Difference between revisions of "Wikitrends: Unstructured Wikipedia-Based Text Analytics Framework"
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+ | {{Infobox work | ||
+ | | title = Wikitrends: Unstructured Wikipedia-Based Text Analytics Framework | ||
+ | | date = 2017 | ||
+ | | authors = [[Michel Naim Gerguis]]<br />[[Cherif Salama]]<br />[[M. Watheq El-Kharashi]] | ||
+ | | doi = 10.1007/978-3-319-59569-6_6 | ||
+ | | link = https://link.springer.com/chapter/10.1007%2F978-3-319-59569-6_6 | ||
+ | }} | ||
'''Wikitrends: Unstructured Wikipedia-Based Text Analytics Framework''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Michel Naim Gerguis]], [[Cherif Salama]] and [[M. Watheq El-Kharashi]]. | '''Wikitrends: Unstructured Wikipedia-Based Text Analytics Framework''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Michel Naim Gerguis]], [[Cherif Salama]] and [[M. Watheq El-Kharashi]]. | ||
== Overview == | == Overview == | ||
WikiTrends is a new analytics framework for [[Wikipedia]] articles. It adds the temporal/spatial dimensions to Wikipedia to visualize the extracted information converting the big static encyclopedia to a vibrant one by enabling the generation of aggregated views in timelines or heat maps for any user-defined collection from unstructured text. Data mining techniques were applied to detect the location, start and end year of existence, gender, and entity class for 4.85 million pages. Authors evaluated extractors over a small manually tagged random set of articles. Heat maps of notable football players’ counts over history or dominant occupations in some specific era are samples of WikiTrends maps while timelines can easily illustrate interesting fame battles over history between male and female actors, music genres, or even between American, Italian, and Indian films. Through information visualization and simple configurations, WikiTrends starts a new experience in answering questions through a figure. | WikiTrends is a new analytics framework for [[Wikipedia]] articles. It adds the temporal/spatial dimensions to Wikipedia to visualize the extracted information converting the big static encyclopedia to a vibrant one by enabling the generation of aggregated views in timelines or heat maps for any user-defined collection from unstructured text. Data mining techniques were applied to detect the location, start and end year of existence, gender, and entity class for 4.85 million pages. Authors evaluated extractors over a small manually tagged random set of articles. Heat maps of notable football players’ counts over history or dominant occupations in some specific era are samples of WikiTrends maps while timelines can easily illustrate interesting fame battles over history between male and female actors, music genres, or even between American, Italian, and Indian films. Through information visualization and simple configurations, WikiTrends starts a new experience in answering questions through a figure. |
Revision as of 09:36, 24 December 2020
Authors | Michel Naim Gerguis Cherif Salama M. Watheq El-Kharashi |
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Publication date | 2017 |
DOI | 10.1007/978-3-319-59569-6_6 |
Links | Original |
Wikitrends: Unstructured Wikipedia-Based Text Analytics Framework - scientific work related to Wikipedia quality published in 2017, written by Michel Naim Gerguis, Cherif Salama and M. Watheq El-Kharashi.
Overview
WikiTrends is a new analytics framework for Wikipedia articles. It adds the temporal/spatial dimensions to Wikipedia to visualize the extracted information converting the big static encyclopedia to a vibrant one by enabling the generation of aggregated views in timelines or heat maps for any user-defined collection from unstructured text. Data mining techniques were applied to detect the location, start and end year of existence, gender, and entity class for 4.85 million pages. Authors evaluated extractors over a small manually tagged random set of articles. Heat maps of notable football players’ counts over history or dominant occupations in some specific era are samples of WikiTrends maps while timelines can easily illustrate interesting fame battles over history between male and female actors, music genres, or even between American, Italian, and Indian films. Through information visualization and simple configurations, WikiTrends starts a new experience in answering questions through a figure.