WikiTrends: Unstructured wikipedia-based text analytics framework
Gerguis, Michel Naim; Salama, Cherif; El-Kharashi, M. Watheq;
Abstract
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. We evaluated our 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.
Other data
Title | WikiTrends: Unstructured wikipedia-based text analytics framework | Authors | Gerguis, Michel Naim; Salama, Cherif ; El-Kharashi, M. Watheq | Keywords | Data mining | Entity analytics | Entity classification | Finegrained classification | Text analytics | Text classification | Text understanding | Wikipedia | Issue Date | 1-Jan-2017 | Publisher | SPRINGER INTERNATIONAL PUBLISHING AG | Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | ISBN | 9783319595689 | ISSN | 03029743 | DOI | 10.1007/978-3-319-59569-6_6 | Scopus ID | 2-s2.0-85021723448 | Web of science ID | WOS:000434206800006 |
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