https://wikipediaquality.com/api.php?action=feedcontributions&user=Macey&feedformat=atomWikipedia Quality - User contributions [en]2024-03-29T00:16:05ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Query_Expansion_from_Wikipedia_and_Topic_Web_Crawler_on_Clir&diff=28270Query Expansion from Wikipedia and Topic Web Crawler on Clir2021-03-14T21:31:19Z<p>Macey: cats.</p>
<hr />
<div>{{Infobox work<br />
| title = Query Expansion from Wikipedia and Topic Web Crawler on Clir<br />
| date = 2010<br />
| authors = [[Meng-Chun Lin]]<br />[[Ming-Xiang Li]]<br />[[Chih-Chuan Hsu]]<br />[[Shih-Hung Wu]]<br />
| link = http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings8/NTCIR/03-NTCIR8-IR4QA-LinM.pdf<br />
}}<br />
'''Query Expansion from Wikipedia and Topic Web Crawler on Clir''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Meng-Chun Lin]], [[Ming-Xiang Li]], [[Chih-Chuan Hsu]] and [[Shih-Hung Wu]].<br />
<br />
== Overview ==<br />
In this paper, authors report various strategies for query expansion (QE) in the NTCIR-8 IR4QA subtask. Authors submit the results of twelve runs from the formal run, which include cross-language [[information retrieval]] from English to traditional Chinese, from English to simplified Chinese, and from English to Japanese in the official T-run, D-run and DN-run. Authors approach uses [[Google]] translation and the Okapi BM25 pseudo relevance feedback as the basic retrieval system. Authors add more QE from [[Wikipedia]] and the result of QA analysis. In the additional runs, authors use a topic web crawler to get more related web pages and to extract more keywords to act as candidates for QE.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Lin, Meng-Chun; Li, Ming-Xiang; Hsu, Chih-Chuan; Wu, Shih-Hung. (2010). "[[Query Expansion from Wikipedia and Topic Web Crawler on Clir]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Lin |first1=Meng-Chun |last2=Li |first2=Ming-Xiang |last3=Hsu |first3=Chih-Chuan |last4=Wu |first4=Shih-Hung |title=Query Expansion from Wikipedia and Topic Web Crawler on Clir |date=2010 |url=https://wikipediaquality.com/wiki/Query_Expansion_from_Wikipedia_and_Topic_Web_Crawler_on_Clir}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Lin, Meng-Chun; Li, Ming-Xiang; Hsu, Chih-Chuan; Wu, Shih-Hung. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Query_Expansion_from_Wikipedia_and_Topic_Web_Crawler_on_Clir">Query Expansion from Wikipedia and Topic Web Crawler on Clir</a>&amp;quot;.<br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]<br />
[[Category:English Wikipedia]]<br />
[[Category:Japanese Wikipedia]]<br />
[[Category:Chinese Wikipedia]]</div>Maceyhttps://wikipediaquality.com/index.php?title=Understanding_User%27s_Query_Intent_with_Wikipedia&diff=28269Understanding User's Query Intent with Wikipedia2021-03-14T21:28:35Z<p>Macey: cat.</p>
<hr />
<div>{{Infobox work<br />
| title = Understanding User's Query Intent with Wikipedia<br />
| date = 2009<br />
| authors = [[Jian Hu]]<br />[[Gang Wang]]<br />[[Frederick H. Lochovsky]]<br />[[Jian-Tao Sun]]<br />[[Zheng Chen]]<br />
| doi = 10.1145/1526709.1526773<br />
| link = http://dl.acm.org/citation.cfm?id=1526773<br />
}}<br />
'''Understanding User's Query Intent with Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Jian Hu]], [[Gang Wang]], [[Frederick H. Lochovsky]], [[Jian-Tao Sun]] and [[Zheng Chen]].<br />
<br />
== Overview ==<br />
Understanding the intent behind a user's query can help search engine to automatically route the query to some corresponding vertical search engines to obtain particularly relevant contents, thus, greatly improving user satisfaction. There are three major challenges to the query intent classification problem: (1) Intent representation; (2) Domain coverage and (3) Semantic interpretation. Current approaches to predict the user's intent mainly utilize machine learning techniques. However, it is difficult and often requires many human efforts to meet all these challenges by the statistical machine learning approaches. In this paper, authors propose a general methodology to the problem of query intent classification. With very little human effort, method can discover large quantities of intent concepts by leveraging [[Wikipedia]], one of the best human knowledge base. The Wikipedia concepts are used as the intent representation space, thus, each intent domain is represented as a set of Wikipedia articles and [[categories]]. The intent of any input query is identified through mapping the query into the Wikipedia representation space. Compared with previous approaches, proposed method can achieve much better coverage to classify queries in an intent domain even through the number of seed intent examples is very small. Moreover, the method is very general and can be easily applied to various intent domains. Authors demonstrate the effectiveness of this method in three different applications, i.e., travel, job, and person name. In each of the three cases, only a couple of seed intent queries are provided. Authors perform the quantitative evaluations in comparison with two baseline methods, and the experimental results shows that method significantly outperforms other methods in each intent domain.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Hu, Jian; Wang, Gang; Lochovsky, Frederick H.; Sun, Jian-Tao; Chen, Zheng. (2009). "[[Understanding User's Query Intent with Wikipedia]]".DOI: 10.1145/1526709.1526773. