https://wikipediaquality.com/api.php?action=feedcontributions&user=Oklahoma&feedformat=atomWikipedia Quality - User contributions [en]2024-03-29T04:52:50ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=You_are_Where_You_Edit:_Locating_Wikipedia_Contributors_Through_Edit_Histories&diff=25985You are Where You Edit: Locating Wikipedia Contributors Through Edit Histories2020-11-16T18:56:23Z<p>Oklahoma: + infobox</p>
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<div>{{Infobox work<br />
| title = You are Where You Edit: Locating Wikipedia Contributors Through Edit Histories<br />
| date = 2009<br />
| authors = [[Michael D. Lieberman]]<br />[[Jimmy J. Lin]]<br />
| link = http://www.umiacs.umd.edu/~jimmylin/publications/Lieberman_Lin_ICWSM2009.pdf<br />
}}<br />
'''You are Where You Edit: Locating Wikipedia Contributors Through Edit Histories''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Michael D. Lieberman]] and [[Jimmy J. Lin]].<br />
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== Overview ==<br />
Whether knowingly or otherwise, [[Wikipedia]] contributors reveal their interests and expertise through their contribution patterns. An analysis of Wikipedia edit histories shows that it is often possible to associate contributors with relatively small geographic regions, usually corresponding to where they were born or where they presently live. For many contributors, the geographic coordinates of pages they have edited are tightly clustered. Results suggest that a wealth of information about contributors can be gleaned from edit histories. This illustrates the efficacy of data mining on large, publicly-available datasets and raises potential privacy concerns.</div>Oklahomahttps://wikipediaquality.com/index.php?title=Interactions_of_Cultures_and_Top_People_of_Wikipedia_from_Ranking_of_24_Language&diff=25984Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language2020-11-16T18:54:01Z<p>Oklahoma: Links</p>
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<div>'''Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Young-Ho Eom]], [[Pablo Aragón]], [[David Laniado]], [[Andreas Kaltenbrunner]], [[Sebastiano Vigna]], [[Dima L. Shepelyansky]], [[Kaltenbrunner A]], [[Vigna S]] and [[Dima L. Shepelyansky]].<br />
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== Overview ==<br />
Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, authors apply methods of Markov chains and [[Google]] matrix for the analysis of the hyperlink networks of 24 [[Wikipedia]] language editions, and rank all their articles by PageRank, 2DRank and CheiRank algorithms. Using automatic extraction of people names, authors obtain the top 100 historical figures, for each edition and for each algorithm. Authors investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Authors study demonstrates not only the existence of skewness with local figures, mainly recognized only in their own cultures, but also the existence of global historical figures appearing in a large number of editions. By determining the birth time and place of these persons, authors perform an analysis of the evolution of such figures through 35 centuries of human history for each language, thus recovering interactions and entanglement of cultures over time. Authors also obtain the distributions of historical figures over world countries, highlighting geographical aspects of cross-cultural links. Considering historical figures who appear in multiple editions as interactions between cultures, authors construct a network of cultures and identify the most influential cultures according to this network.</div>Oklahomahttps://wikipediaquality.com/index.php?title=World_Influence_of_Infectious_Diseases_from_Wikipedia_Network_Analysis&diff=25983World Influence of Infectious Diseases from Wikipedia Network Analysis2020-11-16T18:50:26Z<p>Oklahoma: Categories</p>
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<div>{{Infobox work<br />
| title = World Influence of Infectious Diseases from Wikipedia Network Analysis<br />
| date = 2018<br />
| authors = [[Guillaume Rollin]]<br />[[J. Lages]]<br />[[Dima L. Shepelyansky]]<br />
| doi = 10.1101/424465<br />
