https://wikipediaquality.com/api.php?action=feedcontributions&user=Aubree&feedformat=atomWikipedia Quality - User contributions [en]2024-03-28T20:01:41ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Measuring_Similarities_Between_Technical_Terms_based_on_Wikipedia&diff=24478Measuring Similarities Between Technical Terms based on Wikipedia2020-06-06T06:46:37Z<p>Aubree: + embed code</p>
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<div>{{Infobox work<br />
| title = Measuring Similarities Between Technical Terms based on Wikipedia<br />
| date = 2011<br />
| authors = [[Myunggwon Hwang]]<br />[[Do-Heon Jeong]]<br />[[Seungwoo Lee]]<br />[[Hanmin Jung]]<br />
| doi = 10.1109/iThings/CPSCom.2011.38<br />
| link = http://ieeexplore.ieee.org/document/6142233/<br />
}}<br />
'''Measuring Similarities Between Technical Terms based on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Myunggwon Hwang]], [[Do-Heon Jeong]], [[Seungwoo Lee]] and [[Hanmin Jung]].<br />
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== Overview ==<br />
The term similarities are utilized for measuring [[relatedness]] which is useful for [[semantic information]] processing such as content understanding, query expansion and word sense disambiguation. In research, term similarities are used for grasping technical terms deeply related to emerging technologies. To measure term similarities, authors propose a hybrid method based on both [[Wikipedia]] category and internal link information. And its performance is evaluated by comparative analysis with WLM (Wikipedia Link-based Measure), which is a state-of-the-art methodology. The performance evaluation shows that method works better than other methods.<br />
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Hwang, Myunggwon; Jeong, Do-Heon; Lee, Seungwoo; Jung, Hanmin. (2011). "[[Measuring Similarities Between Technical Terms based on Wikipedia]]".DOI: 10.1109/iThings/CPSCom.2011.38. <br />
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{{cite journal |last1=Hwang |first1=Myunggwon |last2=Jeong |first2=Do-Heon |last3=Lee |first3=Seungwoo |last4=Jung |first4=Hanmin |title=Measuring Similarities Between Technical Terms based on Wikipedia |date=2011 |doi=10.1109/iThings/CPSCom.2011.38 |url=https://wikipediaquality.com/wiki/Measuring_Similarities_Between_Technical_Terms_based_on_Wikipedia}}<br />
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Hwang, Myunggwon; Jeong, Do-Heon; Lee, Seungwoo; Jung, Hanmin. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Measuring_Similarities_Between_Technical_Terms_based_on_Wikipedia">Measuring Similarities Between Technical Terms based on Wikipedia</a>&amp;quot;.DOI: 10.1109/iThings/CPSCom.2011.38. <br />
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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=Mmkg:_an_Approach_to_Generate_Metallic_Materials_Knowledge_Graph_based_on_Dbpedia_and_Wikipedia&diff=24477Mmkg: an Approach to Generate Metallic Materials Knowledge Graph based on Dbpedia and Wikipedia2020-06-06T06:43:53Z<p>Aubree: Category</p>
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<div>{{Infobox work<br />
| title = Mmkg: an Approach to Generate Metallic Materials Knowledge Graph based on Dbpedia and Wikipedia<br />
| date = 2017<br />
| authors = [[Xiaoming Zhang]]<br />[[Xin Liu]]<br />[[Xin Li]]<br />[[Dongyu Pan]]<br />
| doi = 10.1016/j.cpc.2016.07.005<br />
| link = http://www.sciencedirect.com/science/article/pii/S0010465516301874<br />
}}<br />
'''Mmkg: an Approach to Generate Metallic Materials Knowledge Graph based on Dbpedia and Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Xiaoming Zhang]], [[Xin Liu]], [[Xin Li]] and [[Dongyu Pan]].<br />
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== Overview ==<br />
Abstract The research and development of metallic materials are playing an important role in today’s society, and in the meanwhile lots of metallic materials knowledge is generated and available on the Web (e.g., [[Wikipedia]]) for materials experts. However, due to the diversity and complexity of metallic materials knowledge, the knowledge utilization may encounter much inconvenience. The idea of knowledge graph (e.g., [[DBpedia]]) provides a good way to organize the knowledge into a comprehensive entity network. Therefore, the motivation of work is to generate a metallic materials knowledge graph (MMKG) using available knowledge on the Web. In this paper, an approach is proposed to build MMKG based on DBpedia and Wikipedia. First, authors use an algorithm based on directly linked sub-graph semantic distance (DLSSD) to preliminarily extract metallic materials entities from DBpedia according to some predefined seed entities; then based on the results of the preliminary extraction, authors use an algorithm, which considers both semantic distance and string similarity (SDSS), to achieve the further extraction. Second, due to the absence of materials properties in DBpedia, authors use an [[ontology]]-based method to extract properties knowledge from the HTML tables of corresponding Wikipedia Web pages for enriching MMKG. Materials ontology is used to locate materials properties tables as well as to identify the structure of the tables. The proposed approach is evaluated by precision, recall, F1 and time performance, and meanwhile the appropriate thresholds for the algorithms in approach are determined through experiments. The experimental results show that approach returns expected performance. A tool prototype is also designed to facilitate the process of building the MMKG as well as to demonstrate the effectiveness of approach.<br />
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Zhang, Xiaoming; Liu, Xin; Li, Xin; Pan, Dongyu. (2017). "[[Mmkg: an Approach to Generate Metallic Materials Knowledge Graph based on Dbpedia and Wikipedia]]". North-Holland. DOI: 10.1016/j.cpc.2016.07.005. <br />
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{{cite journal |last1=Zhang |first1=Xiaoming |last2=Liu |first2=Xin |last3=Li |first3=Xin |last4=Pan |first4=Dongyu |title=Mmkg: an Approach to Generate Metallic Materials Knowledge Graph based on Dbpedia and Wikipedia |date=2017 |doi=10.1016/j.cpc.2016.07.005 |url=https://wikipediaquality.com/wiki/Mmkg:_an_Approach_to_Generate_Metallic_Materials_Knowledge_Graph_based_on_Dbpedia_and_Wikipedia |journal=North-Holland}}<br />
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Zhang, Xiaoming; Liu, Xin; Li, Xin; Pan, Dongyu. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Mmkg:_an_Approach_to_Generate_Metallic_Materials_Knowledge_Graph_based_on_Dbpedia_and_Wikipedia">Mmkg: an Approach to Generate Metallic Materials Knowledge Graph based on Dbpedia and Wikipedia</a>&amp;quot;. North-Holland. DOI: 10.1016/j.cpc.2016.07.005. <br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=The_Xtrieval_Framework_at_Clef_2008:_Imageclef_Wikipedia_Mm_Task&diff=24476The Xtrieval Framework at Clef 2008: Imageclef Wikipedia Mm Task2020-06-06T06:40:53Z<p>Aubree: + categories</p>
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<div>{{Infobox work<br />
| title = The Xtrieval Framework at Clef 2008: Imageclef Wikipedia Mm Task<br />
| date = 2008<br />
| authors = [[Thomas Wilhelm]]<br />[[Jens Kürsten]]<br />[[Maximilian Eibl]]<br />
| link = http://ceur-ws.org/Vol-1174/CLEF2008wn-ImageCLEF-WilhelmEt2008b.pdf<br />
}}<br />
'''The Xtrieval Framework at Clef 2008: Imageclef Wikipedia Mm Task''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Thomas Wilhelm]], [[Jens Kürsten]] and [[Maximilian Eibl]].<br />
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== Overview ==<br />
This paper describes participation at the ImageCLEF [[Wikipedia]] MM task. Authors used Xtrieval framework for the preparation and execution of the experiments. Authors submitted 4 experiments in total. The results of these experiments were mixed. The text-only experiment scored second best with a mean average precision (MAP) of 0.2166. In combination with image based [[features]] the MAP dropped to 0.2138. With the addition of thesaurus based query expansion it scored best with a MAP of 0.2195. Without query expansion and with the inclusion of the provided concepts the lowest MAP of 0.2048 was achieved, but there were 23 more relevant documents retrieved than in all 3 other experiments. Furthermore, the retrieval speed and comparison operations for vectors could be speeded up by implementing an interface to the PostgreSQL database.<br />
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Wilhelm, Thomas; Kürsten, Jens; Eibl, Maximilian. (2008). "[[The Xtrieval Framework at Clef 2008: Imageclef Wikipedia Mm Task]]".<br />
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{{cite journal |last1=Wilhelm |first1=Thomas |last2=Kürsten |first2=Jens |last3=Eibl |first3=Maximilian |title=The Xtrieval Framework at Clef 2008: Imageclef Wikipedia Mm Task |date=2008 |url=https://wikipediaquality.com/wiki/The_Xtrieval_Framework_at_Clef_2008:_Imageclef_Wikipedia_Mm_Task}}<br />
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Wilhelm, Thomas; Kürsten, Jens; Eibl, Maximilian. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/The_Xtrieval_Framework_at_Clef_2008:_Imageclef_Wikipedia_Mm_Task">The Xtrieval Framework at Clef 2008: Imageclef Wikipedia Mm Task</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=Utilizing_Wikipedia_as_a_Knowledge_Source_in_Categorizing_Topic_Related_Korean_Blogs_into_Facets&diff=24475Utilizing Wikipedia as a Knowledge Source in Categorizing Topic Related Korean Blogs into Facets2020-06-06T06:38:40Z<p>Aubree: + embed code</p>
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<div>{{Infobox work<br />
| title = Utilizing Wikipedia as a Knowledge Source in Categorizing Topic Related Korean Blogs into Facets<br />
| date = 2011<br />
| authors = [[Dongkwon Lim]]<br />[[Daisuke Yokomoto]]<br />[[Kensaku Makita]]<br />[[Takehito Utsuro]]<br />[[Tomohiro Fukuhara]]<br />
| link = http://www.anlp.jp/proceedings/annual_meeting/2011/pdf_dir/F3-6.pdf<br />
}}<br />
'''Utilizing Wikipedia as a Knowledge Source in Categorizing Topic Related Korean Blogs into Facets''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Dongkwon Lim]], [[Daisuke Yokomoto]], [[Kensaku Makita]], [[Takehito Utsuro]] and [[Tomohiro Fukuhara]].<br />
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== Overview ==<br />
As blog services and blog tools are becoming more and more popular, people have been able to express one’s own interests as well as opinions on the Web. Search engines are then used for accessing various information that can be found in the blogosphere, where, given a search query, a ranked list of blog posts is provided as a search result. However, such a search result in the form of a ranked list is not usually helpful for a user to quickly identify blog posts that satisfy his/her information need. This is especially true when, given a search query, the search result is a mixture of blog posts that focus on various sub-topics. In such a situation, the framework of faceted search [8], which has been well studied in the [[information retrieval]] community, can be a solution. In this paper, authors propose a framework of categorizing Korean blog posts according to their sub-topics, where, given a search query, those blog posts are collected from the Korean blogosphere. In framework, the sub-topic of each blog post is regarded as a facet of an initial topic keyword, and a facet is automatically assigned to each blog post. For example, Figure 1 illustrates a result of faceted search for an initial topic keyword “global warming” within the Korean blogosphere. In this result, a number of collected blog posts regarding “global warming” are categorized into facets by identifying each blogger’s interest in a blog post. This procedure of assigning a facet to a blog post is realized by utilizing [[Wikipedia]] entries as a knowledge source and each Wikipedia entry title is considered as a facet label. In the evaluation, authors can achieve about 50∼70 % accuracy.<br />
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Lim, Dongkwon; Yokomoto, Daisuke; Makita, Kensaku; Utsuro, Takehito; Fukuhara, Tomohiro. (2011). "[[Utilizing Wikipedia as a Knowledge Source in Categorizing Topic Related Korean Blogs into Facets]]".<br />
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{{cite journal |last1=Lim |first1=Dongkwon |last2=Yokomoto |first2=Daisuke |last3=Makita |first3=Kensaku |last4=Utsuro |first4=Takehito |last5=Fukuhara |first5=Tomohiro |title=Utilizing Wikipedia as a Knowledge Source in Categorizing Topic Related Korean Blogs into Facets |date=2011 |url=https://wikipediaquality.com/wiki/Utilizing_Wikipedia_as_a_Knowledge_Source_in_Categorizing_Topic_Related_Korean_Blogs_into_Facets}}<br />
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Lim, Dongkwon; Yokomoto, Daisuke; Makita, Kensaku; Utsuro, Takehito; Fukuhara, Tomohiro. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Utilizing_Wikipedia_as_a_Knowledge_Source_in_Categorizing_Topic_Related_Korean_Blogs_into_Facets">Utilizing Wikipedia as a Knowledge Source in Categorizing Topic Related Korean Blogs into Facets</a>&amp;quot;.<br />
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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=Ontology_Enhanced_Web_Image_Retrieval:_Aided_by_Wikipedia_%26_Spreading_Activation_Theory&diff=24474Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory2020-06-06T06:36:38Z<p>Aubree: + category</p>
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<div>{{Infobox work<br />
