />
<nowiki><br />
Shoyama, Rex. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Citations_to_Wikipedia_in_Canadian_Law_Journal_and_Law_Review_Articles">Citations to Wikipedia in Canadian Law Journal and Law Review Articles</a>&amp;quot;.<br />
</nowiki><br />
</code></div>
Abigail
https://wikipediaquality.com/index.php?title=Dcu_at_Wikipediamm_2009:_Document_Expansion_from_Wikipedia_Abstracts&diff=24196
Dcu at Wikipediamm 2009: Document Expansion from Wikipedia Abstracts
2020-05-16T03:48:18Z
<p>Abigail: Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = Dcu at Wikipediamm 2009: Document Expansion from Wikipedia Abstracts<br />
| date = 2009<br />
| authors = [[Jinming Min]]<br />[[Peter Wilkins]]<br />[[Johannes Leveling]]<br />[[Gareth J. F. Jones]]<br />
| link = http://ceur-ws.org/Vol-1175/CLEF2009wn-ImageCLEF-MinEt2009.pdf<br />
}}<br />
'''Dcu at Wikipediamm 2009: Document Expansion from Wikipedia Abstracts''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Jinming Min]], [[Peter Wilkins]], [[Johannes Leveling]] and [[Gareth J. F. Jones]].<br />
<br />
== Overview ==<br />
In this paper, authors describe participation in the [[Wikipedia]]MM task at CLEF 2009. Authors main efforts concern the expansion of the image metadata from the Wikipedia abstracts collection [[DBpedia]]. Since the metadata is short for retrieval by query words, authors decided to expand the metadata using a typical query expansion method. In our</div>
Abigail
https://wikipediaquality.com/index.php?title=Integration_of_Knowledge_on_Wikipedia_and_Other_Web_Resources&diff=24195
Integration of Knowledge on Wikipedia and Other Web Resources
2020-05-16T03:45:28Z
<p>Abigail: + Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Integration of Knowledge on Wikipedia and Other Web Resources<br />
| date = 2011<br />
| authors = [[Eklou Damien]]<br />[[Yasuhito Asano]]<br />[[Masatoshi Yoshikawa]]<br />
| link = http://db-event.jpn.org/deim2011/proceedings/pdf/f3-3.pdf<br />
}}<br />
'''Integration of Knowledge on Wikipedia and Other Web Resources''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Eklou Damien]], [[Yasuhito Asano]] and [[Masatoshi Yoshikawa]].<br />
<br />
== Overview ==<br />
Looking for desired information on the web can be a time consuming task. In this process [[Wikipedia]] constitutes a very helpful tool as it is the largest and most popular general reference site on the internet. Most search engines actually rank Wikipedia pages among the top listed results. However due to the nature of Wikipedia which is manually updated by users, it is virtually impossible to have all the valuable information related to a subject covered in a single article. In order to support the user search experience, authors propose a method for finding valuable information not included in Wikipedia from other web resources.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Damien, Eklou; Asano, Yasuhito; Yoshikawa, Masatoshi. (2011). "[[Integration of Knowledge on Wikipedia and Other Web Resources]]".<br />
</nowiki><br />
</code><br />
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=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Damien |first1=Eklou |last2=Asano |first2=Yasuhito |last3=Yoshikawa |first3=Masatoshi |title=Integration of Knowledge on Wikipedia and Other Web Resources |date=2011 |url=https://wikipediaquality.com/wiki/Integration_of_Knowledge_on_Wikipedia_and_Other_Web_Resources}}<br />
</nowiki><br />
</code><br />
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=== HTML ===<br />
<code><br />
<nowiki><br />
Damien, Eklou; Asano, Yasuhito; Yoshikawa, Masatoshi. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Integration_of_Knowledge_on_Wikipedia_and_Other_Web_Resources">Integration of Knowledge on Wikipedia and Other Web Resources</a>&amp;quot;.<br />
</nowiki><br />
</code></div>
Abigail
https://wikipediaquality.com/index.php?title=Trust_in_Online_Information_-_a_Comparison_Among_High_School_Students,_College_Students_and_Phd_Students_with_Regard_to_Trust_in_Wikipedia&diff=24194
Trust in Online Information - a Comparison Among High School Students, College Students and Phd Students with Regard to Trust in Wikipedia
2020-05-16T03:42:31Z
<p>Abigail: + Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Trust in Online Information - a Comparison Among High School Students, College Students and Phd Students with Regard to Trust in Wikipedia<br />
| date = 2012<br />
| authors = [[Rienco Muilwijk]]<br />
| link = http://essay.utwente.nl/61631/<br />
}}<br />
'''Trust in Online Information - a Comparison Among High School Students, College Students and Phd Students with Regard to Trust in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Rienco Muilwijk]].<br />
<br />
== Overview ==<br />