| link = https://www.biorxiv.org/content/early/2018/09/24/424465<br />
}}<br />
'''World Influence of Infectious Diseases from Wikipedia Network Analysis''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Guillaume Rollin]], [[J. Lages]] and [[Dima L. Shepelyansky]].<br />
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== Overview ==<br />
Authors consider the network of 5 416 537 articles of [[English Wikipedia]] extracted in 2017. Using the recent reduced [[Google]] matrix (REGOMAX) method authors construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix authors determine the sensitivity of world countries to specific diseases integrating their influence over all their history including the times of ancient Egyptian mummies. The obtained results are compared with the World Health Organization (WHO) data demonstrating that the [[Wikipedia]] network analysis provides reliable results with up to about 80 percent overlap between WHO and REGOMAX analyses.<br />
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Rollin, Guillaume; Lages, J.; Shepelyansky, Dima L.. (2018). "[[World Influence of Infectious Diseases from Wikipedia Network Analysis]]". Cold Spring Harbor Laboratory. DOI: 10.1101/424465. <br />
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{{cite journal |last1=Rollin |first1=Guillaume |last2=Lages |first2=J. |last3=Shepelyansky |first3=Dima L. |title=World Influence of Infectious Diseases from Wikipedia Network Analysis |date=2018 |doi=10.1101/424465 |url=https://wikipediaquality.com/wiki/World_Influence_of_Infectious_Diseases_from_Wikipedia_Network_Analysis |journal=Cold Spring Harbor Laboratory}}<br />
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Rollin, Guillaume; Lages, J.; Shepelyansky, Dima L.. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/World_Influence_of_Infectious_Diseases_from_Wikipedia_Network_Analysis">World Influence of Infectious Diseases from Wikipedia Network Analysis</a>&amp;quot;. Cold Spring Harbor Laboratory. DOI: 10.1101/424465. <br />
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[[Category:Scientific works]]<br />
[[Category:English Wikipedia]]</div>Oklahomahttps://wikipediaquality.com/index.php?title=Identifying_Document_Topics_Using_the_Wikipedia_Category_Network&diff=25982Identifying Document Topics Using the Wikipedia Category Network2020-11-16T18:47:46Z<p>Oklahoma: Adding categories</p>
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<div>{{Infobox work<br />
| title = Identifying Document Topics Using the Wikipedia Category Network<br />
| date = 2009<br />
| authors = [[Péter Schönhofen]]<br />
| doi = 10.3233/WIA-2009-0162<br />
| link = http://dl.acm.org/citation.cfm?id=1551707.1551712<br />
}}<br />
'''Identifying Document Topics Using the Wikipedia Category Network''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Péter Schönhofen]].<br />
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== Overview ==<br />
In the last few years the size and coverage of [[Wikipedia]], a community edited, freely available on-line encyclopedia has reached the point where it can be effectively used to identify topics discussed in a document, similarly to an [[ontology]] or taxonomy. In this paper authors will show that even a fairly simple algorithm that exploits only the titles and [[categories]] of Wikipedia articles can characterize documents by [[Wikipedia categories]] surprisingly well. Authors test the [[reliability]] of method by predicting categories of Wikipedia articles themselves based on their bodies, and also by performing classification and clustering on 20 Newsgroups and RCV1, representing documents by their Wikipedia categories instead of (or in addition to) their texts.<br />
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Schönhofen, Péter. (2009). "[[Identifying Document Topics Using the Wikipedia Category Network]]". IOS Press. DOI: 10.3233/WIA-2009-0162. <br />
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{{cite journal |last1=Schönhofen |first1=Péter |title=Identifying Document Topics Using the Wikipedia Category Network |date=2009 |doi=10.3233/WIA-2009-0162 |url=https://wikipediaquality.com/wiki/Identifying_Document_Topics_Using_the_Wikipedia_Category_Network |journal=IOS Press}}<br />
