https://wikipediaquality.com/api.php?action=feedcontributions&user=Cheri&feedformat=atomWikipedia Quality - User contributions [en]2024-03-28T17:35:43ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Language-Independent_Context_Aware_Query_Translation_Using_Wikipedia&diff=27967Language-Independent Context Aware Query Translation Using Wikipedia2021-02-27T07:59:22Z<p>Cheri: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Language-Independent Context Aware Query Translation Using Wikipedia<br />
| date = 2011<br />
| authors = [[Rohit Bharadwaj G]]<br />[[Vasudeva Varma]]<br />
| link = https://dl.acm.org/citation.cfm?id=2024260<br />
}}<br />
'''Language-Independent Context Aware Query Translation Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Rohit Bharadwaj G]] and [[Vasudeva Varma]].<br />
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== Overview ==<br />
Cross lingual information access (CLIA) systems are required to access the large amounts of [[multilingual]] content generated on the world wide web in the form of blogs, news articles and documents. In this paper, authors discuss approach to query formation for CLIA systems where language resources are replaced by [[Wikipedia]]. Authors claim that Wikipedia, with its rich multilingual content and structure, forms an ideal platform to build a CLIA system. Authors approach is particularly useful for under-resourced languages, as all the languages don't have the resources(tools) with sufficient accuracies. Authors propose a context aware language-independent query formation method which, with the help of bilingual dictionaries, forms queries in the target language. Results are encouraging with a precision of 69.75% and thus endorse claim on using Wikipedia for building CLIA systems.<br />
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G, Rohit Bharadwaj; Varma, Vasudeva. (2011). "[[Language-Independent Context Aware Query Translation Using Wikipedia]]". Association for Computational Linguistics. <br />
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{{cite journal |last1=G |first1=Rohit Bharadwaj |last2=Varma |first2=Vasudeva |title=Language-Independent Context Aware Query Translation Using Wikipedia |date=2011 |url=https://wikipediaquality.com/wiki/Language-Independent_Context_Aware_Query_Translation_Using_Wikipedia |journal=Association for Computational Linguistics}}<br />
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G, Rohit Bharadwaj; Varma, Vasudeva. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Language-Independent_Context_Aware_Query_Translation_Using_Wikipedia">Language-Independent Context Aware Query Translation Using Wikipedia</a>&amp;quot;. Association for Computational Linguistics. <br />
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</code></div>Cherihttps://wikipediaquality.com/index.php?title=A_Comparison_of_Approaches_for_Geospatial_Entity_Extraction_from_Wikipedia&diff=27966A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia2021-02-27T07:57:34Z<p>Cheri: Adding embed</p>
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<div>{{Infobox work<br />
| title = A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia<br />
| date = 2010<br />
| authors = [[Daryl Woodward]]<br />[[Jeremy Witmer]]<br />[[Jugal K. Kalita]]<br />
| doi = 10.1109/ICSC.2010.74<br />
| link = http://ieeexplore.ieee.org/document/5629103/<br />
| plink = https://www.researchgate.net/profile/Jugal_Kalita/publication/224193274_A_Comparison_of_Approaches_for_Geospatial_Entity_Extraction_from_Wikipedia/links/54b6a2170cf2e68eb27ebcb6.pdf<br />
}}<br />
'''A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Daryl Woodward]], [[Jeremy Witmer]] and [[Jugal K. Kalita]].<br />
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== Overview ==<br />
Authors target in this paper the challenge of extracting geospatial data from the article text of the [[English Wikipedia]]. Authors present the results of a Hidden Markov Model (HMM) based approach to identify location-related [[named entities]] in the corpus of [[Wikipedia]] articles, which are primarily about battles and wars due to their high geospatial content. The HMM NER process drives a geocoding and resolution process, whose goal is to determine the correct coordinates for each place name (often referred to as grounding). Authors compare results to a previously developed data structure and algorithm for disambiguating place names that can have multiple coordinates. Authors demonstrate an overall f-measure of 79.63% identifying and geocoding place names. Finally, authors compare the results of the HMM-driven process to earlier work using a Support Vector Machine.<br />
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Woodward, Daryl; Witmer, Jeremy; Kalita, Jugal K.. (2010). "[[A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia]]".DOI: 10.1109/ICSC.2010.74. <br />
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{{cite journal |last1=Woodward |first1=Daryl |last2=Witmer |first2=Jeremy |last3=Kalita |first3=Jugal K. |title=A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia |date=2010 |doi=10.1109/ICSC.2010.74 |url=https://wikipediaquality.com/wiki/A_Comparison_of_Approaches_for_Geospatial_Entity_Extraction_from_Wikipedia}}<br />
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Woodward, Daryl; Witmer, Jeremy; Kalita, Jugal K.. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Comparison_of_Approaches_for_Geospatial_Entity_Extraction_from_Wikipedia">A Comparison of Approaches for Geospatial Entity Extraction from Wikipedia</a>&amp;quot;.DOI: 10.1109/ICSC.2010.74. <br />
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</code></div>Cherihttps://wikipediaquality.com/index.php?title=Beyond_Term_Clusters:_Assigning_Wikipedia_Concepts_to_Scientific_Documents&diff=27965Beyond Term Clusters: Assigning Wikipedia Concepts to Scientific Documents2021-02-27T07:54:53Z<p>Cheri: Links</p>
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<div>'''Beyond Term Clusters: Assigning Wikipedia Concepts to Scientific Documents''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Ozge Yeloglu]], [[Evangelos E. Milios]] and [[A. Nur Zincir-Heywood]].<br />
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== Overview ==<br />
Authors propose a model for assigning [[Wikipedia]] Concepts as scientific category labels to scientific documents where their terms are first grouped together using the well-known topic modelling method, Latent Dirichlet Allocation (LDA) and then assigned to Wikipedia Concepts by wikification. Authors wikify the terms of the topic model of a document to extract related concepts from Wikipedia. Authors experiment on two different datasets: the abstracts of the documents from the ACM Digital Library and the full papers of the UvT Collection. The ACM dataset includes Computer Science publications whereas UvT includes scientific publications from a range of topics. Domain specific taxonomies are used for evaluation. Results show that approach is able to assign Wikipedia Concepts to the scientific publications in an automated manner, removing any need for human supervision.</div>Cherihttps://wikipediaquality.com/index.php?title=Towards_Tailored_Semantic_Annotation_Systems_from_Wikipedia&diff=27964Towards Tailored Semantic Annotation Systems from Wikipedia2021-02-27T07:51:55Z<p>Cheri: cat.</p>
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<div>{{Infobox work<br />
| title = Towards Tailored Semantic Annotation Systems from Wikipedia<br />
| date = 2011<br />
| authors = [[Shahad Kudama]]<br />[[Rafael Berlanga Llavori]]<br />[[Lisette García-Moya]]<br />[[Victoria Nebot]]<br />[[María José Aramburu Cabo]]<br />
| doi = 10.1109/DEXA.2011.82<br />
| link = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6059863<br />