With the advent of the World Wide Web, it has become easy to obtain more information in less time. Accessibility, quantity and speed have been improved in the past years, but what about [[information quality]]? The current study focuses on how users perceive the trustworthiness of information. Three user groups, namely high school students, college students, and PhD students, made trust judgments on [[Wikipedia]]-articles which varied in quality and familiarity to the user. These three user groups were selected because as a consequence of differences in age and education progression, they are expected to differ as well in the development of information problem solving skills. User‟s information problem solving skills determine, together with source experience and domain expertise, a trust judgment. These three user characteristics can be found in the 3S-model (Lucassen & Schraagen, 2011), which differentiates three strategies (source, surface, and semantics) applied by a user to evaluate information‟s [[credibility]]. Two relations of the 3S-model were tested in this study: on the one hand the relation between the application of a surface strategy and the degree of information skills (knowledge how to evaluate online information, Metzger, 2007); on the other hand, the relation between the application semantic strategy and the degree of domain expertise. Through the think aloud method participants indicated which information [[features]] (e.g., authority, accuracy, [[completeness]], length) they attended to while making trust judgments. Based on the 3S-model, a coding scheme was developed. For each group, all remarks were coded and counted in order to include them in the coding scheme. The coded remarks were compared to each other to find differences and similarities between the user groups in feature and strategy application. Results show that high school students differed from college students and PhD students in feature and strategy application. High school students frequently mentioned the accuracy of information whereby semantic strategy was applied. College students and PhD students predominantly attended to authority. Presumably, their information skills enabled them to apply a surface strategy more than a semantic strategy. As expected, all three groups have in common that they apply more semantic strategy when confronted with familiar topics compared with unfamiliar topics.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Muilwijk, Rienco. (2012). "[[Trust in Online Information - a Comparison Among High School Students, College Students and Phd Students with Regard to Trust in Wikipedia]]".<br />
</nowiki><br />
</code><br />
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=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Muilwijk |first1=Rienco |title=Trust in Online Information - a Comparison Among High School Students, College Students and Phd Students with Regard to Trust in Wikipedia |date=2012 |url=https://wikipediaquality.com/wiki/Trust_in_Online_Information_-_a_Comparison_Among_High_School_Students,_College_Students_and_Phd_Students_with_Regard_to_Trust_in_Wikipedia}}<br />
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</code><br />
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=== HTML ===<br />
<code><br />
<nowiki><br />
Muilwijk, Rienco. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Trust_in_Online_Information_-_a_Comparison_Among_High_School_Students,_College_Students_and_Phd_Students_with_Regard_to_Trust_in_Wikipedia">Trust in Online Information - a Comparison Among High School Students, College Students and Phd Students with Regard to Trust in Wikipedia</a>&amp;quot;.<br />
</nowiki><br />
</code></div>
Abigail
https://wikipediaquality.com/index.php?title=Good_Faith_Collaboration:_the_Culture_of_Wikipedia&diff=24193
Good Faith Collaboration: the Culture of Wikipedia
2020-05-16T03:39:54Z
<p>Abigail: + Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = Good Faith Collaboration: the Culture of Wikipedia<br />
| date = 2013<br />
| authors = [[Mayo Fuster Morell]]<br />
| doi = 10.1080/1369118X.2011.602092<br />
| link = http://www.tandfonline.com/doi/abs/10.1080/1369118X.2011.602092<br />
}}<br />
'''Good Faith Collaboration: the Culture of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Mayo Fuster Morell]].<br />
<br />
== Overview ==<br />
The image of a community shaped in a virtual environment sounded rather ‘psychedelic’ before the advent of the Internet. Rheingold (1993) proposed the term ‘virtual community’ to connote the intense feelings of camaraderie, empathy and support he observed among people in online spaces. In this field, Reagle makes a very good contribution towards undoing the image of online collective action as ‘non-real’ (an image still frequently present among scientific researchers). Furthermore, Reagle’s book is part of the move towards focusing more attention on open content communities (as he refers to them) as specific types of communities with a knowledge-making goal; and he goes beyond the most researched case of free source communities.</div>