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Schönhofen, Péter. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Identifying_Document_Topics_Using_the_Wikipedia_Category_Network">Identifying Document Topics Using the Wikipedia Category Network</a>&amp;quot;. IOS Press. DOI: 10.3233/WIA-2009-0162. <br />
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[[Category:Scientific works]]</div>Oklahomahttps://wikipediaquality.com/index.php?title=Automatic_Detection_of_Outdated_Information_in_Wikipedia_Infoboxes&diff=25981Automatic Detection of Outdated Information in Wikipedia Infoboxes2020-11-16T18:45:07Z<p>Oklahoma: Category</p>
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<div>{{Infobox work<br />
| title = Automatic Detection of Outdated Information in Wikipedia Infoboxes<br />
| date = 2013<br />
| authors = [[Thong Tran]]<br />[[Tru H. Cao]]<br />
| link = http://www.rcs.cic.ipn.mx/2013_70/Automatic%20Detection%20of%20Outdated%20Information%20in%20Wikipedia%20Infoboxes.html<br />
}}<br />
'''Automatic Detection of Outdated Information in Wikipedia Infoboxes''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Thong Tran]] and [[Tru H. Cao]].<br />
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== Overview ==<br />
An infobox of a [[Wikipedia]] article generally contains key facts in the article and is organized as attribute-value pairs. Infoboxes not only allow read- ers to rapidly gather the most important information about some aspects of the articles in which they appear, but also provide a source for many knowledge ba- ses derived from Wikipedia. However, not all the values of infobox attributes are updated frequently and accurately. In this paper, authors propose a method to au- tomatically detect outdated attribute values in Wikipedia [[infoboxes]] by using facts extracted from the general Web. Authors method uses the pattern-based fact extraction approach. The patterns for fact extraction are automatically learned using a number of available seeds in related Wikipedia infoboxes. Authors have tested and evaluated system on a set of 100 well-established com- panies in the NASDAQ-100 index on their employee numbers, presented by the num_employees attribute value in their Wikipedia article infoboxes. The achieved accuracy is 77% and test result also reveals that 82% of the companies do not have their latest numbers of employees in their Wikipedia article infoboxes.<br />
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Tran, Thong; Cao, Tru H.. (2013). "[[Automatic Detection of Outdated Information in Wikipedia Infoboxes]]".<br />
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{{cite journal |last1=Tran |first1=Thong |last2=Cao |first2=Tru H. |title=Automatic Detection of Outdated Information in Wikipedia Infoboxes |date=2013 |url=https://wikipediaquality.com/wiki/Automatic_Detection_of_Outdated_Information_in_Wikipedia_Infoboxes}}<br />
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Tran, Thong; Cao, Tru H.. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Automatic_Detection_of_Outdated_Information_in_Wikipedia_Infoboxes">Automatic Detection of Outdated Information in Wikipedia Infoboxes</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Oklahomahttps://wikipediaquality.com/index.php?title=Cross-Lingual_Infobox_Alignment_in_Wikipedia_Using_Entity-Attribute_Factor_Graph&diff=25980Cross-Lingual Infobox Alignment in Wikipedia Using Entity-Attribute Factor Graph2020-11-16T18:42:05Z<p>Oklahoma: + cat.</p>
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<div>{{Infobox work<br />
| title = Cross-Lingual Infobox Alignment in Wikipedia Using Entity-Attribute Factor Graph<br />
| date = 2017<br />
| authors = [[Yan Zhang]]<br />[[Thomas Paradis]]<br />[[Lei Hou]]<br />[[Juanzi Li]]<br />[[Jing Zhang]]<br />[[Hai-Tao Zheng]]<br />
| doi = 10.1007/978-3-319-68288-4_44<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-68288-4_44.pdf<br />
}}<br />
'''Cross-Lingual Infobox Alignment in Wikipedia Using Entity-Attribute Factor Graph''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Yan Zhang]], [[Thomas Paradis]], [[Lei Hou]], [[Juanzi Li]], [[Jing Zhang]] and [[Hai-Tao Zheng]].<br />