| title = Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory<br />
| date = 2008<br />
| authors = [[Huan Wang]]<br />[[Xing Jiang]]<br />[[Liang-Tien Chia]]<br />[[Ah-Hwee Tan]]<br />
| doi = 10.1145/1460096.1460128<br />
| link = https://dl.acm.org/citation.cfm?id=1460096.1460128<br />
}}<br />
'''Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Huan Wang]], [[Xing Jiang]], [[Liang-Tien Chia]] and [[Ah-Hwee Tan]].<br />
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== Overview ==<br />
Ontology, as an effective approach to bridge the semantic gap in various domains, has attracted a lot of interests from multimedia researchers. Among the numerous possibilities enabled by [[ontology]], authors are particularly interested in exploiting ontology for a better understanding of media task (particularly, images) on the World Wide Web. To achieve goal, two open issues are inevitably involved: 1) How to avoid the tedious manual work for ontology construction? 2) What are the effective inference models when using an ontology? Recent works[11, 16] about ontology learned from [[Wikipedia]] has been reported in conferences targeting the areas of knowledge management and artificial intelligent. There are also reports of different inference models being investigated [5, 13, 15]. However, so far there has not been any comprehensive solution. In this paper, authors look at these challenges and attempt to provide a general solution to both questions. Through a careful analysis of the online encyclopedia Wikipedia's categorization and page content, authors choose it as knowledge source and propose an automatic ontology construction approach. Authors prove that it is a viable way to build ontology under various domains. To address the inference model issue, authors provide a novel understanding of the ontology and consider it as a type of semantic network, which is similar to brain models in the cognitive research field. Spreading Activation Techniques, which have been proved to be a correct information processing model in the semantic network, are consequently introduced for inference. Authors have implemented a prototype system with the developed solutions for web image retrieval. By comprehensive experiments on the canine category of the animal kingdom, authors show that this is a scalable architecture for proposed methods.<br />
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Wang, Huan; Jiang, Xing; Chia, Liang-Tien; Tan, Ah-Hwee. (2008). "[[Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory]]".DOI: 10.1145/1460096.1460128. <br />
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{{cite journal |last1=Wang |first1=Huan |last2=Jiang |first2=Xing |last3=Chia |first3=Liang-Tien |last4=Tan |first4=Ah-Hwee |title=Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory |date=2008 |doi=10.1145/1460096.1460128 |url=https://wikipediaquality.com/wiki/Ontology_Enhanced_Web_Image_Retrieval:_Aided_by_Wikipedia_&_Spreading_Activation_Theory}}<br />
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Wang, Huan; Jiang, Xing; Chia, Liang-Tien; Tan, Ah-Hwee. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/Ontology_Enhanced_Web_Image_Retrieval:_Aided_by_Wikipedia_&_Spreading_Activation_Theory">Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory</a>&amp;quot;.DOI: 10.1145/1460096.1460128. <br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=Qualifying_Articles_of_Persian_Wikipedia_Encyclopedia_Through_J48_Algorithm,_Anfis_and_Subtractive_Clustering&diff=24473Qualifying Articles of Persian Wikipedia Encyclopedia Through J48 Algorithm, Anfis and Subtractive Clustering2020-06-06T06:34:11Z<p>Aubree: + embed code</p>
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<div>{{Infobox work<br />
| title = Qualifying Articles of Persian Wikipedia Encyclopedia Through J48 Algorithm, Anfis and Subtractive Clustering<br />
| date = 2015<br />
| authors = [[Seyedtaha Seyedsadr]]<br />[[Mohammadali Afsharkazemi]]<br />[[Hashem Nikoomaram]]<br />
| doi = 10.11648/j.acis.20150306.18<br />
| link = http://article.sciencepublishinggroup.com/html/10.11648.j.acis.20150306.18.html<br />
}}<br />
'''Qualifying Articles of Persian Wikipedia Encyclopedia Through J48 Algorithm, Anfis and Subtractive Clustering''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Seyedtaha Seyedsadr]], [[Mohammadali Afsharkazemi]] and [[Hashem Nikoomaram]].<br />
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== Overview ==<br />
Since [[Wikipedia]] encyclopedia is one of the most popular web sites on the internet, providing accurate information is of abundant importance. In this research, the effective variables on quality of Persian articles are identified and a system is, then, designed for judging articles in three quality levels: high quality, cleanup needed, and deletion. First, the variables relating to the articles included in the list of [[featured articles]], good articles, cleanup needed, and deletion articles are collected. Then, two methods are used for the analysis of data: First, a decision tree explains the relationships among the collected variables as rules that are implemented by adaptive neuro fuzzy interference system. Second, the data are implemented by subtractive clustering algorithm and the error of both methods is, finally, measured and compared. The results indicate that the average daily hits, total views, page length, total number of edits, total number of authors, and number of templates used are directly related to quality of Persian articles while the number of recent number of authors is inversely related to quality of articles.<br />
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Seyedsadr, Seyedtaha; Afsharkazemi, Mohammadali; Nikoomaram, Hashem. (2015). "[[Qualifying Articles of Persian Wikipedia Encyclopedia Through J48 Algorithm, Anfis and Subtractive Clustering]]". Science Publishing Group. DOI: 10.11648/j.acis.20150306.18. <br />
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{{cite journal |last1=Seyedsadr |first1=Seyedtaha |last2=Afsharkazemi |first2=Mohammadali |last3=Nikoomaram |first3=Hashem |title=Qualifying Articles of Persian Wikipedia Encyclopedia Through J48 Algorithm, Anfis and Subtractive Clustering |date=2015 |doi=10.11648/j.acis.20150306.18 |url=https://wikipediaquality.com/wiki/Qualifying_Articles_of_Persian_Wikipedia_Encyclopedia_Through_J48_Algorithm,_Anfis_and_Subtractive_Clustering |journal=Science Publishing Group}}<br />
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Seyedsadr, Seyedtaha; Afsharkazemi, Mohammadali; Nikoomaram, Hashem. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Qualifying_Articles_of_Persian_Wikipedia_Encyclopedia_Through_J48_Algorithm,_Anfis_and_Subtractive_Clustering">Qualifying Articles of Persian Wikipedia Encyclopedia Through J48 Algorithm, Anfis and Subtractive Clustering</a>&amp;quot;. Science Publishing Group. DOI: 10.11648/j.acis.20150306.18. <br />
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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=Readability_of_Wikipedia&diff=24472Readability of Wikipedia2020-06-06T06:31:21Z<p>Aubree: Embed</p>
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<div>{{Infobox work<br />
| title = Readability of Wikipedia<br />
| date = 2012<br />
| authors = [[T. Lucassen]]<br />[[Roald Dijkstra]]<br />[[Johannes Martinus Cornelis Schraagen]]<br />
| doi = 10.5210/fm.v0i0.3916<br />
| link = http://journals.uic.edu/ojs/index.php/fm/article/view/3916/3297<br />
}}<br />
'''Readability of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[T. Lucassen]], [[Roald Dijkstra]] and [[Johannes Martinus Cornelis Schraagen]].<br />
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== Overview ==<br />
Wikipedia is becoming widely acknowledged as a reliable source of encyclopedic information. However, concerns have been expressed about its [[readability]]. [[Wikipedia]] articles might be written in a language too difficult to be understood by most of its visitors. In this study, authors apply the Flesch reading ease test to all available articles from the [[English Wikipedia]] to investigate these concerns. The results show that overall readability is poor, with 75 percent of all articles scoring below the desired readability score. The ‘Simple English’ Wikipedia scores better, but its readability is still insufficient for its target audience. A demo of methodology is available at www.readabilityofwikipedia.com .<br />
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Lucassen, T.; Dijkstra, Roald; Schraagen, Johannes Martinus Cornelis. (2012). "[[Readability of Wikipedia]]". First Monday. DOI: 10.5210/fm.v0i0.3916. <br />
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{{cite journal |last1=Lucassen |first1=T. |last2=Dijkstra |first2=Roald |last3=Schraagen |first3=Johannes Martinus Cornelis |title=Readability of Wikipedia |date=2012 |doi=10.5210/fm.v0i0.3916 |url=https://wikipediaquality.com/wiki/Readability_of_Wikipedia |journal=First Monday}}<br />
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Lucassen, T.; Dijkstra, Roald; Schraagen, Johannes Martinus Cornelis. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Readability_of_Wikipedia">Readability of Wikipedia</a>&amp;quot;. First Monday. DOI: 10.5210/fm.v0i0.3916. <br />
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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=Wikipedia_Article_Content_based_Query_Expansion_in_Ir4Qa_System&diff=24471Wikipedia Article Content based Query Expansion in Ir4Qa System2020-06-06T06:29:51Z<p>Aubree: Links</p>
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<div>'''Wikipedia Article Content based Query Expansion in Ir4Qa System''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Maofu Liu]], [[Bin Zhou]], [[Liwen Qi]] and [[Zilou Zhang]].<br />
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== Overview ==<br />
This paper describes the work of WUST group in NTCIR-8 on the subtask of English to Simplified Chinese and Simplified Chinese to Simplified Chinese [[information retrieval]] for [[question answering]] (EN-CS and CS-CS IR4QA). In order to enhance the precision and efficiency in question analysis, authors employ a special question analysis method extracting more appropriate key terms and apply the query expansion technique gaining more relevant key terms based on [[Wikipedia]] article content related to the query.</div>Aubreehttps://wikipediaquality.com/index.php?title=Wikipedia_World_Map:_Method_and_Application_of_Map-Like_Wiki_Visualization&diff=24470Wikipedia World Map: Method and Application of Map-Like Wiki Visualization2020-06-06T06:28:40Z<p>Aubree: + infobox</p>
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<div>{{Infobox work<br />
| title = Wikipedia World Map: Method and Application of Map-Like Wiki Visualization<br />
| date = 2011<br />
| authors = [[Cheong-Iao Pang]]<br />[[Robert P. Biuk-Aghai]]<br />
| doi = 10.1145/2038558.2038579<br />
| link = http://dl.acm.org/citation.cfm?doid=2038558.2038579<br />
}}<br />
'''Wikipedia World Map: Method and Application of Map-Like Wiki Visualization''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Cheong-Iao Pang]] and [[Robert P. Biuk-Aghai]].<br />
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== Overview ==<br />
Wiki are popular platforms for collaborative editing. In volunteer-driven wikis such as [[Wikipedia]], which attracts millions of authors editing articles on a diverse range of topics, contributors' editing activity results in certain semantic coverage of topic areas. Obtaining an understanding of a given wiki's semantic coverage is not easy. To solve this problem, authors have devised a method for visualizing a wiki in a way similar to a geographic map. Authors have applied method to Wikipedia, and generated visualizations for several Wikipedia language editions. This paper presents wiki visualization method and its application.</div>Aubreehttps://wikipediaquality.com/index.php?title=%27Wikipedia,_the_Free_Encyclopedia%27_as_a_Role_Model%3F_Lessons_for_Open_Innovation_from_an_Exploratory_Examination_of_the_Supposedly_Democratic-Anarchic_Nature_of_Wikipedia&diff=24469'Wikipedia, the Free Encyclopedia' as a Role Model? Lessons for Open Innovation from an Exploratory Examination of the Supposedly Democratic-Anarchic Nature of Wikipedia2020-06-06T06:26:08Z<p>Aubree: + categories</p>
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<div>{{Infobox work<br />
| title = 'Wikipedia, the Free Encyclopedia' as a Role Model? Lessons for Open Innovation from an Exploratory Examination of the Supposedly Democratic-Anarchic Nature of Wikipedia<br />
| date = 2010<br />
| authors = [[Gordon Müller-Seitz]]<br />[[Guido Reger]]<br />
| doi = 10.1504/IJTM.2010.035985<br />
| link = https://www.inderscienceonline.com/doi/abs/10.1504/IJTM.2010.035985<br />
}}<br />
''''Wikipedia, the Free Encyclopedia' as a Role Model? Lessons for Open Innovation from an Exploratory Examination of the Supposedly Democratic-Anarchic Nature of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Gordon Müller-Seitz]] and [[Guido Reger]].<br />
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== Overview ==<br />