Abigail
https://wikipediaquality.com/index.php?title=The_Legal_Consciousness_of_Wikipedia&diff=24192
The Legal Consciousness of Wikipedia
2020-05-16T03:38:49Z
<p>Abigail: Adding embed</p>
<hr />
<div>{{Infobox work<br />
| title = The Legal Consciousness of Wikipedia<br />
| date = 2014<br />
| authors = [[Ayelet Oz]]<br />
| doi = 10.2139/ssrn.2572381<br />
| link = https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2572381_code1343579.pdf?abstractid=2572381&amp;mirid=1&amp;type=2<br />
}}<br />
'''The Legal Consciousness of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Ayelet Oz]].<br />
<br />
== Overview ==<br />
For the last two decades, socio-legal scholars have studied the way ordinary people, and predominantly disempowered people, experience and understand law. Only a few studies have focused on the legal consciousness of the upper-middle class or those who hold greater economic, social or symbolic power. The following dissertation adds to this body of knowledge through an online ethnography of the legal consciousness of the editors of [[Wikipedia]]. As the dissertation reveals, legality holds a surprisingly central place in Wikipedia, especially given the expressed rejection of legality in the community’s ethos. [[Wikipedians]] manage a complex and delicate system of formal rules and dispute-resolution institutions that extensively use legal vocabulary and rely on the paradigmatic structures and images of national law. The centrality of legality in Wikipedia further poses the question of the interrelations that are created when an egalitarian, open, participatory and ad-hoc community incorporates formality, strict procedures and semi-legal institutions and vocabulary.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
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Oz, Ayelet. (2014). "[[The Legal Consciousness of Wikipedia]]".DOI: 10.2139/ssrn.2572381. <br />
</nowiki><br />
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=== English Wikipedia ===<br />
<code><br />
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{{cite journal |last1=Oz |first1=Ayelet |title=The Legal Consciousness of Wikipedia |date=2014 |doi=10.2139/ssrn.2572381 |url=https://wikipediaquality.com/wiki/The_Legal_Consciousness_of_Wikipedia}}<br />
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=== HTML ===<br />
<code><br />
<nowiki><br />
Oz, Ayelet. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/The_Legal_Consciousness_of_Wikipedia">The Legal Consciousness of Wikipedia</a>&amp;quot;.DOI: 10.2139/ssrn.2572381. <br />
</nowiki><br />
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Abigail
https://wikipediaquality.com/index.php?title=Wiki-Watchdog:_Anomaly_Detection_in_Wikipedia_Through_a_Distributional_Lens&diff=24191
Wiki-Watchdog: Anomaly Detection in Wikipedia Through a Distributional Lens
2020-05-16T03:37:12Z
<p>Abigail: Adding embed</p>
<hr />
<div>{{Infobox work<br />
| title = Wiki-Watchdog: Anomaly Detection in Wikipedia Through a Distributional Lens<br />
| date = 2011<br />
| authors = [[Chrisil Arackaparambil]]<br />[[Guanhua Yan]]<br />
| doi = 10.1109/WI-IAT.2011.86<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=2052359&amp;type=pdf<br />
}}<br />
'''Wiki-Watchdog: Anomaly Detection in Wikipedia Through a Distributional Lens''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Chrisil Arackaparambil]] and [[Guanhua Yan]].<br />
<br />
== Overview ==<br />
Wikipedia has become a standard source of reference online, and many people (some unknowingly) now trust this corpus of knowledge as an authority to fulfil their information requirements. In doing so they task the human contributors of [[Wikipedia]] with maintaining the accuracy of articles, a job that these contributors have been performing admirably. Authors study the problem of monitoring the Wikipedia corpus with the goal of emph{automated, online} anomaly detection. Authors present Wiki-watchdog, an efficient emph{distribution-based} methodology that monitors distributions of revision activity for changes. Authors show that using methods it is possible to detect the activity of bots, flash events, and outages, as they occur. Authors methods are proposed to support the monitoring of the contributors. They are useful to speed-up anomaly detection, and identify events that are hard to detect manually. Authors show the efficacy and the low false-positive rate of methods by experiments on the revision history of Wikipedia. Authors results show that distribution-based anomaly detection has a higher detection rate than traditional methods based on either volume or entropy alone. Unlike previous work on anomaly detection in information networks that worked with a static network graph, methods consider the network emph{as it evolves} and monitors properties of the network for changes. Although methodology is developed and evaluated on Wikipedia, authors believe it is an effective generic anomaly detection framework in its own right.<br />