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== Overview ==<br />
Wikipedia [[infoboxes]] contain information about article entities in the form of attribute-value pairs, and are thus a very rich source of structured knowledge. However, as the different [[language versions]] of [[Wikipedia]] evolve independently, it is a promising but challenging problem to find correspondences between infobox attributes in [[different language]] editions. In this paper, authors propose 8 effective [[features]] for [[cross lingual]] infobox attribute matching containing [[categories]], templates, attribute labels and values. Authors propose entity-attribute factor graph to consider not only individual features but also the correlations among attribute pairs. Experiments on the two Wikipedia data sets of English-Chinese and English-French show that proposed approach can achieve high F1-measure: 85.5% and 85.4% respectively on the two data sets. Authors proposed approach finds 23,923 new infobox attribute mappings between English and [[Chinese Wikipedia]], and 31,576 between English and French based on no more than six thousand existing matched infobox attributes. Authors conduct an infobox completion experiment on English-Chinese Wikipedia and complement 76,498 (more than 30% of EN-ZH Wikipedia existing [[cross-lingual]] links) pairs of corresponding articles with more than one attribute-value pairs.<br />
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Zhang, Yan; Paradis, Thomas; Hou, Lei; Li, Juanzi; Zhang, Jing; Zheng, Hai-Tao. (2017). "[[Cross-Lingual Infobox Alignment in Wikipedia Using Entity-Attribute Factor Graph]]". Springer, Cham. DOI: 10.1007/978-3-319-68288-4_44. <br />
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{{cite journal |last1=Zhang |first1=Yan |last2=Paradis |first2=Thomas |last3=Hou |first3=Lei |last4=Li |first4=Juanzi |last5=Zhang |first5=Jing |last6=Zheng |first6=Hai-Tao |title=Cross-Lingual Infobox Alignment in Wikipedia Using Entity-Attribute Factor Graph |date=2017 |doi=10.1007/978-3-319-68288-4_44 |url=https://wikipediaquality.com/wiki/Cross-Lingual_Infobox_Alignment_in_Wikipedia_Using_Entity-Attribute_Factor_Graph |journal=Springer, Cham}}<br />
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Zhang, Yan; Paradis, Thomas; Hou, Lei; Li, Juanzi; Zhang, Jing; Zheng, Hai-Tao. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Cross-Lingual_Infobox_Alignment_in_Wikipedia_Using_Entity-Attribute_Factor_Graph">Cross-Lingual Infobox Alignment in Wikipedia Using Entity-Attribute Factor Graph</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-68288-4_44. <br />
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[[Category:Chinese Wikipedia]]</div>Oklahomahttps://wikipediaquality.com/index.php?title=Wikionto:_a_System_for_Semi-Automatic_Extraction_and_Modeling_of_Ontologies_Using_Wikipedia_Xml_Corpus&diff=25979Wikionto: a System for Semi-Automatic Extraction and Modeling of Ontologies Using Wikipedia Xml Corpus2020-11-16T18:40:43Z<p>Oklahoma: Adding categories</p>
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<div>{{Infobox work<br />
| title = Wikionto: a System for Semi-Automatic Extraction and Modeling of Ontologies Using Wikipedia Xml Corpus<br />
| date = 2009<br />
| authors = [[Lalindra De Silva]]<br />[[Lakshman Jayaratne]]<br />
| doi = 10.1109/ICSC.2009.93<br />
| link = http://dl.acm.org/citation.cfm?id=1679947<br />
}}<br />
'''Wikionto: a System for Semi-Automatic Extraction and Modeling of Ontologies Using Wikipedia Xml Corpus''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Lalindra De Silva]] and [[Lakshman Jayaratne]].<br />
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== Overview ==<br />