Accounts of [[open source]] software (OSS) development projects frequently stress their democratic, sometimes even anarchic nature, in contrast to for-profit organisations. Given this observation, research evaluates qualitative data from [[Wikipedia]], a free online encyclopaedia whose development mechanism allegedly resembles that of OSS projects. Authors research offers contributions to the field of open innovation research with three major findings. First, authors shed light on Wikipedia as a phenomenon that has received scant attention from management scholars to date. Second, authors show that OSS-related motivational mechanisms partially apply to Wikipedia participants. Third, exploration of Wikipedia also reveals that its organisational mechanisms are often perceived as bureaucratic by contributors. This finding was unexpected since this type of problem is often associated with for-profit organisations. Such a situation risks attenuating the motivation of contributors and sheds a critical light on the nature of ...<br />
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Müller-Seitz, Gordon; Reger, Guido. (2010). "[['Wikipedia, the Free Encyclopedia' as a Role Model? Lessons for Open Innovation from an Exploratory Examination of the Supposedly Democratic-Anarchic Nature of Wikipedia]]". Inderscience Publishers Ltd. DOI: 10.1504/IJTM.2010.035985. <br />
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{{cite journal |last1=Müller-Seitz |first1=Gordon |last2=Reger |first2=Guido |title='Wikipedia, the Free Encyclopedia' as a Role Model? Lessons for Open Innovation from an Exploratory Examination of the Supposedly Democratic-Anarchic Nature of Wikipedia |date=2010 |doi=10.1504/IJTM.2010.035985 |url=https://wikipediaquality.com/wiki/'Wikipedia,_the_Free_Encyclopedia'_as_a_Role_Model?_Lessons_for_Open_Innovation_from_an_Exploratory_Examination_of_the_Supposedly_Democratic-Anarchic_Nature_of_Wikipedia |journal=Inderscience Publishers Ltd}}<br />
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Müller-Seitz, Gordon; Reger, Guido. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/'Wikipedia,_the_Free_Encyclopedia'_as_a_Role_Model?_Lessons_for_Open_Innovation_from_an_Exploratory_Examination_of_the_Supposedly_Democratic-Anarchic_Nature_of_Wikipedia">'Wikipedia, the Free Encyclopedia' as a Role Model? Lessons for Open Innovation from an Exploratory Examination of the Supposedly Democratic-Anarchic Nature of Wikipedia</a>&amp;quot;. Inderscience Publishers Ltd. DOI: 10.1504/IJTM.2010.035985. <br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=Visualizing_Article_Similarities_in_Wikipedia&diff=24468Visualizing Article Similarities in Wikipedia2020-06-06T06:24:51Z<p>Aubree: Adding categories</p>
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<div>{{Infobox work<br />
| title = Visualizing Article Similarities in Wikipedia<br />
| date = 2016<br />
| authors = [[Patrick Riehmann]]<br />[[Martin Potthast]]<br />[[Henning Gruendl]]<br />[[Johannes Kiesel]]<br />[[Dean Jürges]]<br />[[Giuliano Castiglia]]<br />[[Bagrat Ter-Akopyan]]<br />[[Bernd Froehlich]]<br />
| doi = 10.2312/eurp.20161144<br />
| link = https://diglib.eg.org:443/handle/10.2312/eurp20161144<br />
}}<br />
'''Visualizing Article Similarities in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Patrick Riehmann]], [[Martin Potthast]], [[Henning Gruendl]], [[Johannes Kiesel]], [[Dean Jürges]], [[Giuliano Castiglia]], [[Bagrat Ter-Akopyan]] and [[Bernd Froehlich]].<br />
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== Overview ==<br />
In this poster authors present intermediate results regarding visual text analytics on [[Wikipedia]]. Authors implemented a visualization providing insight about similarities among Wikipedia articles in terms of structure as well as content. The presented data was gathered and processed via a pairwise comparison of all Wikipedia articles. Comparisons were appropriately pruned due to time and memory reasons when providing in-memory database with the computed similarity values for visualization.<br />
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Riehmann, Patrick; Potthast, Martin; Gruendl, Henning; Kiesel, Johannes; Jürges, Dean; Castiglia, Giuliano; Ter-Akopyan, Bagrat; Froehlich, Bernd. (2016). "[[Visualizing Article Similarities in Wikipedia]]". The Eurographics Association. DOI: 10.2312/eurp.20161144. <br />
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{{cite journal |last1=Riehmann |first1=Patrick |last2=Potthast |first2=Martin |last3=Gruendl |first3=Henning |last4=Kiesel |first4=Johannes |last5=Jürges |first5=Dean |last6=Castiglia |first6=Giuliano |last7=Ter-Akopyan |first7=Bagrat |last8=Froehlich |first8=Bernd |title=Visualizing Article Similarities in Wikipedia |date=2016 |doi=10.2312/eurp.20161144 |url=https://wikipediaquality.com/wiki/Visualizing_Article_Similarities_in_Wikipedia |journal=The Eurographics Association}}<br />
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Riehmann, Patrick; Potthast, Martin; Gruendl, Henning; Kiesel, Johannes; Jürges, Dean; Castiglia, Giuliano; Ter-Akopyan, Bagrat; Froehlich, Bernd. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Visualizing_Article_Similarities_in_Wikipedia">Visualizing Article Similarities in Wikipedia</a>&amp;quot;. The Eurographics Association. DOI: 10.2312/eurp.20161144. <br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=An_Efficient_Incentive_Compatible_Mechanism_to_Motivate_Wikipedia_Contributors&diff=22862An Efficient Incentive Compatible Mechanism to Motivate Wikipedia Contributors2019-12-15T08:49:14Z<p>Aubree: + links</p>
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<div>'''An Efficient Incentive Compatible Mechanism to Motivate Wikipedia Contributors''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Mane Pramod]], [[Sajal Mukhopadhyay]] and [[D. Gosh]].<br />
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== Overview ==<br />
Wikipedia is the world’s largest collaboratively edited source of encyclopedic information repository consisting almost 1.5 million articles and more than 90,000 contributors. Although, since its inception on 2001, the numbers of contributors were huge, A study made in 2009 found that members (contributors) may initially contribute to site for pleasure or being motivated by an internal drive to share his knowledge. But latter they are not motivated to edit the related articles so that quality of the articles could be improved [1] [5].In paper authors address above problem in economics perspective. Here authors propose a novel scheme to motivate the contributors of [[Wikipedia]] with the mechanism design theory that is the most emerging tool at present to address the situation when data is privately held with the agents.</div>Aubreehttps://wikipediaquality.com/index.php?title=Rgu_at_Imageclef2010_Wikipedia_Retrieval_Task&diff=22861Rgu at Imageclef2010 Wikipedia Retrieval Task2019-12-15T08:47:41Z<p>Aubree: + embed code</p>
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<div>{{Infobox work<br />
| title = Rgu at Imageclef2010 Wikipedia Retrieval Task<br />
| date = 2010<br />
| authors = [[Jun Wang]]<br />[[Dawei Song]]<br />[[Leszek Kaliciak]]<br />
| link = http://ceur-ws.org/Vol-1176/CLEF2010wn-ImageCLEF-WangEt2010.pdf<br />
| plink = https://www.semanticscholar.org/paper/RGU-at-ImageCLEF2010-Wikipedia-Retrieval-Task-Wang-Song/041f0078f5b9a10918e0a0e0f1fc2ae1e780347f/figure/0<br />
}}<br />
'''Rgu at Imageclef2010 Wikipedia Retrieval Task''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Jun Wang]], [[Dawei Song]] and [[Leszek Kaliciak]].<br />
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== Overview ==<br />
This working notes paper describes first participation in the ImageCLEF2010 [[Wikipedia]] Retrieval Task. In this task, authors mainly test Quantum Theory inspired retrieval function on cross media retrieval. Instead of heuristically combining the ranking scores independently from different media types, authors develop a tensor product based model to represent textual and visual content [[features]] of an image as a non-separable composite system. Such system incorporates the statistical/semantic dependencies between certain features. Then the ranking scores of the images are computed in a way as quantum measurement does. Meanwhile, authors also test a new local feature that authors have developed for content based image retrieval.<br />
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{{cite journal |last1=Wang |first1=Jun |last2=Song |first2=Dawei |last3=Kaliciak |first3=Leszek |title=Rgu at Imageclef2010 Wikipedia Retrieval Task |date=2010 |url=https://wikipediaquality.com/wiki/Rgu_at_Imageclef2010_Wikipedia_Retrieval_Task}}<br />
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Wang, Jun; Song, Dawei; Kaliciak, Leszek. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Rgu_at_Imageclef2010_Wikipedia_Retrieval_Task">Rgu at Imageclef2010 Wikipedia Retrieval Task</a>&amp;quot;.<br />
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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=Omnipedia_Brings_Together_25_Different_Languages_of_Wikipedia_Entries&diff=22860Omnipedia Brings Together 25 Different Languages of Wikipedia Entries2019-12-15T08:46:35Z<p>Aubree: wikilinks</p>
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<div>'''Omnipedia Brings Together 25 Different Languages of Wikipedia Entries''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Jacob Aron]].<br />
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== Overview ==<br />
By melding entries from different [[language versions]], researchers hope their system will give users access to a range of different cultural perspectives</div>Aubreehttps://wikipediaquality.com/index.php?title=Don%27T_Bite_the_Newbies:_How_Reverts_Affect_the_Quantity_and_Quality_of_Wikipedia_Work&diff=22859Don'T Bite the Newbies: How Reverts Affect the Quantity and Quality of Wikipedia Work2019-12-15T08:45:18Z<p>Aubree: + embed code</p>
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<div>{{Infobox work<br />
| title = Don'T Bite the Newbies: How Reverts Affect the Quantity and Quality of Wikipedia Work<br />
| date = 2011<br />
| authors = [[Aaron Halfaker]]<br />[[Aniket Kittur]]<br />[[John Riedl]]<br />
| doi = 10.1145/2038558.2038585<br />
| link = http://dl.acm.org/citation.cfm?doid=2038558.2038585<br />
}}<br />
'''Don'T Bite the Newbies: How Reverts Affect the Quantity and Quality of Wikipedia Work''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Aaron Halfaker]], [[Aniket Kittur]] and [[John Riedl]].<br />
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== Overview ==<br />
Reverts are important to maintaining the quality of [[Wikipedia]]. They fix mistakes, repair vandalism, and help enforce policy. However, reverts can also be damaging, especially to the aspiring editor whose work they destroy. In this research authors analyze 400,000 Wikipedia revisions to understand the effect that reverts had on editors. Authors seek to understand the extent to which they demotivate users, reducing the workforce of contributors, versus the extent to which they help users improve as encyclopedia editors. Overall authors find that reverts are powerfully demotivating, but that their net influence is that more quality work is done in Wikipedia as a result of reverts than is lost by chasing editors away. However, authors identify key conditions -- most specifically new editors being reverted by much more experienced editors - under which reverts are particularly damaging. Authors propose that reducing the damage from reverts might be one effective path for Wikipedia to solve the newcomer retention problem.<br />
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Halfaker, Aaron; Kittur, Aniket; Riedl, John. (2011). "[[Don'T Bite the Newbies: How Reverts Affect the Quantity and Quality of Wikipedia Work]]".DOI: 10.1145/2038558.2038585. <br />
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{{cite journal |last1=Halfaker |first1=Aaron |last2=Kittur |first2=Aniket |last3=Riedl |first3=John |title=Don'T Bite the Newbies: How Reverts Affect the Quantity and Quality of Wikipedia Work |date=2011 |doi=10.1145/2038558.2038585 |url=https://wikipediaquality.com/wiki/Don'T_Bite_the_Newbies:_How_Reverts_Affect_the_Quantity_and_Quality_of_Wikipedia_Work}}<br />
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Halfaker, Aaron; Kittur, Aniket; Riedl, John. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Don'T_Bite_the_Newbies:_How_Reverts_Affect_the_Quantity_and_Quality_of_Wikipedia_Work">Don'T Bite the Newbies: How Reverts Affect the Quantity and Quality of Wikipedia Work</a>&amp;quot;.DOI: 10.1145/2038558.2038585. <br />
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<div>{{Infobox work<br />
| title = Examining Wikipedia with a Broader Lens: Quantifying the Value of Wikipedia's Relationships with Other Large-Scale Online Communities<br />
| date = 2018<br />
| authors = [[Nicholas Vincent]]<br />[[Isaac L. Johnson]]<br />[[Brent J. Hecht]]<br />
| doi = 10.1145/3173574.3174140<br />
| link = https://dl.acm.org/citation.cfm?id=3174140<br />
}}<br />
'''Examining Wikipedia with a Broader Lens: Quantifying the Value of Wikipedia's Relationships with Other Large-Scale Online Communities''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Nicholas Vincent]], [[Isaac L. Johnson]] and [[Brent J. Hecht]].<br />
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== Overview ==<br />