}}<br />
'''Towards Tailored Semantic Annotation Systems from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Shahad Kudama]], [[Rafael Berlanga Llavori]], [[Lisette García-Moya]], [[Victoria Nebot]] and [[María José Aramburu Cabo]].<br />
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== Overview ==<br />
The annotation of texts in natural language links some terms of the text to an external information source that gives us more detailed information about them. Most of the approaches made in this field get any text and annotate it by trying to find out the context of each term, as there are terms that have different meanings depending on the topic treated. In this article, authors propose a variant of this process that annotates a text knowing in advance its context. The external source of information used is [[Wikipedia]] and authors extract and use a fragment of it that embraces all the terms related to the context known beforehand.<br />
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Kudama, Shahad; Llavori, Rafael Berlanga; García-Moya, Lisette; Nebot, Victoria; Cabo, María José Aramburu. (2011). "[[Towards Tailored Semantic Annotation Systems from Wikipedia]]".DOI: 10.1109/DEXA.2011.82. <br />
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{{cite journal |last1=Kudama |first1=Shahad |last2=Llavori |first2=Rafael Berlanga |last3=García-Moya |first3=Lisette |last4=Nebot |first4=Victoria |last5=Cabo |first5=María José Aramburu |title=Towards Tailored Semantic Annotation Systems from Wikipedia |date=2011 |doi=10.1109/DEXA.2011.82 |url=https://wikipediaquality.com/wiki/Towards_Tailored_Semantic_Annotation_Systems_from_Wikipedia}}<br />
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Kudama, Shahad; Llavori, Rafael Berlanga; García-Moya, Lisette; Nebot, Victoria; Cabo, María José Aramburu. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Towards_Tailored_Semantic_Annotation_Systems_from_Wikipedia">Towards Tailored Semantic Annotation Systems from Wikipedia</a>&amp;quot;.DOI: 10.1109/DEXA.2011.82. <br />
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[[Category:Scientific works]]</div>Cherihttps://wikipediaquality.com/index.php?title=Exploring_the_Use_of_Word_Embeddings_and_Random_Walks_on_Wikipedia_for_the_Cogalex_Shared_Task&diff=27963Exploring the Use of Word Embeddings and Random Walks on Wikipedia for the Cogalex Shared Task2021-02-27T07:50:40Z<p>Cheri: + categories</p>
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<div>{{Infobox work<br />
| title = Exploring the Use of Word Embeddings and Random Walks on Wikipedia for the Cogalex Shared Task<br />
| date = 2014<br />
| authors = [[Josu Goikoetxea]]<br />[[Eneko Agirre]]<br />[[Aitor Soroa]]<br />
| doi = 10.3115/v1/W14-4704<br />
| link = http://aclweb.org/anthology/W14-4704<br />
}}<br />
'''Exploring the Use of Word Embeddings and Random Walks on Wikipedia for the Cogalex Shared Task''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Josu Goikoetxea]], [[Eneko Agirre]] and [[Aitor Soroa]].<br />
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== Overview ==<br />
In participation on the task authors wanted to test three different kinds of [[relatedness]] algorithms: one based on embeddings induced from corpora, another based on random walks on [[WordNet]] and a last one based on random walks based on [[Wikipedia]]. All three of them perform similarly in noun relatedness datasets like WordSim353, close to the highest reported values. Although the task definition gave examples of nouns, the train and test data were based on the Edinburgh Association Thesaurus, and around 50% of the target words were not nouns. The corpus-based algorithm performed much better than the other methods in the training dataset, and was thus submitted for the test.<br />
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Goikoetxea, Josu; Agirre, Eneko; Soroa, Aitor. (2014). "[[Exploring the Use of Word Embeddings and Random Walks on Wikipedia for the Cogalex Shared Task]]".DOI: 10.3115/v1/W14-4704. <br />
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{{cite journal |last1=Goikoetxea |first1=Josu |last2=Agirre |first2=Eneko |last3=Soroa |first3=Aitor |title=Exploring the Use of Word Embeddings and Random Walks on Wikipedia for the Cogalex Shared Task |date=2014 |doi=10.3115/v1/W14-4704 |url=https://wikipediaquality.com/wiki/Exploring_the_Use_of_Word_Embeddings_and_Random_Walks_on_Wikipedia_for_the_Cogalex_Shared_Task}}<br />
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Goikoetxea, Josu; Agirre, Eneko; Soroa, Aitor. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Exploring_the_Use_of_Word_Embeddings_and_Random_Walks_on_Wikipedia_for_the_Cogalex_Shared_Task">Exploring the Use of Word Embeddings and Random Walks on Wikipedia for the Cogalex Shared Task</a>&amp;quot;.DOI: 10.3115/v1/W14-4704. <br />
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[[Category:Scientific works]]</div>Cherihttps://wikipediaquality.com/index.php?title=A_Positive-Unlabeled_Learning_Model_for_Extending_a_Vietnamese_Petroleum_Dictionary_based_on_Vietnamese_Wikipedia_Data&diff=27962A Positive-Unlabeled Learning Model for Extending a Vietnamese Petroleum Dictionary based on Vietnamese Wikipedia Data2021-02-27T07:48:24Z<p>Cheri: Infobox</p>
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<div>{{Infobox work<br />
| title = A Positive-Unlabeled Learning Model for Extending a Vietnamese Petroleum Dictionary based on Vietnamese Wikipedia Data<br />
| date = 2018<br />
| authors = [[Ngoc Trinh Vu]]<br />[[Quoc-Dat Nguyen]]<br />[[Tien-Dat Nguyen]]<br />[[Manh-Cuong Nguyen]]<br />[[Van-Vuong Vu]]<br />[[Quang-Thuy Ha]]<br />
| doi = 10.1007/978-3-319-75417-8_18<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-75417-8_18.pdf<br />
}}<br />
'''A Positive-Unlabeled Learning Model for Extending a Vietnamese Petroleum Dictionary based on Vietnamese Wikipedia Data''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Ngoc Trinh Vu]], [[Quoc-Dat Nguyen]], [[Tien-Dat Nguyen]], [[Manh-Cuong Nguyen]], [[Van-Vuong Vu]] and [[Quang-Thuy Ha]].<br />
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== Overview ==<br />
This study provides a positive-unlabeled learning model for extending a Vietnamese petroleum dictionary based on Vietnamese [[Wikipedia]] data. Machine learning algorithms with positive and unlabeled data together with separated and combined between [[Google]] similarity distance and Cosine similarity distance, used in this study. The data sources used to integrate are English - Vietnamese oil and gas dictionary and the Vietnamese Wikipedia. In the results, a novelty way for data integration with higher accuracy by using a combination of algorithms. The first Vietnamese oil and gas [[ontology]] was built in Vietnam. This ontology is a useful tool for staff in the oil and gas industry in training, research, search daily.</div>Cherihttps://wikipediaquality.com/index.php?title=Wikipedia_in_the_Tourism_Industry:_Forecasting_Demand_and_Modeling_Usage_Behavior&diff=27961Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior2021-02-27T07:46:33Z<p>Cheri: cat.</p>
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<div>{{Infobox work<br />
| title = Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior<br />
| date = 2016<br />
| authors = [[Pejman Khadivi]]<br />[[Naren Ramakrishnan]]<br />
| link = https://dl.acm.org/citation.cfm?id=3016472<br />
}}<br />
'''Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Pejman Khadivi]] and [[Naren Ramakrishnan]].<br />
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== Overview ==<br />