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== Embed ==<br />
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Arackaparambil, Chrisil; Yan, Guanhua. (2011). "[[Wiki-Watchdog: Anomaly Detection in Wikipedia Through a Distributional Lens]]".DOI: 10.1109/WI-IAT.2011.86. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Arackaparambil |first1=Chrisil |last2=Yan |first2=Guanhua |title=Wiki-Watchdog: Anomaly Detection in Wikipedia Through a Distributional Lens |date=2011 |doi=10.1109/WI-IAT.2011.86 |url=https://wikipediaquality.com/wiki/Wiki-Watchdog:_Anomaly_Detection_in_Wikipedia_Through_a_Distributional_Lens}}<br />
</nowiki><br />
</code><br />
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=== HTML ===<br />
<code><br />
<nowiki><br />
Arackaparambil, Chrisil; Yan, Guanhua. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wiki-Watchdog:_Anomaly_Detection_in_Wikipedia_Through_a_Distributional_Lens">Wiki-Watchdog: Anomaly Detection in Wikipedia Through a Distributional Lens</a>&amp;quot;.DOI: 10.1109/WI-IAT.2011.86. <br />
</nowiki><br />
</code></div>
Abigail
https://wikipediaquality.com/index.php?title=Wikipedia_Workload_Analysis_for_Decentralized_Hosting&diff=24190
Wikipedia Workload Analysis for Decentralized Hosting
2020-05-16T03:36:00Z
<p>Abigail: cats.</p>
<hr />
<div>{{Infobox work<br />
| title = Wikipedia Workload Analysis for Decentralized Hosting<br />
| date = 2009<br />
| authors = [[Guido Urdaneta]]<br />[[Guillaume Pierre]]<br />[[Maarten van Steen]]<br />
| doi = 10.1016/j.comnet.2009.02.019<br />
| link = http://www.sciencedirect.com/science/article/pii/S1389128609000541<br />
}}<br />
'''Wikipedia Workload Analysis for Decentralized Hosting''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Guido Urdaneta]], [[Guillaume Pierre]] and [[Maarten van Steen]].<br />
<br />
== Overview ==<br />
Authors study an access trace containing a sample of [[Wikipedia]]'s traffic over a 107-day period aiming to identify appropriate replication and distribution strategies in a fully decentralized hosting environment. Authors perform a global analysis of the whole trace, and a detailed analysis of the requests directed to the English edition of Wikipedia. In study, authors classify client requests and examine aspects such as the number of read and save operations, significant load variations and requests for nonexisting pages. Authors also review proposed decentralized wiki architectures and discuss how they would handle Wikipedia's workload. Authors conclude that decentralized architectures must focus on applying techniques to efficiently handle read operations while maintaining consistency and dealing with typical issues on decentralized systems such as churn, unbalanced loads and malicious participating nodes.<br />
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== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Urdaneta, Guido; Pierre, Guillaume; Steen, Maarten van. (2009). "[[Wikipedia Workload Analysis for Decentralized Hosting]]". Elsevier. DOI: 10.1016/j.comnet.2009.02.019. <br />
</nowiki><br />
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=== English Wikipedia ===<br />
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<nowiki><br />
{{cite journal |last1=Urdaneta |first1=Guido |last2=Pierre |first2=Guillaume |last3=Steen |first3=Maarten van |title=Wikipedia Workload Analysis for Decentralized Hosting |date=2009 |doi=10.1016/j.comnet.2009.02.019 |url=https://wikipediaquality.com/wiki/Wikipedia_Workload_Analysis_for_Decentralized_Hosting |journal=Elsevier}}<br />
</nowiki><br />
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=== HTML ===<br />
<code><br />
<nowiki><br />
Urdaneta, Guido; Pierre, Guillaume; Steen, Maarten van. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_Workload_Analysis_for_Decentralized_Hosting">Wikipedia Workload Analysis for Decentralized Hosting</a>&amp;quot;. Elsevier. DOI: 10.1016/j.comnet.2009.02.019. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]<br />
[[Category:English Wikipedia]]</div>
Abigail
https://wikipediaquality.com/index.php?title=Infoguides:_Women_on_Wikipedia_Edit-A-Thon:_What_to_Do_Next..&diff=24189
Infoguides: Women on Wikipedia Edit-A-Thon: What to Do Next..