The extensive [[Wikipedia]] literature has largely considered Wikipedia in isolation, outside of the context of its broader Internet ecosystem. Very recent research has demonstrated the significance of this limitation, identifying critical relationships between [[Google]] and Wikipedia that are highly relevant to many areas of Wikipedia-based research and practice. This paper extends this recent research beyond search engines to examine Wikipedia's relationships with large-scale online communities, [[Stack Overflow]] and [[Reddit]] in particular. Authors find evidence of consequential, albeit unidirectional relationships. Wikipedia provides substantial value to both communities, with Wikipedia content increasing visitation, engagement, and revenue, but authors find little evidence that these websites contribute to Wikipedia in return. Overall, these findings highlight important connections between Wikipedia and its broader ecosystem that should be considered by researchers studying Wikipedia. Critically, results also emphasize the key role that volunteer-created Wikipedia content plays in improving other websites, even contributing to revenue generation.<br />
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Vincent, Nicholas; Johnson, Isaac L.; Hecht, Brent J.. (2018). "[[Examining Wikipedia with a Broader Lens: Quantifying the Value of Wikipedia's Relationships with Other Large-Scale Online Communities]]". ACM Press. DOI: 10.1145/3173574.3174140. <br />
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{{cite journal |last1=Vincent |first1=Nicholas |last2=Johnson |first2=Isaac L. |last3=Hecht |first3=Brent J. |title=Examining Wikipedia with a Broader Lens: Quantifying the Value of Wikipedia's Relationships with Other Large-Scale Online Communities |date=2018 |doi=10.1145/3173574.3174140 |url=https://wikipediaquality.com/wiki/Examining_Wikipedia_with_a_Broader_Lens:_Quantifying_the_Value_of_Wikipedia's_Relationships_with_Other_Large-Scale_Online_Communities |journal=ACM Press}}<br />
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Vincent, Nicholas; Johnson, Isaac L.; Hecht, Brent J.. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Examining_Wikipedia_with_a_Broader_Lens:_Quantifying_the_Value_of_Wikipedia's_Relationships_with_Other_Large-Scale_Online_Communities">Examining Wikipedia with a Broader Lens: Quantifying the Value of Wikipedia's Relationships with Other Large-Scale Online Communities</a>&amp;quot;. ACM Press. DOI: 10.1145/3173574.3174140. <br />
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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=Iscb_Computational_Biology_Wikipedia_Competition&diff=22857Iscb Computational Biology Wikipedia Competition2019-12-15T08:42:05Z<p>Aubree: Wikilinks</p>
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<div>'''Iscb Computational Biology Wikipedia Competition''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Alex Bateman]], [[Janet Kelso]], [[Daniel Mietchen]], [[Geoff Macintyre]], [[Tomás Di Domenico]], [[Thomas Abeel]], [[Thomas Abeel]], [[Darren W. Logan]], [[Predrag Radivojac]] and [[Burkhard Rost]].<br />
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== Overview ==<br />
The International Society for Computational Biology is pleased to announce the 2013 ISCB Computational Biology [[Wikipedia]] competition. The competition, in which entrants create or improve the content of any Wikipedia article in the field of computational biology, is open to all students and trainees. Further information about the competition can be found here: http://en.wikipedia.org/wiki/Wikipedia:WikiProject_Computational_Biology/ISCB_competition_announcement_2013</div>Aubreehttps://wikipediaquality.com/index.php?title=Beyond_the_Book:_Linking_Books_to_Wikipedia&diff=22856Beyond the Book: Linking Books to Wikipedia2019-12-15T08:40:35Z<p>Aubree: infobox</p>
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<div>{{Infobox work<br />
| title = Beyond the Book: Linking Books to Wikipedia<br />
| date = 2015<br />
| authors = [[Carlos Martinez-Ortiz]]<br />[[Marijn Koolen]]<br />[[Floor Buschenhenke]]<br />[[Karina van Dalen-Oskam]]<br />
| doi = 10.1109/eScience.2015.12<br />
| link = https://dl.acm.org/citation.cfm?id=2860688<br />
}}<br />
'''Beyond the Book: Linking Books to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Carlos Martinez-Ortiz]], [[Marijn Koolen]], [[Floor Buschenhenke]] and [[Karina van Dalen-Oskam]].<br />
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== Overview ==<br />
The book translation market is a topic of interest in literary studies, but the reasons why a book is selected for translation are not well understood. The "Beyond the Book" project investigates whether web resources like [[Wikipedia]] can be used to establish the level of cultural bias. This work describes the eScience tools used to estimate the cultural appeal of a book: semantic linking is used to identify key words in the text of the book, and afterwards the revision information from corresponding Wikipedia articles is examined to identify countries that generated a more than average amount of contributions to those articles. Comparison between the number of contributions from two countries on the same set of articles may show with which knowledge the contributors are familiar. Authors assume a lack of contributions from a country may indicate a gap in the knowledge of readers from that country. Authors assume that a book dealing with that concept could be more exotic and therefore more appealing for certain readers, while others are therefore less interested in the book. An indication of the 'level of exoticness' thus could help a reader/publisher to decide to read/translate the book or not. Experimental results are presented for four selected books from a set of 564 books written in Dutch or translated into Dutch, assessing their potential appeal for a Canadian audience. A qualitative assessment of quantitative results provides insight into [[named entities]] that may indicate a high/low cultural bias towards a book.</div>Aubreehttps://wikipediaquality.com/index.php?title=Classification_of_Comments_by_Tree_Kernels_Using_the_Hierarchy_of_Wikipedia_for_Tree_Structures&diff=22855Classification of Comments by Tree Kernels Using the Hierarchy of Wikipedia for Tree Structures2019-12-15T08:39:27Z<p>Aubree: Embed</p>
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| title = Classification of Comments by Tree Kernels Using the Hierarchy of Wikipedia for Tree Structures<br />
| date = 2016<br />
| authors = [[Masahiro Takeda]]<br />[[Nobuyuki Kobayashi]]<br />[[Fumio Kitagawa]]<br />[[Hiromitsu Shiina]]<br />
| doi = 10.1109/IIAI-AAI.2016.62<br />
| link = <br />
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'''Classification of Comments by Tree Kernels Using the Hierarchy of Wikipedia for Tree Structures''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Masahiro Takeda]], [[Nobuyuki Kobayashi]], [[Fumio Kitagawa]] and [[Hiromitsu Shiina]].<br />
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== Overview ==<br />
Many web services posting short comments such as product reviews and [[Twitter]] have been provided. Authors consider that automatic and accurate text classification may lead to develop new web services and system. In the past, the frequency of appearance of words by bag-of-words have been often used for text classification as a basic technique. In contrast, authors propose a technique to classify tweets using tree kernels created by the [[categories]] of [[Wikipedia]] in this study. In addition, authors developed a retrieval system for tourism videos by applying the technique to tweets related to tourism.<br />
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Takeda, Masahiro; Kobayashi, Nobuyuki; Kitagawa, Fumio; Shiina, Hiromitsu. (2016). "[[Classification of Comments by Tree Kernels Using the Hierarchy of Wikipedia for Tree Structures]]".DOI: 10.1109/IIAI-AAI.2016.62. <br />
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{{cite journal |last1=Takeda |first1=Masahiro |last2=Kobayashi |first2=Nobuyuki |last3=Kitagawa |first3=Fumio |last4=Shiina |first4=Hiromitsu |title=Classification of Comments by Tree Kernels Using the Hierarchy of Wikipedia for Tree Structures |date=2016 |doi=10.1109/IIAI-AAI.2016.62 |url=https://wikipediaquality.com/wiki/Classification_of_Comments_by_Tree_Kernels_Using_the_Hierarchy_of_Wikipedia_for_Tree_Structures}}<br />
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Takeda, Masahiro; Kobayashi, Nobuyuki; Kitagawa, Fumio; Shiina, Hiromitsu. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Classification_of_Comments_by_Tree_Kernels_Using_the_Hierarchy_of_Wikipedia_for_Tree_Structures">Classification of Comments by Tree Kernels Using the Hierarchy of Wikipedia for Tree Structures</a>&amp;quot;.DOI: 10.1109/IIAI-AAI.2016.62. <br />
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<div>'''In Search of the Ur-Wikipedia: Universality, Similarity, and Translation in the Wikipedia Inter-Language Link Network''' - scientific work related to Wikipedia quality published in 2012, written by Morten Warncke-Wang, Anuradha Uduwage, Zhenhua Dong and John Riedl.<br />
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== Overview ==<br />
Wikipedia has become one of the primary encyclopaedic information repositories on the World Wide Web. It started in 2001 with a single edition in the English language and has since expanded to more than 20 million articles in 283 languages. Criss-crossing between the Wikipedias is an inter-language link network, connecting the articles of one edition of Wikipedia to another. Authors describe characteristics of articles covered by nearly all Wikipedias and those covered by only a single language edition, authors use the network to understand how authors can judge the similarity between Wikipedias based on concept coverage, and authors investigate the flow of translation between a selection of the larger Wikipedias. Authors findings indicate that the relationships between Wikipedia editions follow Tobler's first law of geography: similarity decreases with increasing distance. The number of articles in a Wikipedia edition is found to be the strongest predictor of similarity, while language similarity also appears to have an influence. The English Wikipedia edition is by far the primary source of translations. Authors discuss the impact of these results for Wikipedia as well as user-generated content communities in general.</div>Aubreehttps://wikipediaquality.com/index.php?title=A_Model_for_Ranking_Entities_and_Its_Application_to_Wikipedia&diff=22853A Model for Ranking Entities and Its Application to Wikipedia2019-12-15T08:35:30Z<p>Aubree: Infobox</p>
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<div>{{Infobox work<br />
| title = A Model for Ranking Entities and Its Application to Wikipedia<br />
| date = 2008<br />
| authors = [[Gianluca Demartini]]<br />[[Claudiu S. Firan]]<br />[[Tereza Iofciu]]<br />[[Ralf Krestel]]<br />[[Wolfgang Nejdl]]<br />
| doi = 10.1109/LA-WEB.2008.8<br />
| link = https://dl.acm.org/citation.cfm?id=1510532.1511722<br />
}}<br />
'''A Model for Ranking Entities and Its Application to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Gianluca Demartini]], [[Claudiu S. Firan]], [[Tereza Iofciu]], [[Ralf Krestel]] and [[Wolfgang Nejdl]].<br />
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== Overview ==<br />
Entity Ranking (ER) is a recently emerging search task in Information Retrieval, where the goal is not finding documents matching the query words, but instead finding entities which match types and attributes mentioned in the query. In this paper authors propose a formal model to define entities as well as a complete ER system, providing examples of its application to enterprise, Web, and [[Wikipedia]] scenarios. Since searching for entities on Web scale repositories is an open challenge as the effectiveness of ranking is usually not satisfactory, authors present a set of algorithms based on model and evaluate their retrieval effectiveness. The results show that combining simple Link Analysis, [[Natural Language Processing]], and Named Entity Recognition methods improves retrieval performance of entity search by over 53% for P@10 and 35% for MAP.</div>Aubreehttps://wikipediaquality.com/index.php?title=Libguides._What_About_Wikipedia%3F._About&diff=22852Libguides. What About Wikipedia?. About2019-12-15T08:33:59Z<p>Aubree: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Libguides. What About Wikipedia?. About<br />
| date = 2011<br />
| authors = [[Maria de Jesus Ayala-Schueneman]]<br />
| link = http://libguides.tamuk.edu/content.php?pid=164882&amp;sid=1390914<br />
}}<br />
'''Libguides. What About Wikipedia?. About''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Maria de Jesus Ayala-Schueneman]].<br />
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== Overview ==<br />
What is [[Wikipedia]] and why can't Author cite it? This guide discusses the strengths and weaknesses of Wikipedia articles and how Academia can improve the accuracy of this source.</div>Aubreehttps://wikipediaquality.com/index.php?title=Extraction_of_Historical_Events_from_Wikipedia&diff=22851Extraction of Historical Events from Wikipedia2019-12-15T08:31:35Z<p>Aubree: Adding categories</p>
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<div>{{Infobox work<br />
| title = Extraction of Historical Events from Wikipedia<br />
| date = 2012<br />
| authors = [[Daniel Hienert]]<br />[[Francesco Luciano]]<br />
| doi = 10.1007/978-3-662-46641-4_2<br />
| link = https://link.springer.com/chapter/10.1007/978-3-662-46641-4_2/fulltext.html<br />
| plink = http://arxiv.org/pdf/1205.4138.pdf<br />
}}<br />
'''Extraction of Historical Events from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Daniel Hienert]] and [[Francesco Luciano]].<br />
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== Overview ==<br />