Due to the economic and social impacts of tourism, both private and public sectors are interested in precisely forecasting the tourism demand volume in a timely manner. With recent advances in [[social network]]s, more people use online resources to plan their future trips. In this paper authors explore the application of [[Wikipedia]] usage trends (WUTs) in tourism analysis. Authors propose a framework that deploys WUTs for forecasting the tourism demand of Hawaii. Authors also propose a data-driven approach, using WUTs, to estimate the behavior of tourists when they plan their trips.<br />
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Khadivi, Pejman; Ramakrishnan, Naren. (2016). "[[Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior]]". AAAI Press. <br />
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{{cite journal |last1=Khadivi |first1=Pejman |last2=Ramakrishnan |first2=Naren |title=Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior |date=2016 |url=https://wikipediaquality.com/wiki/Wikipedia_in_the_Tourism_Industry:_Forecasting_Demand_and_Modeling_Usage_Behavior |journal=AAAI Press}}<br />
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Khadivi, Pejman; Ramakrishnan, Naren. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_in_the_Tourism_Industry:_Forecasting_Demand_and_Modeling_Usage_Behavior">Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior</a>&amp;quot;. AAAI Press. <br />
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[[Category:Scientific works]]</div>Cherihttps://wikipediaquality.com/index.php?title=Wiki-Metasemantik:_a_Wikipedia-Derived_Query_Expansion_Approach_based_on_Network_Properties&diff=27960Wiki-Metasemantik: a Wikipedia-Derived Query Expansion Approach based on Network Properties2021-02-27T07:45:28Z<p>Cheri: cat.</p>
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<div>{{Infobox work<br />
| title = Wiki-Metasemantik: a Wikipedia-Derived Query Expansion Approach based on Network Properties<br />
| date = 2017<br />
| authors = [[Diyah Puspitaningrum]]<br />[[Gries Yulianti]]<br />[[I.S.W.B. Prasetya]]<br />
| doi = 10.1109/CITSM.2017.8089228<br />
| link = http://guides.lib.uw.edu/research/scand155/encyclopedias<br />
| plink = http://arxiv.org/pdf/1711.08730.pdf<br />
}}<br />
'''Wiki-Metasemantik: a Wikipedia-Derived Query Expansion Approach based on Network Properties''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Diyah Puspitaningrum]], [[Gries Yulianti]] and [[I.S.W.B. Prasetya]].<br />
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== Overview ==<br />
This paper discusses the use of [[Wikipedia]] for building semantic ontologies to do Query Expansion (QE) in order to improve the search results of search engines. In this technique, selecting related Wikipedia concepts becomes important. Authors propose the use of network properties (degree, closeness, and pageRank) to build an [[ontology]] graph of user query concepts which is derived directly from Wikipedia structures. The resulting expansion system is called Wiki-MetaSemantik. Authors tested this system against other online thesauruses and ontology based QE in both individual and meta-search engines setups. Despite that system has to build a Wikipedia ontology graph in order to do its work, the technique turns out to work very fast (1:281) compared to another ontology QE baseline (Wikipedia Persian ontology QE). It has thus the potential to be utilized online. Furthermore, it shows significant improvement in accuracy. Wiki-MetaSemantik also shows better performance in a meta-search engine (MSE) set up rather than in an individual search engine set up.<br />
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Puspitaningrum, Diyah; Yulianti, Gries; Prasetya, I.S.W.B.. (2017). "[[Wiki-Metasemantik: a Wikipedia-Derived Query Expansion Approach based on Network Properties]]".DOI: 10.1109/CITSM.2017.8089228. <br />
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{{cite journal |last1=Puspitaningrum |first1=Diyah |last2=Yulianti |first2=Gries |last3=Prasetya |first3=I.S.W.B. |title=Wiki-Metasemantik: a Wikipedia-Derived Query Expansion Approach based on Network Properties |date=2017 |doi=10.1109/CITSM.2017.8089228 |url=https://wikipediaquality.com/wiki/Wiki-Metasemantik:_a_Wikipedia-Derived_Query_Expansion_Approach_based_on_Network_Properties}}<br />
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Puspitaningrum, Diyah; Yulianti, Gries; Prasetya, I.S.W.B.. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wiki-Metasemantik:_a_Wikipedia-Derived_Query_Expansion_Approach_based_on_Network_Properties">Wiki-Metasemantik: a Wikipedia-Derived Query Expansion Approach based on Network Properties</a>&amp;quot;.DOI: 10.1109/CITSM.2017.8089228. <br />
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[[Category:Persian Wikipedia]]</div>Cherihttps://wikipediaquality.com/index.php?title=New!_Agency_Redefines_Wikipedia&diff=27959New! Agency Redefines Wikipedia2021-02-27T07:42:37Z<p>Cheri: + embed code</p>
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<div>{{Infobox work<br />
| title = New! Agency Redefines Wikipedia<br />
| date = 2012<br />
| authors = [[David Brbaklic]]<br />
| link = http://www.brandingmagazine.com/2012/08/10/new-agency-redefines-wikipedia/<br />
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'''New! Agency Redefines Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[David Brbaklic]].<br />
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== Overview ==<br />
“Imagine you were granted the magic power to change any website in the whole world-wide web…. Which one would you pick?” Says at the beginning of the introductory text by NEW!, a creative agency from Vilnius, Lithuania, who took the initiative to rethink the largest online knowledge database – [[Wikipedia]]. They’ve started with the thought that both from…<br />
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Brbaklic, David. (2012). "[[New! Agency Redefines Wikipedia]]". Branding Magazine. <br />
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{{cite journal |last1=Brbaklic |first1=David |title=New! Agency Redefines Wikipedia |date=2012 |url=https://wikipediaquality.com/wiki/New!_Agency_Redefines_Wikipedia |journal=Branding Magazine}}<br />
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Brbaklic, David. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/New!_Agency_Redefines_Wikipedia">New! Agency Redefines Wikipedia</a>&amp;quot;. Branding Magazine. <br />
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</code></div>Cherihttps://wikipediaquality.com/index.php?title=Wikipedia_Links_and_Viral_Loops&diff=27958Wikipedia Links and Viral Loops2021-02-27T07:41:33Z<p>Cheri: + cat.</p>
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<div>{{Infobox work<br />
| title = Wikipedia Links and Viral Loops<br />
| date = 2011<br />
| authors = [[Sara Bremen]]<br />
| doi = 10.1111/j.2151-6952.2011.00076.x<br />
| link = http://onlinelibrary.wiley.com/doi/10.1111/j.2151-6952.2011.00076.x/full<br />
}}<br />
'''Wikipedia Links and Viral Loops''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Sara Bremen]].<br />
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== Overview ==<br />
Exhibitions have social potential, beyond just the prospect of sharing the experience with a friend standing next to you. Authors can imagine ways in which the museum space can link art, objects, experiences, people, and technologies to provide a structure within which visitors feel comfortable participating. Their own contributions and the museum’s skillful use of social media can result in a more engaging exhibition, not a dumbing down of the content.<br />
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Bremen, Sara. (2011). "[[Wikipedia Links and Viral Loops]]". Blackwell Publishing Ltd. DOI: 10.1111/j.2151-6952.2011.00076.x. <br />