This paper introduces WikiOnto: a system that assists in the extraction and modeling of topic ontologies in a semi-automatic manner using a preprocessed document corpus of one of the largest knowledge bases in the world - the [[Wikipedia]]. Based on the Wikipedia XML Corpus, authors present a three-tiered framework for extracting topic ontologies in quick time and a modeling environment to refine these ontologies. Using [[Natural Language Processing]] (NLP) and other Machine Learning (ML) techniques along with a very rich document corpus, this system proposes a solution to a task that is generally considered extremely cumbersome. The initial results of the prototype suggest strong potential of the system to become highly successful in [[ontology]] extraction and modeling and also inspire further research on extracting ontologies from other semi-structured document corpora as well.<br />
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Silva, Lalindra De; Jayaratne, Lakshman. (2009). "[[Wikionto: a System for Semi-Automatic Extraction and Modeling of Ontologies Using Wikipedia Xml Corpus]]".DOI: 10.1109/ICSC.2009.93. <br />
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{{cite journal |last1=Silva |first1=Lalindra De |last2=Jayaratne |first2=Lakshman |title=Wikionto: a System for Semi-Automatic Extraction and Modeling of Ontologies Using Wikipedia Xml Corpus |date=2009 |doi=10.1109/ICSC.2009.93 |url=https://wikipediaquality.com/wiki/Wikionto:_a_System_for_Semi-Automatic_Extraction_and_Modeling_of_Ontologies_Using_Wikipedia_Xml_Corpus}}<br />
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Silva, Lalindra De; Jayaratne, Lakshman. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikionto:_a_System_for_Semi-Automatic_Extraction_and_Modeling_of_Ontologies_Using_Wikipedia_Xml_Corpus">Wikionto: a System for Semi-Automatic Extraction and Modeling of Ontologies Using Wikipedia Xml Corpus</a>&amp;quot;.DOI: 10.1109/ICSC.2009.93. <br />
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[[Category:Scientific works]]</div>Oklahomahttps://wikipediaquality.com/index.php?title=Bots_vs._Wikipedians,_Anons_vs._Logged-Ins&diff=25978Bots vs. Wikipedians, Anons vs. Logged-Ins2020-11-16T18:38:22Z<p>Oklahoma: + Embed</p>
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<div>{{Infobox work<br />
| title = Bots vs. Wikipedians, Anons vs. Logged-Ins<br />
| date = 2014<br />
| authors = [[Thomas Steiner]]<br />
| doi = 10.1145/2567948.2576948<br />
| link = https://dl.acm.org/citation.cfm?id=2576948<br />
| plink = https://arxiv.org/abs/1402.0412<br />
}}<br />
'''Bots vs. Wikipedians, Anons vs. Logged-Ins''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Thomas Steiner]].<br />
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== Overview ==<br />
Wikipedia is a global crowdsourced encyclopedia that at time of writing is available in 287 languages. [[Wikidata]] is a likewise global crowdsourced knowledge base that provides shared facts to be used by [[Wikipedia]]s. In the context of this research, authors have developed an application and an underlying Application Programming Interface (API) capable of monitoring realtime edit activity of all [[language versions]] of Wikipedia and Wikidata. This application allows us to easily analyze edits in order to answer questions such as "Bots vs. [[Wikipedians]], who edits more?", "Which is the most anonymously edited Wikipedia?", or "Who are the bots and what do they edit?". To the best of knowledge, this is the first time such an analysis could be done in realtime for Wikidata and for really all Wikipedias-large and small. Authors application is available publicly online at the URL http://wikipedia-edits.herokuapp.com/, its code has been [[open-source]]d under the Apache 2.0 license.<br />
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Steiner, Thomas. (2014). "[[Bots vs. Wikipedians, Anons vs. Logged-Ins]]".DOI: 10.1145/2567948.2576948. <br />
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{{cite journal |last1=Steiner |first1=Thomas |title=Bots vs. Wikipedians, Anons vs. Logged-Ins |date=2014 |doi=10.1145/2567948.2576948 |url=https://wikipediaquality.com/wiki/Bots_vs._Wikipedians,_Anons_vs._Logged-Ins}}<br />
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Steiner, Thomas. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Bots_vs._Wikipedians,_Anons_vs._Logged-Ins">Bots vs. Wikipedians, Anons vs. Logged-Ins</a>&amp;quot;.DOI: 10.1145/2567948.2576948. <br />
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