The [[DBpedia]] project extracts [[structured information]] from [[Wikipedia]] and makes it available on the web. Information is gathered mainly with the help of [[infoboxes]] that contain structured information of the Wikipedia article. A lot of information is only contained in the article body and is not yet included in DBpedia. In this paper authors focus on the extraction of historical events from Wikipedia articles that are available for about 2,500 years for [[different language]]s. Authors have extracted about 121,000 events with more than 325,000 links to DBpedia entities and provide access to this data via a Web API, SPARQL endpoint, Linked Data Interface and in a timeline application.<br />
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Hienert, Daniel; Luciano, Francesco. (2012). "[[Extraction of Historical Events from Wikipedia]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-662-46641-4_2. <br />
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{{cite journal |last1=Hienert |first1=Daniel |last2=Luciano |first2=Francesco |title=Extraction of Historical Events from Wikipedia |date=2012 |doi=10.1007/978-3-662-46641-4_2 |url=https://wikipediaquality.com/wiki/Extraction_of_Historical_Events_from_Wikipedia |journal=Springer, Berlin, Heidelberg}}<br />
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Hienert, Daniel; Luciano, Francesco. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extraction_of_Historical_Events_from_Wikipedia">Extraction of Historical Events from Wikipedia</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-662-46641-4_2. <br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=Beyond_Wikipedia:_How_Good_a_Reference_Source_are_Medical_Wikis%3F&diff=22850Beyond Wikipedia: How Good a Reference Source are Medical Wikis?2019-12-15T08:30:20Z<p>Aubree: + categories</p>
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<div>{{Infobox work<br />
| title = Beyond Wikipedia: How Good a Reference Source are Medical Wikis?<br />
| date = 2010<br />
| authors = [[Paula Younger]]<br />
| doi = 10.1108/09504121011019899<br />
| link = http://www.emeraldinsight.com/doi/pdf/10.1108/09504121011019899<br />
}}<br />
'''Beyond Wikipedia: How Good a Reference Source are Medical Wikis?''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Paula Younger]].<br />
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== Overview ==<br />
Purpose – – The purpose of this paper is to examine the case for using subject (medical) wikis as a reference tool.Design/methodology/approach – The paper summarises content of ganfyd and WikiMD, comparing their ethos and approach to information. It describes some other medical and health wikis in brief.Findings – As their audience is somewhat more specialised, medical wikis, currently in their infancy, cover topics in more depth than [[Wikipedia]] but coverage remains patchy. They may be of particular use for those without access to expensive resources such as UpToDate requiring a short literature review or overview of a topic. Wikis at present are best used as a signpost to other resources with tighter editorial control.Research limitations/implications – The assessment of the subject wikis is brief and the analysis of wikis as a reference tool is largely drawn from general literature, not medical.Practical implications – This assessment provides exposure of subject wikis as a potential reference tool.Origi...<br />
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Younger, Paula. (2010). "[[Beyond Wikipedia: How Good a Reference Source are Medical Wikis?]]". Emerald Group Publishing Limited. DOI: 10.1108/09504121011019899. <br />
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{{cite journal |last1=Younger |first1=Paula |title=Beyond Wikipedia: How Good a Reference Source are Medical Wikis? |date=2010 |doi=10.1108/09504121011019899 |url=https://wikipediaquality.com/wiki/Beyond_Wikipedia:_How_Good_a_Reference_Source_are_Medical_Wikis? |journal=Emerald Group Publishing Limited}}<br />
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Younger, Paula. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Beyond_Wikipedia:_How_Good_a_Reference_Source_are_Medical_Wikis?">Beyond Wikipedia: How Good a Reference Source are Medical Wikis?</a>&amp;quot;. Emerald Group Publishing Limited. DOI: 10.1108/09504121011019899. <br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=The_Collaborative_Construction_of_%22Fact%22_on_Wikipedia&diff=22849The Collaborative Construction of "Fact" on Wikipedia2019-12-15T08:27:53Z<p>Aubree: The Collaborative Construction of "Fact" on Wikipedia -- new article</p>
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<div>'''The Collaborative Construction of "Fact" on Wikipedia''' - scientific work related to Wikipedia quality published in 2009, written by Jason Swarts.<br />
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== Overview ==<br />
For years Wikipedia has come to symbolize the potential of Web 2.0 for harnessing the power of mass collaboration and collective intelligence. As wikis continue to develop and move into streams of cultural, social, academic, and enterprise work activity, it is appropriate to consider how collective intelligence emerges from mass collaboration. Collective intelligence can take many forms - this paper examines one, the emergence of stable facts on Wikipedia. More specifically, this paper examines ways of participating that lead to the creation of facts. This research will show how authors can be more effective consumers, producers, and managers of wiki information by understanding how collaboration shapes facts.</div>Aubreehttps://wikipediaquality.com/index.php?title=Semantic_Relatedness_Measurement_based_on_Wikipedia_Link_Co%E2%80%90Occurrence_Analysis&diff=22848Semantic Relatedness Measurement based on Wikipedia Link Co‐Occurrence Analysis2019-12-15T08:25:28Z<p>Aubree: Wikilinks</p>
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<div>'''Semantic Relatedness Measurement based on Wikipedia Link Co‐Occurrence Analysis''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Masahiro Ito]], [[Kotaro Nakayama]], [[Takahiro Hara]] and [[Shojiro Nishio]].<br />
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== Overview ==<br />
Purpose – Recently, the importance and effectiveness of [[Wikipedia]] Mining has been shown in several researches. One popular research area on Wikipedia Mining focuses on semantic [[relatedness]] measurement, and research in this area has shown that Wikipedia can be used for semantic relatedness measurement. However, previous methods are facing two problems; accuracy and scalability. To solve these problems, the purpose of this paper is to propose an efficient semantic relatedness measurement method that leverages global statistical information of Wikipedia. Furthermore, a new test collection is constructed based on Wikipedia concepts for evaluating semantic relatedness measurement methods.Design/methodology/approach – The authors' approach leverages global statistical information of the whole Wikipedia to compute semantic relatedness among concepts (disambiguated terms) by analyzing co‐occurrences of link pairs in all Wikipedia articles. In Wikipedia, an article represents a concept and a link to another articl...</div>Aubreehttps://wikipediaquality.com/index.php?title=A_Technique_for_Suggesting_Related_Wikipedia_Articles_Using_Link_Analysis&diff=22847A Technique for Suggesting Related Wikipedia Articles Using Link Analysis2019-12-15T08:23:30Z<p>Aubree: Categories</p>
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<div>{{Infobox work<br />
| title = A Technique for Suggesting Related Wikipedia Articles Using Link Analysis<br />
| date = 2012<br />
| authors = [[Christopher Markson]]<br />[[Min Song]]<br />
| doi = 10.1145/2232817.2232883<br />
| link = https://dl.acm.org/ft_gateway.cfm?id=2232883&amp;type=pdf<br />
}}<br />
'''A Technique for Suggesting Related Wikipedia Articles Using Link Analysis''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Christopher Markson]] and [[Min Song]].<br />
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== Overview ==<br />
With more than 3.7 million articles, [[Wikipedia]] has become an important social medium for sharing knowledge. However, with this enormous repository of information, it can often be difficult to locate fundamental topics that support lower-level articles. By exploiting the information stored in the links between articles, authors propose that related companion articles can be automatically generated to help further the reader's understanding of a given topic. This approach to a recommendation system uses tested link analysis techniques to present users with a clear path to related high-level articles, furthering the understanding of low-level topics.<br />
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Markson, Christopher; Song, Min. (2012). "[[A Technique for Suggesting Related Wikipedia Articles Using Link Analysis]]".DOI: 10.1145/2232817.2232883. <br />
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{{cite journal |last1=Markson |first1=Christopher |last2=Song |first2=Min |title=A Technique for Suggesting Related Wikipedia Articles Using Link Analysis |date=2012 |doi=10.1145/2232817.2232883 |url=https://wikipediaquality.com/wiki/A_Technique_for_Suggesting_Related_Wikipedia_Articles_Using_Link_Analysis}}<br />
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Markson, Christopher; Song, Min. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Technique_for_Suggesting_Related_Wikipedia_Articles_Using_Link_Analysis">A Technique for Suggesting Related Wikipedia Articles Using Link Analysis</a>&amp;quot;.DOI: 10.1145/2232817.2232883. <br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=Group_Size_and_Incentives_to_Contribute:_a_Natural_Experiment_at_Chinese_Wikipedia&diff=22846Group Size and Incentives to Contribute: a Natural Experiment at Chinese Wikipedia2019-12-15T08:20:40Z<p>Aubree: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Group Size and Incentives to Contribute: a Natural Experiment at Chinese Wikipedia<br />
| date = 2011<br />
| authors = [[Xiaoquan Zhang]]<br />[[Feng Zhu]]<br />
| doi = 10.1257/aer.101.4.1601<br />
| link = https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1579545_code400098.pdf?abstractid=1021450&amp;mirid=5&amp;type=2<br />
}}<br />
'''Group Size and Incentives to Contribute: a Natural Experiment at Chinese Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Xiaoquan Zhang]] and [[Feng Zhu]].<br />
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== Overview ==<br />
The literature on the private provision of public goods suggests an inverse relationship between incentives to contribute and group size. Authors find, however, that after an exogenous reduction of group size at [[Chinese Wikipedia]], the nonblocked contributors decrease their contributions by 42.8 percent on average. Authors attribute the cause to social effects: contributors receive social benefits that increase with both the amount of their contributions and group size, and the shrinking group size weakens these social benefits. Consistent with explanation, authors find that the more contributors value social benefits, the more they reduce their contributions after the block. (JEL H41, L17, L82)</div>Aubreehttps://wikipediaquality.com/index.php?title=Extracting_the_Gist_of_Social_Network_Services_Using_Wikipedia&diff=22845Extracting the Gist of Social Network Services Using Wikipedia2019-12-15T08:18:17Z<p>Aubree: Basic information on Extracting the Gist of Social Network Services Using Wikipedia</p>
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<div>'''Extracting the Gist of Social Network Services Using Wikipedia''' - scientific work related to Wikipedia quality published in 2010, written by Akiyo Nadamoto, Eiji Aramaki, Takeshi Abekawa and Yohei Murakami.<br />
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== Overview ==<br />
Social Network Services(SNSs), which are maintained by a community of people, are among the popular Web 2.0 tools. Multiple users freely post their comments to an SNS thread. It is difficult to understand the gist of the comments because the dialog in an SNS thread is complicated. In this paper, authors propose a system that presents the gist of information at a glance and basic information about an SNS thread by using Wikipedia. Authors focus on the table of contents (TOC) of the relevant articles on Wikipedia. Authors system compares the comments in a thread with the information in the TOC and identifies contents that are similar. Authors consider the similar contents in the TOC as the gist of the thread and paragraphs in Wikipedia similar to the comments in the thread as comprising basic information about the thread. Thus, a user can obtain the gist of an SNS thread by viewing a table with similar contents.</div>Aubreehttps://wikipediaquality.com/index.php?title=Could_Someone_Please_Translate_This%3F:_Activity_Analysis_of_Wikipedia_Article_Translation_by_Non-Experts&diff=22844Could Someone Please Translate This?: Activity Analysis of Wikipedia Article Translation by Non-Experts2019-12-15T08:15:48Z<p>Aubree: + embed code</p>
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<div>{{Infobox work<br />
| title = Could Someone Please Translate This?: Activity Analysis of Wikipedia Article Translation by Non-Experts<br />
| date = 2013<br />
| authors = [[Ari Hautasaari]]<br />
| doi = 10.1145/2441776.2441883<br />
| link = http://dl.acm.org/citation.cfm?id=2441776.2441883<br />
}}<br />
'''Could Someone Please Translate This?: Activity Analysis of Wikipedia Article Translation by Non-Experts''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Ari Hautasaari]].<br />