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{{cite journal |last1=Bremen |first1=Sara |title=Wikipedia Links and Viral Loops |date=2011 |doi=10.1111/j.2151-6952.2011.00076.x |url=https://wikipediaquality.com/wiki/Wikipedia_Links_and_Viral_Loops |journal=Blackwell Publishing Ltd}}<br />
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Bremen, Sara. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_Links_and_Viral_Loops">Wikipedia Links and Viral Loops</a>&amp;quot;. Blackwell Publishing Ltd. DOI: 10.1111/j.2151-6952.2011.00076.x. <br />
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[[Category:Scientific works]]</div>Cherihttps://wikipediaquality.com/index.php?title=Short-Text_Domain_Specific_Key_Terms/Phrases_Extraction_Using_an_N-Gram_Model_with_Wikipedia&diff=27957Short-Text Domain Specific Key Terms/Phrases Extraction Using an N-Gram Model with Wikipedia2021-02-27T07:38:19Z<p>Cheri: + links</p>
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<div>'''Short-Text Domain Specific Key Terms/Phrases Extraction Using an N-Gram Model with Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[M. Atif Qureshi]], [[Colm O'Riordan]] and [[Gabriella Pasi]].<br />
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== Overview ==<br />
Finding domain specific key terms/phrases from a given set of documents is a challenging task. A domain may be defined as an area of interest over a collection of documents which may not be explicitly defined but implicitly observable in those documents. When considering a collection of documents related to academic research, examples of key terms/phrases may be Information Retrieval", "Marine Biology", etc. In this paper a technique for extracting important key terms/phrases in a considered topical domain is proposed using external evidence from the titles of [[Wikipedia]] articles and the Wikipedia category graph. Authors performed some experiments over the document collection of Web sites of different post-graduate schools. Authors preliminary evaluations show promising results for the detection of domain specific key terms/phrases from the given set of domain focused Web pages.</div>Cherihttps://wikipediaquality.com/index.php?title=Research_Guides:_Contributing_to_Wikipedia_%26_Wikimedia_Commons:_Getting_Started&diff=27956Research Guides: Contributing to Wikipedia & Wikimedia Commons: Getting Started2021-02-27T07:35:31Z<p>Cheri: Adding wikilinks</p>
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<div>'''Research Guides: Contributing to Wikipedia & Wikimedia Commons: Getting Started''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Jess Rios]].<br />
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== Overview ==<br />
Wikipedia is the sixth most popular website in the world and authors almost all use it. But, do you know how to contribute to it? This guide will help you get started making edits and additions to [[Wikipedia]] and uploading content to [[Wikimedia]] Commons.</div>Cherihttps://wikipediaquality.com/index.php?title=Mining_Naturally-Occurring_Corrections_and_Paraphrases_from_Wikipedia%27s_Revision_History&diff=27955Mining Naturally-Occurring Corrections and Paraphrases from Wikipedia's Revision History2021-02-27T07:32:29Z<p>Cheri: Categories</p>
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<div>{{Infobox work<br />
| title = Mining Naturally-Occurring Corrections and Paraphrases from Wikipedia's Revision History<br />
| date = 2010<br />
| authors = [[Aurélien Max]]<br />[[Guillaume Wisniewski]]<br />
| link = http://www.lrec-conf.org/proceedings/lrec2010/pdf/827_Paper.pdf<br />
}}<br />
'''Mining Naturally-Occurring Corrections and Paraphrases from Wikipedia's Revision History''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Aurélien Max]] and [[Guillaume Wisniewski]].<br />
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== Overview ==<br />
Naturally-occurring instances of linguistic phenomena are important both for training and for evaluating automatic text processing. When available in large quantities, they also prove interesting material for linguistic studies. In this article, authors present WiCoPaCo ([[Wikipedia]] Correction and Paraphrase Corpus), a new freely-available resource built by automatically mining Wikipedia’s revision history. The WiCoPaCo corpus focuses on local modifications made by human revisors and include various types of corrections (such as spelling error or typographical corrections) and rewritings, which can be categorized broadly into meaning-preserving and meaning-altering revisions. Authors present an initial hand-built typology of these revisions, but the resource allows for any possible annotation scheme. Authors discuss the main motivations for building such a resource and describe the main technical details guiding its construction. Authors also present applications and data analysis on French and report initial results on spelling error correction and morphosyntactic rewriting. The WiCoPaCo corpus can be freely downloaded from http://wicopaco.limsi.fr.<br />
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{{cite journal |last1=Max |first1=Aurélien |last2=Wisniewski |first2=Guillaume |title=Mining Naturally-Occurring Corrections and Paraphrases from Wikipedia's Revision History |date=2010 |url=https://wikipediaquality.com/wiki/Mining_Naturally-Occurring_Corrections_and_Paraphrases_from_Wikipedia's_Revision_History}}<br />
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Max, Aurélien; Wisniewski, Guillaume. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Mining_Naturally-Occurring_Corrections_and_Paraphrases_from_Wikipedia's_Revision_History">Mining Naturally-Occurring Corrections and Paraphrases from Wikipedia's Revision History</a>&amp;quot;.<br />
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[[Category:Scientific works]]<br />
[[Category:French Wikipedia]]</div>Cherihttps://wikipediaquality.com/index.php?title=Building_User_Interest_Profiles_from_Wikipedia_Clusters&diff=27954Building User Interest Profiles from Wikipedia Clusters2021-02-27T07:29:31Z<p>Cheri: Infobox work</p>
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<div>{{Infobox work<br />
| title = Building User Interest Profiles from Wikipedia Clusters<br />
| date = 2011<br />
| authors = [[Jinming Min]]<br />[[Gareth J. F. Jones]]<br />
| link = http://doras.dcu.ie/16398/<br />
}}<br />
'''Building User Interest Profiles from Wikipedia Clusters''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Jinming Min]] and [[Gareth J. F. Jones]].<br />
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== Overview ==<br />
Users of search systems are often reluctant to explicitly build profiles to indicate their search interests. Thus automatically building user profiles is an important research area for personalized search. One difficult component of doing this is accessing a knowledge system which provides broad coverage of user search interests. In this work, authors describe a</div>Cherihttps://wikipediaquality.com/index.php?title=Cirgirgdisco_at_Replab2014_Reputation_Dimension_Task:_Using_Wikipedia_Graph_Structure_for_Classifying_the_Reputation_Dimension_of_a_Tweet&diff=27953Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet2021-02-27T07:26:28Z<p>Cheri: + categories</p>
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<div>{{Infobox work<br />
| title = Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet<br />
| date = 2014<br />
| authors = [[Muhammad Atif Qureshi]]<br />[[Arjumand Younus]]<br />[[Colm O'Riordan]]<br />[[Gabriella Pasi]]<br />
| link = http://ceur-ws.org/Vol-1180/CLEF2014wn-Rep-QureshiEt2014.pdf<br />
}}<br />
'''Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Muhammad Atif Qureshi]], [[Arjumand Younus]], [[Colm O'Riordan]] and [[Gabriella Pasi]].<br />