2020-05-16T03:33:25Z
<p>Abigail: Embed for English Wikipedia, HTML</p>
<hr />
<div>{{Infobox work<br />
| title = Infoguides: Women on Wikipedia Edit-A-Thon: What to Do Next..<br />
| date = 2017<br />
| authors = [[Lara Nicosia]]<br />
| link = http://infoguides.rit.edu/WomenWikiRIT/more<br />
}}<br />
'''Infoguides: Women on Wikipedia Edit-A-Thon: What to Do Next..''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Lara Nicosia]].<br />
<br />
== Overview ==<br />
Information and resources relevant to the Women on [[Wikipedia]] Edit-a-thon hosted at RIT on Saturday, March 24th from 11am-4pm<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Nicosia, Lara. (2017). "[[Infoguides: Women on Wikipedia Edit-A-Thon: What to Do Next..]]".<br />
</nowiki><br />
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=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Nicosia |first1=Lara |title=Infoguides: Women on Wikipedia Edit-A-Thon: What to Do Next.. |date=2017 |url=https://wikipediaquality.com/wiki/Infoguides:_Women_on_Wikipedia_Edit-A-Thon:_What_to_Do_Next..}}<br />
</nowiki><br />
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=== HTML ===<br />
<code><br />
<nowiki><br />
Nicosia, Lara. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Infoguides:_Women_on_Wikipedia_Edit-A-Thon:_What_to_Do_Next..">Infoguides: Women on Wikipedia Edit-A-Thon: What to Do Next..</a>&amp;quot;.<br />
</nowiki><br />
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Abigail
https://wikipediaquality.com/index.php?title=Extracting_Knowledge_from_Web_Search_Engine_Using_Wikipedia&diff=24188
Extracting Knowledge from Web Search Engine Using Wikipedia
2020-05-16T03:31:54Z
<p>Abigail: + cat.</p>
<hr />
<div>{{Infobox work<br />
| title = Extracting Knowledge from Web Search Engine Using Wikipedia<br />
| date = 2013<br />
| authors = [[Andreas Kanavos]]<br />[[Christos Makris]]<br />[[Yannis Plegas]]<br />[[Evangelos Theodoridis]]<br />
| doi = 10.1007/978-3-642-41016-1_11<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-642-41016-1_11.pdf<br />
}}<br />
'''Extracting Knowledge from Web Search Engine Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Andreas Kanavos]], [[Christos Makris]], [[Yannis Plegas]] and [[Evangelos Theodoridis]].<br />
<br />
== Overview ==<br />
Nowadays, search engines are definitely a dominating web tool for finding information on the web. However, web search engines usually return web page references in a global ranking making it difficult to the users to browse different topics captured in the result set. Recently, there are meta-search engine systems that discover knowledge in these web search results providing the user with the possibility to browse different topics contained in the result set. In this paper, authors focus on the problem of determining different thematic groups on web search engine results that existing web search engines provide. Authors propose a novel system that exploits semantic entities of [[Wikipedia]] for grouping the result set in different topic groups, according to the various meanings of the provided query. The proposed method utilizes a number of semantic annotation techniques using Knowledge Bases, like [[WordNet]] and Wikipedia, in order to perceive the different senses of each query term. Finally, the method annotates the extracted topics using information derived from clusters which in following are presented to the end user.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Kanavos, Andreas; Makris, Christos; Plegas, Yannis; Theodoridis, Evangelos. (2013). "[[Extracting Knowledge from Web Search Engine Using Wikipedia]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-41016-1_11. <br />
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=== English Wikipedia ===<br />
<code><br />
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{{cite journal |last1=Kanavos |first1=Andreas |last2=Makris |first2=Christos |last3=Plegas |first3=Yannis |last4=Theodoridis |first4=Evangelos |title=Extracting Knowledge from Web Search Engine Using Wikipedia |date=2013 |doi=10.1007/978-3-642-41016-1_11 |url=https://wikipediaquality.com/wiki/Extracting_Knowledge_from_Web_Search_Engine_Using_Wikipedia |journal=Springer, Berlin, Heidelberg}}<br />
</nowiki><br />
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=== HTML ===<br />
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Kanavos, Andreas; Makris, Christos; Plegas, Yannis; Theodoridis, Evangelos. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extracting_Knowledge_from_Web_Search_Engine_Using_Wikipedia">Extracting Knowledge from Web Search Engine Using Wikipedia</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-41016-1_11. <br />
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[[Category:Scientific works]]</div>
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https://wikipediaquality.com/index.php?title=Derivation_of_is_a_Taxonomy_from_Wikipedia_Category_Graph&diff=24187
Derivation of is a Taxonomy from Wikipedia Category Graph
2020-05-16T03:30:36Z
<p>Abigail: Infobox</p>
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<div>{{Infobox work<br />