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Hu |first1=Jian |last2=Wang |first2=Gang |last3=Lochovsky |first3=Frederick H. |last4=Sun |first4=Jian-Tao |last5=Chen |first5=Zheng |title=Understanding User's Query Intent with Wikipedia |date=2009 |doi=10.1145/1526709.1526773 |url=https://wikipediaquality.com/wiki/Understanding_User's_Query_Intent_with_Wikipedia}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Hu, Jian; Wang, Gang; Lochovsky, Frederick H.; Sun, Jian-Tao; Chen, Zheng. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Understanding_User's_Query_Intent_with_Wikipedia">Understanding User's Query Intent with Wikipedia</a>&amp;quot;.DOI: 10.1145/1526709.1526773. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Maceyhttps://wikipediaquality.com/index.php?title=Models_for_Understanding_Collective_Intelligence_on_Wikipedia&diff=28268Models for Understanding Collective Intelligence on Wikipedia2021-03-14T21:27:30Z<p>Macey: Adding infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Models for Understanding Collective Intelligence on Wikipedia<br />
| date = 2016<br />
| authors = [[Randall M. Livingstone]]<br />
| doi = 10.1177/0894439315591136<br />
| link = https://dl.acm.org/citation.cfm?id=2975903<br />
}}<br />
'''Models for Understanding Collective Intelligence on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Randall M. Livingstone]].<br />
<br />
== Overview ==<br />
Through examining established and evolving conceptions of intelligence across natural and social science and applying them to [[Wikipedia]], this article argues that the world's largest encyclopedia and broadest implementation of the wiki is an online instance of collective intelligence CI, as it fits key models for this concept. Further, by relying on sociotechnical ensembles of human intelligence, programmed bots, social bureaucracy, and software protocols, a more humanistic CI, as proposed by Levy, is realized in a virtual knowledge space that embodies information as both product and process while empowering its community to explore the cultural possibilities of its collectivism.</div>Maceyhttps://wikipediaquality.com/index.php?title=Investigations_into_Trust_for_Collaborative_Information_Repositories:_a_Wikipedia_Case_Study&diff=28267Investigations into Trust for Collaborative Information Repositories: a Wikipedia Case Study2021-03-14T21:25:55Z<p>Macey: cat.</p>
<hr />
<div>{{Infobox work<br />
| title = Investigations into Trust for Collaborative Information Repositories: a Wikipedia Case Study<br />
| date = 2006<br />
| authors = [[Deborah L. McGuinness]]<br />[[Honglei Zeng]]<br />[[Paulo Pinheiro da Silva]]<br />[[Li Ding]]<br />[[Dhyanesh Narayanan]]<br />[[Mayukh Bhaowal]]<br />
| link = http://ceur-ws.org/Vol-190/paper05.pdf<br />
}}<br />
'''Investigations into Trust for Collaborative Information Repositories: a Wikipedia Case Study''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Deborah L. McGuinness]], [[Honglei Zeng]], [[Paulo Pinheiro da Silva]], [[Li Ding]], [[Dhyanesh Narayanan]] and [[Mayukh Bhaowal]].<br />
<br />
== Overview ==<br />
As collaborative repositories grow in popularity and use, issues concerning the quality and trustworthiness of information grow. Some current popular repositories contain contributions from a wide variety of users, many of which will be unknown to a potential end user. Additionally the content may change rapidly and information that was previously contributed by a known user may be updated by an unknown user. End users are now faced with more challenges as they evaluate how much they may want to rely on information that was generated and updated in this manner. A trust management layer has become an important requirement for the continued growth and acceptance of collaboratively developed and maintained information resources. In this paper, authors will describe initial investigations into designing and implementing an extensible trust management layer for collaborative and/or aggregated repositories of information. Authors leverage work on the Inference Web explanation infrastructure and exploit and expand the Proof Markup Language to handle a simple notion of trust. Authors work is designed to support representation, computation, and visualization of trust information. Authors have grounded work in the setting of [[Wikipedia]]. In this paper, authors present vision, expose motivations, relate work to date on trust representation, and present a trust computation algorithm with experimental results. Authors also discuss some issues encountered in work that authors found interesting.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
McGuinness, Deborah L.; Zeng, Honglei; Silva, Paulo Pinheiro da; Ding, Li; Narayanan, Dhyanesh; Bhaowal, Mayukh. (2006). "[[Investigations into Trust for Collaborative Information Repositories: a Wikipedia Case Study]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=McGuinness |first1=Deborah L. |last2=Zeng |first2=Honglei |last3=Silva |first3=Paulo Pinheiro da |last4=Ding |first4=Li |last5=Narayanan |first5=Dhyanesh |last6=Bhaowal |first6=Mayukh |title=Investigations into Trust for Collaborative Information Repositories: a Wikipedia Case Study |date=2006 |url=https://wikipediaquality.com/wiki/Investigations_into_Trust_for_Collaborative_Information_Repositories:_a_Wikipedia_Case_Study}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
McGuinness, Deborah L.; Zeng, Honglei; Silva, Paulo Pinheiro da; Ding, Li; Narayanan, Dhyanesh; Bhaowal, Mayukh. (2006). &amp;quot;<a href="https://wikipediaquality.com/wiki/Investigations_into_Trust_for_Collaborative_Information_Repositories:_a_Wikipedia_Case_Study">Investigations into Trust for Collaborative Information Repositories: a Wikipedia Case Study</a>&amp;quot;.<br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Macey