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== Overview ==<br />
Social media repositories serve as a significant source of ev- idence when extracting information related to the [[reputation]] of a par- ticular entity (e.g., a particular politician, singer or company). Reputa- tion management experts manually mine the social media repositories (in particular [[Twitter]]) for monitoring the reputation of a particular en- tity. Recently, the online reputation management evaluation campaign known as RepLab at CLEF has turned attention to devising computa- tional methods for facilitating reputation management experts. A quite significant research challenge related to the above issue is to classify the reputation dimension of tweets with respect to entity names. More specifically, finding various aspects of a brand's reputation is an impor- tant task which can help companies in monitoring areas of their strengths and weaknesses in an effective manner. To address this issue in this paper authors use dominant [[Wikipedia categories]] related to a reputation dimension in a random forest classifier. Additionally authors also use tweet-specific fea- tures, language-specific [[features]] and similarity-based features. The ex- perimental evaluations show a significant improvement over the baseline accuracy.<br />
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{{cite journal |last1=Qureshi |first1=Muhammad Atif |last2=Younus |first2=Arjumand |last3=O'Riordan |first3=Colm |last4=Pasi |first4=Gabriella |title=Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet |date=2014 |url=https://wikipediaquality.com/wiki/Cirgirgdisco_at_Replab2014_Reputation_Dimension_Task:_Using_Wikipedia_Graph_Structure_for_Classifying_the_Reputation_Dimension_of_a_Tweet}}<br />
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Qureshi, Muhammad Atif; Younus, Arjumand; O'Riordan, Colm; Pasi, Gabriella. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Cirgirgdisco_at_Replab2014_Reputation_Dimension_Task:_Using_Wikipedia_Graph_Structure_for_Classifying_the_Reputation_Dimension_of_a_Tweet">Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet</a>&amp;quot;.<br />
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[[Category:Twi Wikipedia]]</div>Cherihttps://wikipediaquality.com/index.php?title=Research_Guides:&diff=27952Research Guides:2021-02-27T07:24:10Z<p>Cheri: Cats.</p>
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<div>{{Infobox work<br />
| title = Research Guides: #Charlestonsyllabus Research Guide: Wikipedia Edit-A-Thon<br />
| date = 2016<br />
| authors = [[Chanelle Pickens]]<br />
| link = http://libguides.wvu.edu/c.php?g=486441&amp;p=3327189<br />
}}<br />
'''Research Guides: #Charlestonsyllabus Research Guide: Wikipedia Edit-A-Thon''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Chanelle Pickens]].<br />
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== Overview ==<br />
A curated guide on racial violence and race relations in the US based on readings compiled by African-American scholars following the devastating events of June 2015 in Charleston, SC.<br />
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{{cite journal |last1=Pickens |first1=Chanelle |title=Research Guides: #Charlestonsyllabus Research Guide: Wikipedia Edit-A-Thon |date=2016 |url=https://wikipediaquality.com/wiki/Research_Guides:_#Charlestonsyllabus_Research_Guide:_Wikipedia_Edit-A-Thon}}<br />
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Pickens, Chanelle. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Research_Guides:_#Charlestonsyllabus_Research_Guide:_Wikipedia_Edit-A-Thon">Research Guides: #Charlestonsyllabus Research Guide: Wikipedia Edit-A-Thon</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Cherihttps://wikipediaquality.com/index.php?title=Participation_in_Wikipedia%27s_Article_Deletion_Processes&diff=27951Participation in Wikipedia's Article Deletion Processes2021-02-27T07:22:34Z<p>Cheri: + categories</p>
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<div>{{Infobox work<br />
| title = Participation in Wikipedia's Article Deletion Processes<br />
| date = 2011<br />
| authors = [[R. Stuart Geiger]]<br />[[Heather Ford]]<br />
| doi = 10.1145/2038558.2038593<br />
| link = http://dl.acm.org/citation.cfm?id=2038558.2038593<br />
| plink = https://www.researchgate.net/profile/Rstuart_Geiger/publication/221367824_Participation_in_Wikipedia&#039;s_article_deletion_processes/links/00463539f034226a89000000.pdf<br />
}}<br />
'''Participation in Wikipedia's Article Deletion Processes''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[R. Stuart Geiger]] and [[Heather Ford]].<br />
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== Overview ==<br />
Authors present results on a study of two levels of [[Wikipedia]]'s article deletion process: speedy deletions (or CSDs) and articles for deletions (or AfDs). Authors findings indicate that the deletion process is heavily frequented by a relatively small number of longstanding users. In analyzing the rationales given for such deletions, it is apparent that the vast majority of such deleted articles are not spam, vandalism, or 'patent nonsense,' but rather articles which could be considered encyclopedic, but do not fit the project's standards.<br />
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Geiger, R. Stuart; Ford, Heather. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Participation_in_Wikipedia's_Article_Deletion_Processes">Participation in Wikipedia's Article Deletion Processes</a>&amp;quot;.DOI: 10.1145/2038558.2038593. <br />
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[[Category:Scientific works]]</div>Cherihttps://wikipediaquality.com/index.php?title=Searching_Wikipedia:_Learning_the_Why,_the_How,_and_the_Role_Played_by_Emotion&diff=27950Searching Wikipedia: Learning the Why, the How, and the Role Played by Emotion2021-02-27T07:21:12Z<p>Cheri: Cats.</p>
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<div>{{Infobox work<br />
| title = Searching Wikipedia: Learning the Why, the How, and the Role Played by Emotion<br />
| date = 2012<br />
| authors = [[Hanna Knäusl]]<br />
| link = http://ceur-ws.org/Vol-836/paper5.pdf<br />
}}<br />
'''Searching Wikipedia: Learning the Why, the How, and the Role Played by Emotion''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Hanna Knäusl]].<br />
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== Overview ==<br />
Searching [[Wikipedia]] has been the focus of study for an increasing number of [[information retrieval]] publications. In recent years dierent IR tasks have used Wikipedia as a basis for evaluating algorithms and interfaces for various types of search tasks, including Question Answering, Exploratory Search, Entity Search and Structured Document retrieval. Despite being associated with these well-dened task types, little is known about why people actually search wikipedia, what they try to nd, how and why they try to<br />
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Knäusl, Hanna. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Searching_Wikipedia:_Learning_the_Why,_the_How,_and_the_Role_Played_by_Emotion">Searching Wikipedia: Learning the Why, the How, and the Role Played by Emotion</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Cherihttps://wikipediaquality.com/index.php?title=Visualizing_and_Exploring_Evolving_Information_Networks_in_Wikipedia&diff=27949Visualizing and Exploring Evolving Information Networks in Wikipedia2021-02-27T07:18:13Z<p>Cheri: Adding categories</p>
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<div>{{Infobox work<br />
| title = Visualizing and Exploring Evolving Information Networks in Wikipedia<br />
| date = 2010<br />