| title = Derivation of is a Taxonomy from Wikipedia Category Graph<br />
| date = 2016<br />
| authors = [[Mohamed Ben Aouicha]]<br />[[Mohamed Ali Hadj Taieb]]<br />[[Malek Ezzeddine]]<br />
| doi = 10.1016/j.engappai.2016.01.033<br />
| link = https://dl.acm.org/citation.cfm?id=2902092<br />
}}<br />
'''Derivation of is a Taxonomy from Wikipedia Category Graph''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Mohamed Ben Aouicha]], [[Mohamed Ali Hadj Taieb]] and [[Malek Ezzeddine]].<br />
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== Overview ==<br />
Knowledge acquisition still represents one of the main challenging obstacles to designing intelligent systems exhibiting human-level performance in complex intelligent tasks. The recent developments in crowdsourcing technologies have opened new promising opportunities to overcome this problem by exploiting large amounts of machine readable knowledge to perform tasks requiring human intelligence. [[Wikipedia]] is a case of this research trend, being the largest collaborative and [[multilingual]] resource and linguistic knowledge that contains unstructured and semi-[[structured information]]. In this paper, authors propose an approach for deriving "is a" taxonomy from the Wikipedia Categories Graph (WCG), which is an open collaborative resource. After building and filtering the WCG from a Wikipedia dump, the process would mainly consist in the exploitation of the "BY" tag and the sharing of plural headers. These methods provide a graph formed by a set of non-connected sub-graphs. Therefore, authors propose a process for linking them to finally obtain an "is a" taxonomy with only one root and modeled as a direct acyclic graph (DAG). In this work, specific DAG handling algorithms are used, including an algorithm for a DAG into sub-DAGs and another for merging two DAGs. The obtained taxonomy is assessed using [[semantic similarity]] [[measures]], which consist in quantifying the likeness between two concepts or words. Therefore, authors exploit a set of well-known benchmarks to compare the results obtained via the generated taxonomy to those achieved with [[WordNet]], a resource created and maintained by domain experts. The experimental results revealed good correlations between computed values and human judgments. Compared to WordNet, the derived taxonomy was also noted to lead to an enhanced coverage capacity.</div>
Abigail
https://wikipediaquality.com/index.php?title=Casual_Compulsions_and_Compulsive_Casualities:_a_Characterization_of_User-Contributions_to_Wikipedia&diff=23838
Casual Compulsions and Compulsive Casualities: a Characterization of User-Contributions to Wikipedia
2020-04-08T07:06:19Z
<p>Abigail: wikilinks</p>
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<div>'''Casual Compulsions and Compulsive Casualities: a Characterization of User-Contributions to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Matthijs Den-Besten]].<br />
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== Overview ==<br />
Participation in online communities is obviously key, but the concrete interplay of the heterogeneous motivations of participants in those peer production communities has yet to be inquired in appropriate depth. Authors know about the existence of core and peripheral members, but how the heterogeneity of motivations and online roles results in a division of online labor, and furthermore in the peer production of higher or lesser quality products, is still largely an open question. Focusing here on a part of the inner core of the [[Wikipedia community]] - registered users who declare themselves as 'Wikipediholics' by displaying an userbox accordingly on their user pages, and who self-measure their level of Wikipediholism - authors show that these users are not only more active but that the pages to which they contribute also seem associated with higher levels of [[readability]], in a sense although thse pages are more active and receive a larger number of contributions including more contributions from anonymous users. Furthermore, the self-declared level of Wikipediholism seems to be positively correlated to increased levels of readability, as if the more Wikipediholic users had self-committed themselves, and their [[reputation]], to produce higher quality pages.</div>
Abigail
https://wikipediaquality.com/index.php?title=The_Category_Structure_in_Wikipedia:_to_Analyze_and_Know_How_It_Grows&diff=23837
The Category Structure in Wikipedia: to Analyze and Know How It Grows
2020-04-08T07:04:32Z
<p>Abigail: Adding categories</p>
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<div>{{Infobox work<br />
| title = The Category Structure in Wikipedia: to Analyze and Know How It Grows<br />
| date = 2013<br />
| authors = [[Qishun Wang]]<br />[[Xiaohua Wang]]<br />[[Zhiqun Chen]]<br />[[Rongbo Wang]]<br />
| doi = 10.1007/978-3-642-45185-0_56<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-642-45185-0_56.pdf<br />
}}<br />
'''The Category Structure in Wikipedia: to Analyze and Know How It Grows''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Qishun Wang]], [[Xiaohua Wang]], [[Zhiqun Chen]] and [[Rongbo Wang]].<br />