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== Overview ==<br />
Wikipedia translation activities aim to improve the quality of the [[multilingual]] [[Wikipedia]] through article translation. Authors performed an activity analysis of the translation work done by individual English to Chinese non-expert translators, who translated linguistically complex Wikipedia articles in a laboratory setting. From the analysis, which was based on Activity Theory, and which examined both information search and translation activities, authors derived three translation strategies that were used to inform the design of a support system for human translation activities in Wikipedia.<br />
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{{cite journal |last1=Hautasaari |first1=Ari |title=Could Someone Please Translate This?: Activity Analysis of Wikipedia Article Translation by Non-Experts |date=2013 |doi=10.1145/2441776.2441883 |url=https://wikipediaquality.com/wiki/Could_Someone_Please_Translate_This?:_Activity_Analysis_of_Wikipedia_Article_Translation_by_Non-Experts}}<br />
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Hautasaari, Ari. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Could_Someone_Please_Translate_This?:_Activity_Analysis_of_Wikipedia_Article_Translation_by_Non-Experts">Could Someone Please Translate This?: Activity Analysis of Wikipedia Article Translation by Non-Experts</a>&amp;quot;.DOI: 10.1145/2441776.2441883. <br />
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<div>{{Infobox work<br />
| title = Multi-Level Topical Text Categorization with Wikipedia<br />
| date = 2016<br />
| authors = [[Nan Guo]]<br />[[Yuan He]]<br />[[ChunGang Yan]]<br />[[Lu Liu]]<br />[[Cheng Wang]]<br />
| doi = 10.1145/2996890.3007856<br />
| link = https://doi.acm.org/10.1145/2996890.3007856<br />
}}<br />
'''Multi-Level Topical Text Categorization with Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Nan Guo]], [[Yuan He]], [[ChunGang Yan]], [[Lu Liu]] and [[Cheng Wang]].<br />
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== Overview ==<br />
This paper introduces an automatic categorical-marking model for text categorization. Traditional classification algorithms are generally applying labeled training set and call for a lot of manual work to tag classifications beforehand. Also due to the ambiguity and fuzziness of texts, the results of traditional text categorization algorithms may not be clear enough and abundant in content. This paper presents an unsupervised, training-set-free and hierarchical categorization model called Folk-Topical Text Categorization (FTTC). FTTC applies topic model to abstract documents to topical words and make use of [[Wikipedia]]'s crowd-sourcing and collective control to extend hierarchical classifications. The results are not restricted to predefined [[categories]] but contain categories abstracted to deeper semantic levels and greatly facilitate traditional text categorization applications. For a document, its topical words are obtained using a popular topic model called Latent Dirichlet Allocation (LDA). Afterwards, the topical words are used to build and trace through the category-trees of Wikipedia. Based on the filtered results, the final classifications comprehensively reflect the diversified and content-rich information of the text, and fully cover different aspects of the text. Experimental results on different kinds of datasets show that model advances in classification accuracy, flexibility and intelligibility, as compared with traditional models.</div>Aubreehttps://wikipediaquality.com/index.php?title=Structural_Analysis_on_Wikipedia_Linkage_Network&diff=22842Structural Analysis on Wikipedia Linkage Network2019-12-15T08:10:54Z<p>Aubree: Int.links</p>
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<div>'''Structural Analysis on Wikipedia Linkage Network''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Shi Quan]].<br />
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== Overview ==<br />
In order to make better use of [[Wikipedia]],it is necessary to have a clear understanding of the structural [[features]] of word entry hyperlink network of Wikipedia.This paper conducted brief experimental analysis in aspects of degree distribution,weight distribution and marco structural analysis on Wikipedia crawled on January 2010.By comparing these characteristic with previous works done before 2006,it found that the Wikipedia hyperlink network still followed power-law distribution on degree and the marco structure still obeyed bow-tie model,however,the proportion of different components defined in bow-tie model varied significantly.</div>Aubreehttps://wikipediaquality.com/index.php?title=Wikipedia_Infobox_Temporal_Rdf_Knowledge_Base_and_Indices&diff=22841Wikipedia Infobox Temporal Rdf Knowledge Base and Indices2019-12-15T08:07:49Z<p>Aubree: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Wikipedia Infobox Temporal Rdf Knowledge Base and Indices<br />
| date = 2015<br />
| authors = [[Aige Song]]<br />
| link = http://escholarship.org/uc/item/7kc476n9.pdf<br />
}}<br />
'''Wikipedia Infobox Temporal Rdf Knowledge Base and Indices''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Aige Song]].<br />
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== Overview ==<br />
As real world evolves, Infoboxes for [[Wikipedia]] subjects are updated to reflect the information changes in the real world, and there is a growing interest in the evolution history of subjects in the Wikipedia. Thus, the management of historical information and the efficiencies of queries for these temporal information have become the major concern. In this paper, authors introduce the Wikipedia Infobox temporal RDF knowledge base that constructed from the Wikipedia Infobox history dump, and evaluate the efficiencies of temporal queries based on the temporal knowledge base. Specifically, authors evaluate temporal selection and temporal join queries based on different database systems with different indices, including MySQL B+ Tree, PostgreSQL B-Tree, and Interval Tree.<br />
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Song, Aige. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_Infobox_Temporal_Rdf_Knowledge_Base_and_Indices">Wikipedia Infobox Temporal Rdf Knowledge Base and Indices</a>&amp;quot;.<br />
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<div>{{Infobox work<br />
| title = Learning to Split and Rephrase from Wikipedia Edit History<br />
| date = 2018<br />
| authors = [[Jan A. Botha]]<br />[[Manaal Faruqui]]<br />[[John Alex]]<br />[[Jason Baldridge]]<br />[[Dipanjan Das]]<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-99972-2_11.pdf<br />
| plink = http://arxiv.org/pdf/1808.09468.pdf<br />
}}<br />
'''Learning to Split and Rephrase from Wikipedia Edit History''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Jan A. Botha]], [[Manaal Faruqui]], [[John Alex]], [[Jason Baldridge]] and [[Dipanjan Das]].<br />
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== Overview ==<br />
Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning. Authors extract a rich new dataset for this task by mining [[Wikipedia]]'s edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al. (2017) as a benchmark for this task. Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark.<br />
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Botha, Jan A.; Faruqui, Manaal; Alex, John; Baldridge, Jason; Das, Dipanjan. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Learning_to_Split_and_Rephrase_from_Wikipedia_Edit_History">Learning to Split and Rephrase from Wikipedia Edit History</a>&amp;quot;.<br />
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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=Authoring_the_Neighbourhood_in_Wikipedia&diff=22839Authoring the Neighbourhood in Wikipedia2019-12-15T08:02:45Z<p>Aubree: wikilinks</p>
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<div>'''Authoring the Neighbourhood in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Rebecca Ross]] and [[Chi Nguyen]].<br />
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== Overview ==<br />
Engaged Urbanism showcases the exciting ways in which urbanists are responding to this question and working towards fairer cities. Its authors offer succinct, candid and carefully illustrated commentaries on the trials and successes of risk-taking research, revealing how they collaborate across fields of expertise, inventing or adapting methods to suit bespoke situations. Featuring novel uses and combinations of practice-from activism, architectural design and undercover journalism, to film, sculpture, performance and photography- in a diversity of cities such as Beirut, Johannesburg, Kisumu, London and Rio de Janeiro, Engaged Urbanism demonstrates how some of the greatest challenges for present and future populations are being rigorously and creatively addressed.</div>Aubreehttps://wikipediaquality.com/index.php?title=Focused_Search_in_Books_and_Wikipedia:_Categories,_Links_and_Relevance_Feedback&diff=22838Focused Search in Books and Wikipedia: Categories, Links and Relevance Feedback2019-12-15T08:00:37Z<p>Aubree: + category</p>
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<div>{{Infobox work<br />
| title = Focused Search in Books and Wikipedia: Categories, Links and Relevance Feedback<br />
| date = 2009<br />
| authors = [[Marijn Koolen]]<br />[[Rianne Kaptein]]<br />[[Jaap Kamps]]<br />
| doi = 10.1007/978-3-642-14556-8_28<br />
| link = https://dl.acm.org/citation.cfm?id=1881065.1881098<br />
}}<br />
'''Focused Search in Books and Wikipedia: Categories, Links and Relevance Feedback''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Marijn Koolen]], [[Rianne Kaptein]] and [[Jaap Kamps]].<br />
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== Overview ==<br />
In this paper authors describe participation in INEX 2009 in the Ad Hoc Track, the Book Track, and the Entity Ranking Track. In the Ad Hoc track authors investigate focused link evidence, using only links from retrieved sections. The new collection is not only annotated with [[Wikipedia categories]], but also with YAGO/[[WordNet]] [[categories]]. Authors explore how authors can use both types of category information, in the Ad Hoc Track as well as in the Entity Ranking Track. Results in the Ad Hoc Track show [[Wikipedia]] categories are more effective than WordNet categories, and Wikipedia categories in combination with relevance feed-back lead to the best results. Preliminary results of the Book Track show full-text retrieval is effective for high early precision. Relevance feedback further increases early precision. Authors findings for the Entity Ranking Track are in direct opposition of Ad Hoc findings, namely, that the WordNet categories are more effective than the Wikipedia categories. This marks an interesting difference between ad hoc search and entity ranking.<br />
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Koolen, Marijn; Kaptein, Rianne; Kamps, Jaap. (2009). "[[Focused Search in Books and Wikipedia: Categories, Links and Relevance Feedback]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-14556-8_28. <br />
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{{cite journal |last1=Koolen |first1=Marijn |last2=Kaptein |first2=Rianne |last3=Kamps |first3=Jaap |title=Focused Search in Books and Wikipedia: Categories, Links and Relevance Feedback |date=2009 |doi=10.1007/978-3-642-14556-8_28 |url=https://wikipediaquality.com/wiki/Focused_Search_in_Books_and_Wikipedia:_Categories,_Links_and_Relevance_Feedback |journal=Springer, Berlin, Heidelberg}}<br />
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Koolen, Marijn; Kaptein, Rianne; Kamps, Jaap. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Focused_Search_in_Books_and_Wikipedia:_Categories,_Links_and_Relevance_Feedback">Focused Search in Books and Wikipedia: Categories, Links and Relevance Feedback</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-14556-8_28. <br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=Sense-Aware_Semantic_Analysis:_a_Multi-Prototype_Word_Representation_Model_Using_Wikipedia&diff=22837Sense-Aware Semantic Analysis: a Multi-Prototype Word Representation Model Using Wikipedia2019-12-15T07:59:26Z<p>Aubree: + Embed</p>
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<div>{{Infobox work<br />
| title = Sense-Aware Semantic Analysis: a Multi-Prototype Word Representation Model Using Wikipedia<br />
| date = 2015<br />
| authors = [[Zhaohui Wu]]<br />[[C. Lee Giles]]<br />
| link = http://dl.acm.org/citation.cfm?id=2886625<br />
}}<br />
'''Sense-Aware Semantic Analysis: a Multi-Prototype Word Representation Model Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Zhaohui Wu]] and [[C. Lee Giles]].<br />
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== Overview ==<br />
Human languages are naturally ambiguous, which makes it difficult to automatically understand the semantics of text. Most vector space models (VSM) treat all occurrences of a word as the same and build a single vector to represent the meaning of a word, which fails to capture any ambiguity. Authors present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on [[Wikipedia]], which could account for homonymy and polysemy. The "sense-specific" prototypes of a word are produced by clustering Wikipedia pages based on both local and global contexts of the word in Wikipedia. Experimental evaluation on semantic [[relatedness]] for both isolated words and words in sentential contexts and word sense induction demonstrate its effectiveness.<br />