| authors = [[Ee-Peng Lim]]<br />[[Agus Trisnajaya Kwee]]<br />[[Nelman Lubis Ibrahim]]<br />[[Aixin Sun]]<br />[[Anwitaman Datta]]<br />[[Kuiyu Chang]]<br />[[Maureen Maureen]]<br />
| doi = 10.1007/978-3-642-13654-2_7<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-642-13654-2_7.pdf<br />
}}<br />
'''Visualizing and Exploring Evolving Information Networks in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Ee-Peng Lim]], [[Agus Trisnajaya Kwee]], [[Nelman Lubis Ibrahim]], [[Aixin Sun]], [[Anwitaman Datta]], [[Kuiyu Chang]] and [[Maureen Maureen]].<br />
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== Overview ==<br />
Information networks in [[Wikipedia]] evolve as users collaboratively edit articles that embed the networks. These information networks represent both the structure and content of community's knowledge and the networks evolve as the knowledge gets updated. By observing the networks evolve and finding their evolving patterns, one can gain higher order knowledge about the networks and conduct longitudinal network analysis to detect events and summarize trends. In this paper, authors present SSNetViz+, a visual analytic tool to support visualization and exploration of Wikipedia's information networks. SSNetViz+ supports time-based network browsing, content browsing and search. Using a terrorism information network as an example, authors show that different timestamped versions of the network can be interactively explored. As information networks in Wikipedia are created and maintained by collaborative editing efforts, the edit activity data are also shown to help detecting interesting events that may have happened to the network. SSNetViz+ also supports temporal queries that allow other relevant nodes to be added so as to expand the network being analyzed.<br />
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Lim, Ee-Peng; Kwee, Agus Trisnajaya; Ibrahim, Nelman Lubis; Sun, Aixin; Datta, Anwitaman; Chang, Kuiyu; Maureen, Maureen. (2010). "[[Visualizing and Exploring Evolving Information Networks in Wikipedia]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-13654-2_7. <br />
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{{cite journal |last1=Lim |first1=Ee-Peng |last2=Kwee |first2=Agus Trisnajaya |last3=Ibrahim |first3=Nelman Lubis |last4=Sun |first4=Aixin |last5=Datta |first5=Anwitaman |last6=Chang |first6=Kuiyu |last7=Maureen |first7=Maureen |title=Visualizing and Exploring Evolving Information Networks in Wikipedia |date=2010 |doi=10.1007/978-3-642-13654-2_7 |url=https://wikipediaquality.com/wiki/Visualizing_and_Exploring_Evolving_Information_Networks_in_Wikipedia |journal=Springer, Berlin, Heidelberg}}<br />
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Lim, Ee-Peng; Kwee, Agus Trisnajaya; Ibrahim, Nelman Lubis; Sun, Aixin; Datta, Anwitaman; Chang, Kuiyu; Maureen, Maureen. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Visualizing_and_Exploring_Evolving_Information_Networks_in_Wikipedia">Visualizing and Exploring Evolving Information Networks in Wikipedia</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-13654-2_7. <br />
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[[Category:Scientific works]]</div>Cherihttps://wikipediaquality.com/index.php?title=Ranking_Very_Many_Typed_Entities_on_Wikipedia&diff=27948Ranking Very Many Typed Entities on Wikipedia2021-02-27T07:16:35Z<p>Cheri: + embed code</p>
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<div>{{Infobox work<br />
| title = Ranking Very Many Typed Entities on Wikipedia<br />
| date = 2007<br />
| authors = [[Hugo Zaragoza]]<br />[[Henning Rode]]<br />[[Peter Mika]]<br />[[Jordi Atserias]]<br />[[Massimiliano Ciaramita]]<br />[[Giuseppe Attardi]]<br />
| doi = 10.1145/1321440.1321599<br />
| link = https://dl.acm.org/citation.cfm?id=1321599<br />
}}<br />
'''Ranking Very Many Typed Entities on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Hugo Zaragoza]], [[Henning Rode]], [[Peter Mika]], [[Jordi Atserias]], [[Massimiliano Ciaramita]] and [[Giuseppe Attardi]].<br />
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== Overview ==<br />
Authors discuss the problem of ranking very many entities of different types. In particular authors deal with a heterogeneous set of types, some being very generic and some very specific. Authors discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine to compute entity relevance. Authors evaluate these approaches on the real task of ranking [[Wikipedia]] entities typed with a state-of-the-art named-entity tagger. Results show that both approaches can greatly increase the performance of methods based only on passage retrieval.<br />
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Zaragoza, Hugo; Rode, Henning; Mika, Peter; Atserias, Jordi; Ciaramita, Massimiliano; Attardi, Giuseppe. (2007). "[[Ranking Very Many Typed Entities on Wikipedia]]". ACM Press. DOI: 10.1145/1321440.1321599. <br />
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{{cite journal |last1=Zaragoza |first1=Hugo |last2=Rode |first2=Henning |last3=Mika |first3=Peter |last4=Atserias |first4=Jordi |last5=Ciaramita |first5=Massimiliano |last6=Attardi |first6=Giuseppe |title=Ranking Very Many Typed Entities on Wikipedia |date=2007 |doi=10.1145/1321440.1321599 |url=https://wikipediaquality.com/wiki/Ranking_Very_Many_Typed_Entities_on_Wikipedia |journal=ACM Press}}<br />
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Zaragoza, Hugo; Rode, Henning; Mika, Peter; Atserias, Jordi; Ciaramita, Massimiliano; Attardi, Giuseppe. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Ranking_Very_Many_Typed_Entities_on_Wikipedia">Ranking Very Many Typed Entities on Wikipedia</a>&amp;quot;. ACM Press. DOI: 10.1145/1321440.1321599. <br />
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</code></div>Cherihttps://wikipediaquality.com/index.php?title=Content_Hole_Search_in_Community-Type_Content_Using_Wikipedia&diff=27947Content Hole Search in Community-Type Content Using Wikipedia2021-02-27T07:15:09Z<p>Cheri: + embed code</p>
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<div>{{Infobox work<br />
| title = Content Hole Search in Community-Type Content Using Wikipedia<br />
| date = 2009<br />
| authors = [[Akiyo Nadamoto]]<br />[[Eiji Aramaki]]<br />[[Takeshi Abekawa]]<br />[[Yohei Murakami]]<br />
| doi = 10.1145/1806338.1806353<br />
| link = https://dl.acm.org/citation.cfm?id=1806338.1806353<br />
}}<br />
'''Content Hole Search in Community-Type Content Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Akiyo Nadamoto]], [[Eiji Aramaki]], [[Takeshi Abekawa]] and [[Yohei Murakami]].<br />
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== Overview ==<br />
SNSs and blogs, both of which are maintained by a community of people, have become popular in Web 2.0. Authors call these content as "Community-type content." This community is associated with the content, and those who use or contribute to community-type content are considered as members of the community. Occasionally, the members of a community do not understand the theme of the content from multiple viewpoints, hence, the amount of information is often insufficient. It is convenient to present the user missed information. In this way, when Web 2.0 became popular, the content on the Internet and type of users are changed. Authors believe that there is a need for next-generation search engines in Web 2.0. Authors require a search engine that can search for information users are unaware of; authors call such information as "content holes." In this paper, authors propose a method for searching content holes in community-type content. Authors attempt to extract and represent content holes from discussions on SNSs and blogs. Conventional Web search technique is generally based on similarities. On the other hand, content-hole search is a different search. In this paper, authors classify and represent a number of images for different searching methods; authors define content holes and as the first step toward realizing aim, authors propose a content-hole search system using [[Wikipedia]].<br />