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== Overview ==<br />
Wikipedia is a famous encyclopedia and is applied to a lot of famous fields for many years, such as [[natural language processing]]. The [[category structure]] is used and analyzed in this paper. Authors take the important topological properties into account, such as the connectivity distribution. What’s the most important of all is to analyze the growth of the structure from 2004 to 2012 in detail. In order to tell about the growth, the basic properties and the small-worldness is brought in. Some different edge attachment models based on the properties of nodes are tested in order to study how the properties of nodes influence the creation of edges. Authors are very interested in the phenomenon that the data in 2011 and 2012 is so strange and study the reason closely. Authors results offer useful insights for the structure and the growth of the category structure.<br />
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Wang, Qishun; Wang, Xiaohua; Chen, Zhiqun; Wang, Rongbo. (2013). "[[The Category Structure in Wikipedia: to Analyze and Know How It Grows]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-45185-0_56. <br />
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{{cite journal |last1=Wang |first1=Qishun |last2=Wang |first2=Xiaohua |last3=Chen |first3=Zhiqun |last4=Wang |first4=Rongbo |title=The Category Structure in Wikipedia: to Analyze and Know How It Grows |date=2013 |doi=10.1007/978-3-642-45185-0_56 |url=https://wikipediaquality.com/wiki/The_Category_Structure_in_Wikipedia:_to_Analyze_and_Know_How_It_Grows |journal=Springer, Berlin, Heidelberg}}<br />
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Wang, Qishun; Wang, Xiaohua; Chen, Zhiqun; Wang, Rongbo. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/The_Category_Structure_in_Wikipedia:_to_Analyze_and_Know_How_It_Grows">The Category Structure in Wikipedia: to Analyze and Know How It Grows</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-45185-0_56. <br />
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Abigail
https://wikipediaquality.com/index.php?title=Structures_of_Knowledge_from_Wikipedia_Networks&diff=23836
Structures of Knowledge from Wikipedia Networks
2020-04-08T07:02:10Z
<p>Abigail: + embed code</p>
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<div>{{Infobox work<br />
| title = Structures of Knowledge from Wikipedia Networks<br />
| date = 2017<br />
| authors = [[Maxime Gabella]]<br />
| link = http://www.sciencedirect.com/science/article/pii/S0198971517303216<br />
| plink = https://arxiv.org/abs/1708.05368<br />
}}<br />
'''Structures of Knowledge from Wikipedia Networks''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Maxime Gabella]].<br />
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== Overview ==<br />
Knowledge is useless without structure. While the classification of knowledge has been an enduring philosophical enterprise, it recently found applications in computer science, notably for [[artificial intelligence]]. The availability of large databases allowed for complex ontologies to be built automatically, for example by extracting structured content from [[Wikipedia]]. However, this approach is subject to manual categorization decisions made by online editors. Here authors show that an implicit classification system emerges spontaneously on Wikipedia. Authors study the network of first links between articles, and find that it centers on a core cycle involving concepts of fundamental classifying importance. Authors argue that this structure is rooted in cultural history. For European languages, articles like Philosophy and Science are central, whereas Human and Earth dominate for East Asian languages. This reflects the differences between ancient Greek thought and Chinese tradition. Authors results reveal the powerful influence of culture on the intrinsic architecture of complex data sets.<br />
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Gabella, Maxime. (2017). "[[Structures of Knowledge from Wikipedia Networks]]".<br />
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{{cite journal |last1=Gabella |first1=Maxime |title=Structures of Knowledge from Wikipedia Networks |date=2017 |url=https://wikipediaquality.com/wiki/Structures_of_Knowledge_from_Wikipedia_Networks}}<br />
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Gabella, Maxime. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Structures_of_Knowledge_from_Wikipedia_Networks">Structures of Knowledge from Wikipedia Networks</a>&amp;quot;.<br />
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Abigail
https://wikipediaquality.com/index.php?title=Exploring_Linguistic_Points_of_View_of_Wikipedia&diff=23835
Exploring Linguistic Points of View of Wikipedia
2020-04-08T06:59:12Z
<p>Abigail: Category</p>
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<div>{{Infobox work<br />
| title = Exploring Linguistic Points of View of Wikipedia<br />
| date = 2011<br />