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Wu, Zhaohui; Giles, C. Lee. (2015). "[[Sense-Aware Semantic Analysis: a Multi-Prototype Word Representation Model Using Wikipedia]]". AAAI Press. <br />
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{{cite journal |last1=Wu |first1=Zhaohui |last2=Giles |first2=C. Lee |title=Sense-Aware Semantic Analysis: a Multi-Prototype Word Representation Model Using Wikipedia |date=2015 |url=https://wikipediaquality.com/wiki/Sense-Aware_Semantic_Analysis:_a_Multi-Prototype_Word_Representation_Model_Using_Wikipedia |journal=AAAI Press}}<br />
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Wu, Zhaohui; Giles, C. Lee. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Sense-Aware_Semantic_Analysis:_a_Multi-Prototype_Word_Representation_Model_Using_Wikipedia">Sense-Aware Semantic Analysis: a Multi-Prototype Word Representation Model Using Wikipedia</a>&amp;quot;. AAAI Press. <br />
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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=Aprenent_Mitjan%C3%A7ant_La_Comparaci%C3%B3_Amb_La_Wikipedia:_La_Seva_Import%C3%A0ncia_En_L%27Aprenentatge_Dels_Estudiants&diff=22836Aprenent Mitjançant La Comparació Amb La Wikipedia: La Seva Importància En L'Aprenentatge Dels Estudiants2019-12-15T07:56:41Z<p>Aubree: cats.</p>
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<div>{{Infobox work<br />
| title = Aprenent Mitjançant La Comparació Amb La Wikipedia: La Seva Importància En L'Aprenentatge Dels Estudiants<br />
| date = 2014<br />
| authors = [[Antoni Meseguer-Artola]]<br />
| doi = 10.7238/rusc.v11i2.2042<br />
| link = http://www.raco.cat/index.php/RUSC/article/view/285052<br />
}}<br />
'''Aprenent Mitjançant La Comparació Amb La Wikipedia: La Seva Importància En L'Aprenentatge Dels Estudiants''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Antoni Meseguer-Artola]].<br />
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== Overview ==<br />
The main purpose of this research work is to describe and evaluate a learning technique that actively uses [[Wikipedia]] in an online master’s degree course in Statistics. It is based on the comparison between Wikipedia content and standard academic learning materials. Authors define this technique as ‘learning by comparing’. In order to evaluate the performance of this learning technique, data from different academic semesters was collected. Through different hypothesis tests, the academic performance of the students following a learning-by-comparing strategy is compared with the case where Wikipedia is not used. Additionally, during the course the students are asked about the [[reliability]], currentness, [[completeness]] and usefulness of Wikipedia, as rated on a 5-point Likert scale. This data is used to analyse the perceived quality of Wikipedia, for each statistical concept of the course, and to discover its relationship with academic performance. To that end, descriptive statistics, dependence tests, and contrasts of means have been performed.<br />
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Meseguer-Artola, Antoni. (2014). "[[Aprenent Mitjançant La Comparació Amb La Wikipedia: La Seva Importància En L'Aprenentatge Dels Estudiants]]". Universitat Oberta de Catalunya, University of New England. DOI: 10.7238/rusc.v11i2.2042. <br />
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{{cite journal |last1=Meseguer-Artola |first1=Antoni |title=Aprenent Mitjançant La Comparació Amb La Wikipedia: La Seva Importància En L'Aprenentatge Dels Estudiants |date=2014 |doi=10.7238/rusc.v11i2.2042 |url=https://wikipediaquality.com/wiki/Aprenent_Mitjançant_La_Comparació_Amb_La_Wikipedia:_La_Seva_Importància_En_L'Aprenentatge_Dels_Estudiants |journal=Universitat Oberta de Catalunya, University of New England}}<br />
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Meseguer-Artola, Antoni. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Aprenent_Mitjançant_La_Comparació_Amb_La_Wikipedia:_La_Seva_Importància_En_L'Aprenentatge_Dels_Estudiants">Aprenent Mitjançant La Comparació Amb La Wikipedia: La Seva Importància En L'Aprenentatge Dels Estudiants</a>&amp;quot;. Universitat Oberta de Catalunya, University of New England. DOI: 10.7238/rusc.v11i2.2042. <br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=Generating_Information-Rich_Taxonomy_from_Wikipedia&diff=22835Generating Information-Rich Taxonomy from Wikipedia2019-12-15T07:55:04Z<p>Aubree: + cat.</p>
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<div>{{Infobox work<br />
| title = Generating Information-Rich Taxonomy from Wikipedia<br />
| date = 2010<br />
| authors = [[Ichiro Yamada]]<br />[[Chikara Hashimoto]]<br />[[Jong-Hoon Oh]]<br />[[Kentaro Torisawa]]<br />[[Kow Kuroda]]<br />[[Stijn De Saeger]]<br />[[Masaaki Tsuchida]]<br />[[Jun’ichi Kazama]]<br />
| doi = 10.1109/IUCS.2010.5666764<br />
| link = http://ieeexplore.ieee.org/xpl/abstractReferences.jsp?reload=true&amp;arnumber=5666764&amp;punumber%3D5654670<br />
}}<br />
'''Generating Information-Rich Taxonomy from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Ichiro Yamada]], [[Chikara Hashimoto]], [[Jong-Hoon Oh]], [[Kentaro Torisawa]], [[Kow Kuroda]], [[Stijn De Saeger]], [[Masaaki Tsuchida]] and [[Jun’ichi Kazama]].<br />
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== Overview ==<br />
Even though hyponymy relation acquisition has been extensively studied, “how informative such acquired hyponymy relations are” has not been sufficiently discussed. Authors found that the hypernyms in automatically acquired hyponymy relations were often too vague or ambiguous to specify the meaning of their hyponyms. For instance, hypernym work is vague and ambiguous in hyponymy relations work/Avatar and work/The Catcher in the Rye. In this paper, authors propose a simple method of generating intermediate concepts of hyponymy relations that can make such (vague) hypernyms more specific. Authors method generates such an information-rich hyponymy relation as work / work by film director / work by James Cameron / Avatar from the less informative relation work/Avatar. Furthermore, the generated relation work by film director/Avatar can be paraphrased into a new relation movie/Avatar. Experiments showed that method successfully acquired 2,719,441 enriched hyponymy relations with one intermediate concept with 0.853 precision and another 6,347,472 hyponymy relations with 0.786 precision.<br />
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Yamada, Ichiro; Hashimoto, Chikara; Oh, Jong-Hoon; Torisawa, Kentaro; Kuroda, Kow; Saeger, Stijn De; Tsuchida, Masaaki; Kazama, Jun’ichi. (2010). "[[Generating Information-Rich Taxonomy from Wikipedia]]".DOI: 10.1109/IUCS.2010.5666764. <br />
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{{cite journal |last1=Yamada |first1=Ichiro |last2=Hashimoto |first2=Chikara |last3=Oh |first3=Jong-Hoon |last4=Torisawa |first4=Kentaro |last5=Kuroda |first5=Kow |last6=Saeger |first6=Stijn De |last7=Tsuchida |first7=Masaaki |last8=Kazama |first8=Jun’ichi |title=Generating Information-Rich Taxonomy from Wikipedia |date=2010 |doi=10.1109/IUCS.2010.5666764 |url=https://wikipediaquality.com/wiki/Generating_Information-Rich_Taxonomy_from_Wikipedia}}<br />
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Yamada, Ichiro; Hashimoto, Chikara; Oh, Jong-Hoon; Torisawa, Kentaro; Kuroda, Kow; Saeger, Stijn De; Tsuchida, Masaaki; Kazama, Jun’ichi. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Generating_Information-Rich_Taxonomy_from_Wikipedia">Generating Information-Rich Taxonomy from Wikipedia</a>&amp;quot;.DOI: 10.1109/IUCS.2010.5666764. <br />
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[[Category:Scientific works]]</div>Aubreehttps://wikipediaquality.com/index.php?title=A_Tale_of_Information_Ethics_and_Encyclop%C3%A6dias;_Or,_is_Wikipedia_Just_Another_Internet_Scam%3F&diff=22834A Tale of Information Ethics and Encyclopædias; Or, is Wikipedia Just Another Internet Scam?2019-12-15T07:53:43Z<p>Aubree: Adding embed</p>
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<div>{{Infobox work<br />
| title = A Tale of Information Ethics and Encyclopædias; Or, is Wikipedia Just Another Internet Scam?<br />
| date = 2007<br />
| authors = [[Gary E. Gorman]]<br />
| doi = 10.1108/14684520710773050<br />
| link = https://www.emeraldinsight.com/doi/abs/10.1108/14684520710773050<br />
}}<br />
'''A Tale of Information Ethics and Encyclopædias; Or, is Wikipedia Just Another Internet Scam?''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Gary E. Gorman]].<br />
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== Overview ==<br />
Purpose – This paper seeks to look at the question of accuracy of content regarding [[Wikipedia]] and other internet encyclopaedias.Design/methodology/approach – By looking at other sources, the paper considers whether the information contained within Wikipedia can be relied on to be accurate.Findings – Wikipedia poses as an encyclopaedia when by no stretch of the definition can it be termed such; therefore, it should be subject to regulation.Originality/value – The paper highlights the issue that, without regulation, content cannot be relied on to be accurate.<br />
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Gorman, Gary E.. (2007). "[[A Tale of Information Ethics and Encyclopædias; Or, is Wikipedia Just Another Internet Scam?]]". Emerald Group Publishing Limited. DOI: 10.1108/14684520710773050. <br />
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{{cite journal |last1=Gorman |first1=Gary E. |title=A Tale of Information Ethics and Encyclopædias; Or, is Wikipedia Just Another Internet Scam? |date=2007 |doi=10.1108/14684520710773050 |url=https://wikipediaquality.com/wiki/A_Tale_of_Information_Ethics_and_Encyclopædias;_Or,_is_Wikipedia_Just_Another_Internet_Scam? |journal=Emerald Group Publishing Limited}}<br />
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Gorman, Gary E.. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Tale_of_Information_Ethics_and_Encyclopædias;_Or,_is_Wikipedia_Just_Another_Internet_Scam?">A Tale of Information Ethics and Encyclopædias; Or, is Wikipedia Just Another Internet Scam?</a>&amp;quot;. Emerald Group Publishing Limited. DOI: 10.1108/14684520710773050. <br />
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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=Wikionto:_a_System_for_Semi-Automatic_Extraction_and_Modeling_of_Ontologies_Using_Wikipedia_Xml_Corpus&diff=22833Wikionto: a System for Semi-Automatic Extraction and Modeling of Ontologies Using Wikipedia Xml Corpus2019-12-15T07:52:12Z<p>Aubree: + Embed</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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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=What_Do_Chinese-Language_Microblog_Users_Do_with_Baidu_Baike_and_Chinese_Wikipedia%3F_a_Case_Study_of_Information_Engagement&diff=21419What Do Chinese-Language Microblog Users Do with Baidu Baike and Chinese Wikipedia? a Case Study of Information Engagement2019-10-19T10:22:37Z<p>Aubree: Wikilinks</p>
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<div>'''What Do Chinese-Language Microblog Users Do with Baidu Baike and Chinese Wikipedia? a Case Study of Information Engagement''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Han-Teng Liao]].<br />
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== Overview ==<br />
This paper presents a case study of information engagement based on microblog posts gathered from Sina Weibo and [[Twitter]] that mentioned the two major Chinese-language user-generated encyclopaedias. The content analysis shows that microblog users not only engaged in public discussions by using and citing both encyclopaedias, but also shared their perceptions and experiences more generally with various online platforms and China's filtering/censorship regime to which user-generated content and activities are subjected. This exploratory study thus raises several research and practice questions on the links between public discussions and information engagement on user-generated platforms.</div>Aubreehttps://wikipediaquality.com/index.php?title=A_Method_for_Automated_Document_Classification_Using_Wikipedia-Derived_Weighted_Keywords&diff=21418A Method for Automated Document Classification Using Wikipedia-Derived Weighted Keywords2019-10-19T10:21:30Z<p>Aubree: + wikilinks</p>
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<div>'''A Method for Automated Document Classification Using Wikipedia-Derived Weighted Keywords''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Robert P. Biuk-Aghai]] and [[Ka Kit Ng]].<br />
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== Overview ==<br />