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Nadamoto, Akiyo; Aramaki, Eiji; Abekawa, Takeshi; Murakami, Yohei. (2009). "[[Content Hole Search in Community-Type Content Using Wikipedia]]".DOI: 10.1145/1806338.1806353. <br />
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Nadamoto, Akiyo; Aramaki, Eiji; Abekawa, Takeshi; Murakami, Yohei. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Content_Hole_Search_in_Community-Type_Content_Using_Wikipedia">Content Hole Search in Community-Type Content Using Wikipedia</a>&amp;quot;.DOI: 10.1145/1806338.1806353. <br />
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</code></div>Cherihttps://wikipediaquality.com/index.php?title=Stigmergic_Coordination_in_Wikipedia&diff=27946Stigmergic Coordination in Wikipedia2021-02-27T07:12:30Z<p>Cheri: + embed code</p>
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<div>{{Infobox work<br />
| title = Stigmergic Coordination in Wikipedia<br />
| date = 2018<br />
| authors = [[Amira Rezgui]]<br />[[Kevin Crowston]]<br />
| doi = 10.1145/3233391.3233543<br />
| link = https://dl.acm.org/citation.cfm?doid=3233391.3233543<br />
}}<br />
'''Stigmergic Coordination in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Amira Rezgui]] and [[Kevin Crowston]].<br />
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== Overview ==<br />
Authors look for evidence of stigmergic coordination (i.e., coordination mediated by changes to a shared work product) in the context of [[Wikipedia]]. Using a novel approach to identifying edits to the same part of a Wikipedia article, authors show that a majority of edits to two example articles are not associated with discussion on the article Talk page, suggesting the possibility of stigmergic coordination. However, discussion does seem to be related to [[article quality]], suggesting the limits to this approach to coordination.<br />
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Rezgui, Amira; Crowston, Kevin. (2018). "[[Stigmergic Coordination in Wikipedia]]". ACM Press. DOI: 10.1145/3233391.3233543. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Rezgui |first1=Amira |last2=Crowston |first2=Kevin |title=Stigmergic Coordination in Wikipedia |date=2018 |doi=10.1145/3233391.3233543 |url=https://wikipediaquality.com/wiki/Stigmergic_Coordination_in_Wikipedia |journal=ACM Press}}<br />
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Rezgui, Amira; Crowston, Kevin. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Stigmergic_Coordination_in_Wikipedia">Stigmergic Coordination in Wikipedia</a>&amp;quot;. ACM Press. DOI: 10.1145/3233391.3233543. <br />
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</code></div>Cherihttps://wikipediaquality.com/index.php?title=Semantic_Sense_Extraction_from_Wikipedia_Pages&diff=27945Semantic Sense Extraction from Wikipedia Pages2021-02-27T07:09:22Z<p>Cheri: Infobox work</p>
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<div>{{Infobox work<br />
| title = Semantic Sense Extraction from Wikipedia Pages<br />
| date = 2010<br />
| authors = [[Arianna Pipitone]]<br />[[Giuseppe Russo]]<br />
| doi = 10.1109/HSI.2010.5514514<br />
| link = http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5514514<br />
}}<br />
'''Semantic Sense Extraction from Wikipedia Pages''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Arianna Pipitone]] and [[Giuseppe Russo]].<br />
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== Overview ==<br />
This paper discusses a modality to access and to organize unstructured contents related to a particular topic coming from the access to [[Wikipedia]] pages. The proposed approach is focused on the acquisition of new knowledge from Wikipedia pages and is based on the definition of useful patterns able to extract and identify novel concepts and relations to be added in the knowledge base. Authors proposes a method that uses information from the wiki page's structure. According to the different part of the page authors define different strategies to obtain new concepts or relation between them. Authors analyze not only structure but text directly to obtain relations and concepts and to extract the type of relations to be incorporated in a domain [[ontology]]. The purpose is to use the obtained information in an intelligent tutoring system to improve his capabilities in dialogue management with users.</div>Cherihttps://wikipediaquality.com/index.php?title=Interactions_and_Influence_of_World_Painters_from_the_Reduced_Google_Matrix_of_Wikipedia_Networks&diff=27944Interactions and Influence of World Painters from the Reduced Google Matrix of Wikipedia Networks2021-02-27T07:05:20Z<p>Cheri: Int.links</p>
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<div>'''Interactions and Influence of World Painters from the Reduced Google Matrix of Wikipedia Networks''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Samer El Zant]], [[Katia Jaffrès-Runser]], [[Klaus M. Frahm]] and [[Dima L. Shepelyansky]].<br />
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== Overview ==<br />
This paper concentrates on extracting painting art history knowledge from the network structure of [[Wikipedia]]. Therefore, authors construct theoretical networks of webpages representing the hyper-linked structure of articles of seven Wikipedia language editions. These seven networks are analyzed to extract the most influential painters in each edition using [[Google]] matrix theory. Importance of webpages of over 3000 painters is measured using the PageRank algorithm. The most influential painters are enlisted and their ties are studied with the reduced Google matrix analysis. The reduced Google matrix is a powerful method that captures both direct and hidden interactions between a subset of selected nodes taking into account the indirect links between these nodes via the remaining part of large global network. This method originates from the scattering theory of nuclear and mesoscopic physics and field of quantum chaos. In this paper, authors show that it is possible to extract from the components of the reduced Google matrix meaningful information on the ties between these painters. For instance, analysis groups together painters that belong to the same painting movement and shows meaningful ties between painters of different movements. Authors also determine the influence of painters on world countries using link sensitivity between Wikipedia articles of painters and countries. The reduced Google matrix approach allows to obtain a balanced view of various cultural opinions of Wikipedia language editions. The world countries with the largest number of top painters of selected seven Wikipedia editions are found to be Italy, France, and Russia. Authors argue that this approach gives meaningful information about art and that it could be a part of extensive network analysis on human knowledge and cultures.</div>Cherihttps://wikipediaquality.com/index.php?title=Wikipedia_Culture_Gap:_Quantifying_Content_Imbalances_Across_40_Language_Editions&diff=27943Wikipedia Culture Gap: Quantifying Content Imbalances Across 40 Language Editions2021-02-27T07:01:19Z<p>Cheri: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Wikipedia Culture Gap: Quantifying Content Imbalances Across 40 Language Editions<br />
| date = 2018<br />
| authors = [[Marc Miquel-Ribé]]<br />[[David Laniado]]<br />
| doi = 10.3389/fdigh.2018.00012<br />