| authors = [[Paolo Massa]]<br />[[Federico Scrinzi]]<br />
| doi = 10.1145/2038558.2038599<br />
| link = https://dl.acm.org/citation.cfm?id=2038599<br />
}}<br />
'''Exploring Linguistic Points of View of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Paolo Massa]] and [[Federico Scrinzi]].<br />
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== Overview ==<br />
The 3 million articles of the [[English Wikipedia]] has been written since 2011 by more than 14 million volunteers. On each article, the community of editors strive to reach a [[neutral point of view]], representing all significant views fairly, proportionately, and without bias. However, beside the English one, there are more than 270 [[Wikipedia]]s in [[different language]]s and their relatively isolated communities of editors are not forced by the platform to discuss and negotiate their points of view. So the empirical question is: do communities on different languages editions of Wikipedia develop their own diverse Linguistic Points of View (LPOV)? To answer this question authors created Manypedia, a web tool whose goal is to ease cross-cultural comparisons of Wikipedia language communities by analyzing their different representations of the same topic.<br />
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Massa, Paolo; Scrinzi, Federico. (2011). "[[Exploring Linguistic Points of View of Wikipedia]]".DOI: 10.1145/2038558.2038599. <br />
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{{cite journal |last1=Massa |first1=Paolo |last2=Scrinzi |first2=Federico |title=Exploring Linguistic Points of View of Wikipedia |date=2011 |doi=10.1145/2038558.2038599 |url=https://wikipediaquality.com/wiki/Exploring_Linguistic_Points_of_View_of_Wikipedia}}<br />
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Massa, Paolo; Scrinzi, Federico. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Exploring_Linguistic_Points_of_View_of_Wikipedia">Exploring Linguistic Points of View of Wikipedia</a>&amp;quot;.DOI: 10.1145/2038558.2038599. <br />
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Abigail
https://wikipediaquality.com/index.php?title=A_Survival_Modeling_Approach_to_Biomedical_Search_Result_Diversification_Using_Wikipedia&diff=23834
A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia
2020-04-08T06:57:30Z
<p>Abigail: Adding categories</p>
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<div>{{Infobox work<br />
| title = A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia<br />
| date = 2013<br />
| authors = [[Xiaoshi Yin]]<br />[[Jimmy Xiangji Huang]]<br />[[Zhoujun Li]]<br />[[Xiaofeng Zhou]]<br />
| doi = 10.1109/TKDE.2012.24<br />
| link = https://dl.acm.org/citation.cfm?id=2498755<br />
}}<br />
'''A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Xiaoshi Yin]], [[Jimmy Xiangji Huang]], [[Zhoujun Li]] and [[Xiaofeng Zhou]].<br />
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== Overview ==<br />
In this paper, authors propose a survival modeling approach to promoting ranking diversity for biomedical [[information retrieval]]. The proposed approach concerns with finding relevant documents that can deliver more different aspects of a query. First, two probabilistic models derived from the survival analysis theory are proposed for measuring aspect novelty. Second, a new method using [[Wikipedia]] to detect aspects covered by retrieved documents is presented. Third, an aspect filter based on a two-stage model is introduced. It ranks the detected aspects in decreasing order of the probability that an aspect is generated by the query. Finally, the relevance and the novelty of retrieved documents are combined at the aspect level for reranking. Experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed approach in promoting ranking diversity for biomedical information retrieval. Moreover, authors further evaluate approach in the Web retrieval environment. The evaluation results on the ClueWeb09-T09B collection show that approach can achieve promising performance improvements.<br />
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Yin, Xiaoshi; Huang, Jimmy Xiangji; Li, Zhoujun; Zhou, Xiaofeng. (2013). "[[A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia]]".DOI: 10.1109/TKDE.2012.24. <br />
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{{cite journal |last1=Yin |first1=Xiaoshi |last2=Huang |first2=Jimmy Xiangji |last3=Li |first3=Zhoujun |last4=Zhou |first4=Xiaofeng |title=A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia |date=2013 |doi=10.1109/TKDE.2012.24 |url=https://wikipediaquality.com/wiki/A_Survival_Modeling_Approach_to_Biomedical_Search_Result_Diversification_Using_Wikipedia}}<br />
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Yin, Xiaoshi; Huang, Jimmy Xiangji; Li, Zhoujun; Zhou, Xiaofeng. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Survival_Modeling_Approach_to_Biomedical_Search_Result_Diversification_Using_Wikipedia">A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia</a>&amp;quot;.DOI: 10.1109/TKDE.2012.24. <br />
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[[Category:Scientific works]]</div>
Abigail