Predicting the Evolution of Taxonomy Restructuring in Collective Web Catalogues
Natalia Prytkova, Marc Spaniol, Gerhard Weikum · 2012
Collectively maintained Web catalogues organize links to interesting Web sites into topic hierarchies, based on community input and editorial decisions. These taxonomic systems reflect the interests and diversity of ongoing societal discourses. Catalogues evolve by adding new topics, splitting topics, and other restructuring, in order to capture newly emerging concepts of long-lasting interest. In this paper, we investigate these changes in taxonomies and develop models for predicting such structural changes. Our approach identifies newly emerging latent concepts by analyzing news articles (or social media), by means of a temporal term relatedness graph. We predict the addition of new topics to the catalogue based on statistical measures associated with the identified latent concepts. Experiments with a large news archive corpus demonstrate the high precision of our method, and its suitability for Web-scale application.