| link = https://www.frontiersin.org/articles/10.3389/fdigh.2018.00012<br />
}}<br />
'''Wikipedia Culture Gap: Quantifying Content Imbalances Across 40 Language Editions''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Marc Miquel-Ribé]] and [[David Laniado]].<br />
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== Overview ==<br />
The online encyclopedia [[Wikipedia]] is the largest general information repository created through collaborative efforts from all over the globe. Despite the project’s goal being to achieve the sum of human knowledge, there are strong content imbalances across the language editions. In order to quantify and investigate these imbalances, authors study the impact of cultural context in 40 language editions. To this purpose, authors developed a computational method to identify articles that can be related to the editors' cultural context associated to each Wikipedia language edition. Authors employed a combination of strategies taking into account geolocated articles, specific keywords and [[categories]], as well as links between articles. Authors verified the method’s quality with manual assessment and found an average precision of 0.92 and an average recall of 0.95. The results show that about a quarter of each Wikipedia language edition is dedicated to represent the corresponding cultural context. Although a considerable part of this content was created during the first years of the project, its creation is sustained over time. An analysis of cross-language coverage of this content shows that most of it is unique in its original language, and reveals special links between cultural contexts; at the same time, it highlights gaps where the encyclopaedia could extend its content. The approach and findings presented in this study can help to foster participation and inter-cultural enrichment of Wikipedias. The datasets produced are made available for further research.<br />
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Miquel-Ribé, Marc; Laniado, David. (2018). "[[Wikipedia Culture Gap: Quantifying Content Imbalances Across 40 Language Editions]]". Frontiers. DOI: 10.3389/fdigh.2018.00012. <br />
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{{cite journal |last1=Miquel-Ribé |first1=Marc |last2=Laniado |first2=David |title=Wikipedia Culture Gap: Quantifying Content Imbalances Across 40 Language Editions |date=2018 |doi=10.3389/fdigh.2018.00012 |url=https://wikipediaquality.com/wiki/Wikipedia_Culture_Gap:_Quantifying_Content_Imbalances_Across_40_Language_Editions |journal=Frontiers}}<br />
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Miquel-Ribé, Marc; Laniado, David. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_Culture_Gap:_Quantifying_Content_Imbalances_Across_40_Language_Editions">Wikipedia Culture Gap: Quantifying Content Imbalances Across 40 Language Editions</a>&amp;quot;. Frontiers. DOI: 10.3389/fdigh.2018.00012. <br />
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</code></div>Cherihttps://wikipediaquality.com/index.php?title=Governance_in_Social_Media:_a_Case_Study_of_the_Wikipedia_Promotion_Process&diff=27942Governance in Social Media: a Case Study of the Wikipedia Promotion Process2021-02-27T06:58:17Z<p>Cheri: + categories</p>
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<div>{{Infobox work<br />
| title = Governance in Social Media: a Case Study of the Wikipedia Promotion Process<br />
| date = 2010<br />
| authors = [[Jure Leskovec]]<br />[[Daniel P. Huttenlocher]]<br />[[Jon M. Kleinberg]]<br />
| link = http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1056&amp;context=dubaipapers<br />
| plink = http://arxiv.org/pdf/1004.3547.pdf<br />
}}<br />
'''Governance in Social Media: a Case Study of the Wikipedia Promotion Process''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Jure Leskovec]], [[Daniel P. Huttenlocher]] and [[Jon M. Kleinberg]].<br />
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== Overview ==<br />
Social media sites are often guided by a core group of committed users engaged in various forms of governance. A crucial aspect of this type of governance is deliberation, in which such a group reaches decisions on issues of importance to the site. Despite its crucial — though subtle — role in how a number of prominent social media sites function, there has been relatively little investigation of the deliberative aspects of social media governance. Here authors explore this issue, investigating a particular deliberative process that is extensive, public, and recorded: the promotion of [[Wikipedia]] admins, which is determined by elections that engage committed members of the [[Wikipedia community]]. Authors find that the group decision-making at the heart of this process exhibits several fundamental forms of relative assessment. First authors observe that the chance that a voter will support a candidate is strongly dependent on the relationship between characteristics of the voter and the candidate. Second authors investigate how both individual voter decisions and overall election outcomes can be based on models that take into account the sequential, public nature of the voting.<br />
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Leskovec, Jure; Huttenlocher, Daniel P.; Kleinberg, Jon M.. (2010). "[[Governance in Social Media: a Case Study of the Wikipedia Promotion Process]]".<br />
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{{cite journal |last1=Leskovec |first1=Jure |last2=Huttenlocher |first2=Daniel P. |last3=Kleinberg |first3=Jon M. |title=Governance in Social Media: a Case Study of the Wikipedia Promotion Process |date=2010 |url=https://wikipediaquality.com/wiki/Governance_in_Social_Media:_a_Case_Study_of_the_Wikipedia_Promotion_Process}}<br />
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Leskovec, Jure; Huttenlocher, Daniel P.; Kleinberg, Jon M.. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Governance_in_Social_Media:_a_Case_Study_of_the_Wikipedia_Promotion_Process">Governance in Social Media: a Case Study of the Wikipedia Promotion Process</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Cherihttps://wikipediaquality.com/index.php?title=Kshitij:_a_Search_and_Page_Recommendation_System_for_Wikipedia&diff=27941Kshitij: a Search and Page Recommendation System for Wikipedia2021-02-27T06:55:16Z<p>Cheri: Infobox</p>
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<div>{{Infobox work<br />
| title = Kshitij: a Search and Page Recommendation System for Wikipedia<br />
| date = 2008<br />
| authors = [[Phanikumar Bhamidipati]]<br />[[Kamalakar Karlapalem]]<br />
| link = https://www.cse.iitb.ac.in/~comad/2008/PDFs/48-kshitij.pdf<br />
}}<br />
'''Kshitij: a Search and Page Recommendation System for Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Phanikumar Bhamidipati]] and [[Kamalakar Karlapalem]].<br />
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== Overview ==<br />
Semantic information helps in identifying the context of a document. It will be interesting to find out how effectively this information can be used in recommending related documents in a partially annotated knowledge base such as [[Wikipedia]]. In this paper, authors present a generic recommendation system that utilizes the stored as well as dynamically extracted semantics from Wikipedia. The system generates two kinds of recommendations - for search results and for each page viewed by the user. It explores different meta-information such as links and [[categories]] in this process. Authors experiments show that the system is able to yield good quality recommendations and help in improving the user experience. Though the algorithms are tested on Wikipedia, external systems that do not have access to structured data can benefit from the recommendations.</div>Cheri