The pace of knowledge creation such as in academic research has accelerated rapidly in recent years, resulting in ever more new research publications. This has made it difficult to keep abreast of new developments, or to know which new publications are relevant to a given research area. Authors have developed a method for analysing and automatically classifying publications. Authors method makes use of the [[Wikipedia]] category hierarchy, and the content of Wikipedia articles associated to [[Wikipedia categories]]. Initially authors perform pre-processing and simplification of the Wikipedia category hierarchy, resulting in a rooted directed graph. Wikipedia articles are then analysed, and a set of keywords per Wikipedia category are extracted using a modified tf-idf (term frequency-inverse document frequency) model proposed in this paper. To classify a given input document, tf-idf weights are used to extract relevant keywords from the document, which are then matched to the keywords previously extracted from Wikipedia. The closest matching top-level [[categories]] are identified from all categories containing the document's keywords. A cosine similarity metric is then applied to select the closest matching sub-category, recursing down the category hierarchy until the best matching categories are identified. The final result produced shows a set of categories matching the input document, together with a matching percentage. This result can be used to identify new documents that are relevant to a specific research area, or to classify a whole set of documents into different topic areas, with sub-topics, main keywords, and associated weights. Authors present an experimental study using data from [[English Wikipedia]].</div>Aubreehttps://wikipediaquality.com/index.php?title=Improved_Automatic_Maturity_Assessment_of_Wikipedia_Medical_Articles&diff=21417Improved Automatic Maturity Assessment of Wikipedia Medical Articles2019-10-19T10:19:55Z<p>Aubree: Wikilinks</p>
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<div>'''Improved Automatic Maturity Assessment of Wikipedia Medical Articles''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Emanuel Marzini]], [[Angelo Spognardi]], [[Ilaria Matteucci]], [[Paolo Mori]], [[Marinella Petrocchi]] and [[Riccardo Conti]].<br />
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== Overview ==<br />
The Internet is naturally a simple and immediate mean to retrieve information. However, not everything one can find is equally accurate and reliable. In this paper, authors continue line of research towards effective techniques for assessing the quality of online content. Focusing on the [[Wikipedia]] Medicinal Portal, in a previous work authors implemented an automatic technique to assess the quality of each article and authors compared results to the classification of the articles given by the portal itself, obtaining quite different outcomes. Here, authors present a lightweight instantiation of methodology that reduces both redundant [[features]] and those not mentioned by the WikiProject guidelines. What authors obtain is a fine-grained assessment and a better discrimination of the articles’ quality, w.r.t. previous work. Authors proposal could help to automatically evaluate the maturity of Wikipedia medical articles in an efficient way.</div>Aubreehttps://wikipediaquality.com/index.php?title=Research_of_Social_Network_Analysis_Technology_based_on_Wikipedia&diff=21416Research of Social Network Analysis Technology based on Wikipedia2019-10-19T10:17:43Z<p>Aubree: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Research of Social Network Analysis Technology based on Wikipedia<br />
| date = 2011<br />
| authors = [[Zhu Pei-dong]]<br />
| link = http://en.cnki.com.cn/Article_en/CJFDTOTAL-WJFZ201112002.htm<br />
}}<br />
'''Research of Social Network Analysis Technology based on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Zhu Pei-dong]].<br />
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== Overview ==<br />
SNA analyses the relationship of users in [[social network]],based on which visualize the potential relationship and structure [[features]] in a more direct way.It is based on information snatched from [[Wikipedia]].Three kinds of social network were built and analyzed using SNA methods.Each of the three focuses on different kind of relationships,relationship of related reaches,relationship of technologies in computer network and relationship of scientists.Information of 80 scientists who have made valuable contribution to computer science were collected and analyzed.The result shows that most of them are not in the core and seldom cooperating with others.Besides,although some technologies have little subfields,they have more space to exploit and may become the "hot point" in the future.<br />
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{{cite journal |last1=Pei-dong |first1=Zhu |title=Research of Social Network Analysis Technology based on Wikipedia |date=2011 |url=https://wikipediaquality.com/wiki/Research_of_Social_Network_Analysis_Technology_based_on_Wikipedia}}<br />
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Pei-dong, Zhu. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Research_of_Social_Network_Analysis_Technology_based_on_Wikipedia">Research of Social Network Analysis Technology based on Wikipedia</a>&amp;quot;.<br />
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<div>{{Infobox work<br />
| title = Social Network Mining based on Wikipedia<br />
| date = 2010<br />
| authors = [[Fangfang Yang]]<br />[[Zhiming Xu]]<br />[[Sheng Li]]<br />[[Zhikai Xu]]<br />
| doi = 10.1109/IALP.2010.39<br />
| link = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5681597<br />
}}<br />
'''Social Network Mining based on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Fangfang Yang]], [[Zhiming Xu]], [[Sheng Li]] and [[Zhikai Xu]].<br />
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== Overview ==<br />
This paper proposes a method to build and analyze [[social network]]s of person entities on [[Wikipedia]]. Here each person entity is represented by a few attributes. Different estimation approaches of attribute similarity are used, on which authors employ the Systematic Similarity Measure theory to compute the person entity similarity. On the basis of the similarity array of person entities, authors build the social network for them. On Wikipedia data, authors conduct some experiments on social network analysis, and the experimental results show social network mining approaches are effective.<br />
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Yang, Fangfang; Xu, Zhiming; Li, Sheng; Xu, Zhikai. (2010). "[[Social Network Mining based on Wikipedia]]".DOI: 10.1109/IALP.2010.39. <br />
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{{cite journal |last1=Yang |first1=Fangfang |last2=Xu |first2=Zhiming |last3=Li |first3=Sheng |last4=Xu |first4=Zhikai |title=Social Network Mining based on Wikipedia |date=2010 |doi=10.1109/IALP.2010.39 |url=https://wikipediaquality.com/wiki/Social_Network_Mining_based_on_Wikipedia}}<br />
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Yang, Fangfang; Xu, Zhiming; Li, Sheng; Xu, Zhikai. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Social_Network_Mining_based_on_Wikipedia">Social Network Mining based on Wikipedia</a>&amp;quot;.DOI: 10.1109/IALP.2010.39. <br />
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</code></div>Aubreehttps://wikipediaquality.com/index.php?title=Improving_Keyphrase_Extraction_Using_Wikipedia_Semantics&diff=21414Improving Keyphrase Extraction Using Wikipedia Semantics2019-10-19T10:14:27Z<p>Aubree: Links</p>
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<div>'''Improving Keyphrase Extraction Using Wikipedia Semantics''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Tianyi Shi]], [[Shidou Jiao]], [[Junqi Hou]] and [[Minglu Li]].<br />
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== Overview ==<br />
Keyphrase extraction plays a key role in various fields such as [[information retrieval]], text classification etc. However, most traditional keyphrase extraction methods relies on word frequency and position instead of document inherent [[semantic information]], often results in inaccurate output. In this paper, authors propose a novel automatic keyphrase extraction algorithm using semantic [[features]] mined from online [[Wikipedia]]. This algorithm first identifies candidate keyphrases based on lexical methods, and then a semantic graph which connects candidate keyphrases with document topics is constructed. Afterwards, a link analysis algorithm is applied to assign semantic feature weight to the candidate keyphrases. Finally, several statistical and semantic features are assembled by a regression model to predict the quality of candidates. Encouraging results are achieved in experiments which show the effectiveness of method.</div>Aubreehttps://wikipediaquality.com/index.php?title=A_Search_Engine_for_Browsing_the_Wikipedia_Thesaurus&diff=21413A Search Engine for Browsing the Wikipedia Thesaurus2019-10-19T10:13:01Z<p>Aubree: Int.links</p>
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<div>'''A Search Engine for Browsing the Wikipedia Thesaurus''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Kotaro Nakayama]], [[Takahiro Hara]] and [[Shojiro Nishio]].<br />
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== Overview ==<br />
Wikipedia has become a huge phenomenon on the WWW. As a corpus for knowledge extraction, it has various impressive characteristics such as a huge amount of articles, live updates, a dense link structure, brief link texts and URL identification for concepts. In previous work, authors proposed link structure mining algorithms to extract a huge scale and accurate association thesaurus from [[Wikipedia]]. The association thesaurus covers almost 1.3million concepts and the significant accuracy is proved in detailed experiments. To prove its practicality, authors implemented three [[features]] on the association thesaurus; a search engine for browsing Wikipedia Thesaurus, an XML Web service for the thesaurus and a Semantic Web support feature. Authors show these features in this demonstration.</div>Aubreehttps://wikipediaquality.com/index.php?title=Computing_Semantic_Similarity_for_Vietnamese_Concepts_Using_Wikipedia&diff=21412Computing Semantic Similarity for Vietnamese Concepts Using Wikipedia2019-10-19T10:10:40Z<p>Aubree: Categories</p>
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<div>{{Infobox work<br />
| title = Computing Semantic Similarity for Vietnamese Concepts Using Wikipedia<br />
| date = 2015<br />
| authors = [[Hien T. Nguyen]]<br />
| doi = 10.1007/978-3-319-14633-1_7<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-14633-1_7.pdf<br />
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'''Computing Semantic Similarity for Vietnamese Concepts Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Hien T. Nguyen]].<br />
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== Overview ==<br />
Evaluating [[semantic similarity]] between concepts is a very common component in many applications dealing with textual data such as [[information extraction]], [[information retrieval]], [[natural language processing]], or knowledge acquisition. This paper presents an approach to assess semantic similarity between Vietnamese concepts using Vietnamese [[Wikipedia]]. Firstly, the Vietnamese Wikipedia’ structure is exploited to derive a Vietnamese [[ontology]]. Next, based on the obtained ontology, authors employ similarity [[measures]] in literature to evaluate the semantic similarity between Vietnamese concepts. Then authors conduct an experiment providing 30 Vietnamese concept pairs to 18 human subjects to assess similarity of these pairs. Finally, authors use Pearson product-moment correlation coefficient to estimate the correlation between human judgments and the results of similarity measures employed. The experiment results show that system achieves quite good performance and that similarity measures between Vietnamese concepts are potential in enhancing the performance of applications dealing with textual data.<br />
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Nguyen, Hien T.. (2015). "[[Computing Semantic Similarity for Vietnamese Concepts Using Wikipedia]]". Springer, Cham. DOI: 10.1007/978-3-319-14633-1_7. <br />
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{{cite journal |last1=Nguyen |first1=Hien T. |title=Computing Semantic Similarity for Vietnamese Concepts Using Wikipedia |date=2015 |doi=10.1007/978-3-319-14633-1_7 |url=https://wikipediaquality.com/wiki/Computing_Semantic_Similarity_for_Vietnamese_Concepts_Using_Wikipedia |journal=Springer, Cham}}<br />
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Nguyen, Hien T.. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Computing_Semantic_Similarity_for_Vietnamese_Concepts_Using_Wikipedia">Computing Semantic Similarity for Vietnamese Concepts Using Wikipedia</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-14633-1_7. <br />
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[[Category:Scientific works]]<br />
[[Category:Vietnamese Wikipedia]]</div>Aubreehttps://wikipediaquality.com/index.php?title=Identifying_Document_Topics_Using_the_Wikipedia_Category_Network&diff=21411Identifying Document Topics Using the Wikipedia Category Network2019-10-19T10:08:53Z<p>Aubree: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Identifying Document Topics Using the Wikipedia Category Network<br />
| date = 2006<br />
| authors = [[Peter Sch]]<br />
| link = http://doi.ieeecomputersociety.org/10.1109/WI.2006.92<br />
}}<br />
'''Identifying Document Topics Using the Wikipedia Category Network''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Peter Sch]].<br />
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== Overview ==<br />
In the last few years the size and coverage of [[Wikipedia]], a freely available on-line encyclopedia has reached the point where it can be utilized similar to an [[ontology]] or taxonomy to identify the topics discussed in a document. In this paper authors will show that even a 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 by performing classification and clustering on 20 Newsgroups and RCV1, representing documents by their Wikipedia categories instead of their texts.</div>Aubree